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Risk management
Risk management
from Wikipedia
Example of risk assessment: A NASA model showing areas at high risk from impact for the International Space Station

Risk management is the identification, evaluation, and prioritization of risks,[1] followed by the minimization, monitoring, and control of the impact or probability of those risks occurring.[2] Risks can come from various sources (i.e, threats) including uncertainty in international markets, political instability, dangers of project failures (at any phase in design, development, production, or sustaining of life-cycles), legal liabilities, credit risk, accidents, natural causes and disasters, deliberate attack from an adversary, or events of uncertain or unpredictable root-cause.[3] Retail traders also apply risk management by using fixed percentage position sizing and risk-to-reward frameworks to avoid large drawdowns and support consistent decision-making under pressure.

Two types of events are analyzed in risk management: risks and opportunities. Negative events can be classified as risks while positive events are classified as opportunities. Risk management standards have been developed by various institutions, including the Project Management Institute, the National Institute of Standards and Technology, actuarial societies, and International Organization for Standardization.[4][5][6] Methods, definitions and goals vary widely according to whether the risk management method is in the context of project management, security, engineering, industrial processes, financial portfolios, actuarial assessments, or public health and safety. Certain risk management standards have been criticized for having no measurable improvement on risk, whereas the confidence in estimates and decisions seems to increase.[2]

Strategies to manage threats (uncertainties with negative consequences) typically include avoiding the threat, reducing the negative effect or probability of the threat, transferring all or part of the threat to another party, and even retaining some or all of the potential or actual consequences of a particular threat. The opposite of these strategies can be used to respond to opportunities (uncertain future states with benefits).[7]

As a professional role, a risk manager[8] will "oversee the organization's comprehensive insurance and risk management program, assessing and identifying risks that could impede the reputation, safety, security, or financial success of the organization", and then develop plans to minimize and/or mitigate any negative (financial) outcomes. Risk analysts[9] support the technical side of the organization's risk management approach: once risk data has been compiled and evaluated, analysts share their findings with their managers, who use those insights to decide among possible solutions. See also Chief Risk Officer, internal audit, and Financial risk management § Corporate finance.

Introduction

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Risk is defined as the possibility that an event will occur that adversely affects the achievement of an objective.[10] Uncertainty, therefore, is a key aspect of risk.[11] Risk management appears in scientific and management literature since the 1920s.[12] It became a formal science in the 1950s, when articles and books with "risk management" in the title also appear in library searches.[13] Most of research was initially related to finance and insurance.[14][15] One popular standard clarifying vocabulary used in risk management is ISO Guide 31073:2022, "Risk management — Vocabulary".[4]

Ideally in risk management, a prioritization process is followed.[16] Whereby the risks with the greatest loss (or impact) and the greatest probability of occurring are handled first. Risks with lower probability of occurrence and lower loss are handled in descending order. In practice the process of assessing overall risk can be tricky, and organisation has to balance resources used to mitigate between risks with a higher probability but lower loss, versus a risk with higher loss but lower probability. Opportunity cost represents a unique challenge for risk managers. It can be difficult to determine when to put resources toward risk management and when to use those resources elsewhere. Again, ideal risk management optimises resource usage (spending, manpower etc), and also minimizes the negative effects of risks.

Risks vs. opportunities

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Opportunities first appear in academic research or management books in the 1990s. The first PMBoK Project Management Body of Knowledge draft of 1987 doesn't mention opportunities at all.

Modern project management school recognize the importance of opportunities. Opportunities have been included in project management literature since the 1990s, e.g. in PMBoK, and became a significant part of project risk management in the years 2000s,[17] when articles titled "opportunity management" also begin to appear in library searches. Opportunity management thus became an important part of risk management.

Modern risk management theory deals with any type of external events, positive and negative. Positive risks are called opportunities. Similarly to risks, opportunities have specific mitigation strategies: exploit, share, enhance, ignore.

In practice, risks are considered "usually negative". Risk-related research and practice focus significantly more on threats than on opportunities. This can lead to negative phenomena such as target fixation.[18]

Method

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For the most part, these methods consist of the following elements, performed, more or less, in the following order:

  1. Identify the threats.
  2. Assess the vulnerability of critical assets to specific threats.
  3. Determine the risk (i.e. the expected likelihood and consequences of specific attacks on specific assets).
  4. Identify ways to reduce those risks.
  5. Prioritize risk reduction measures.

The Risk management knowledge area, as defined by the Project Management Body of Knowledge PMBoK, consists of the following processes:

  1. Plan Risk Management – defining how to conduct risk management activities.
  2. Identify Risks – identifying individual project risks as well as sources.
  3. Perform Qualitative Risk Analysis – prioritizing individual project risks by assessing probability and impact.
  4. Perform Quantitative Risk Analysis – numerical analysis of the effects.
  5. Plan Risk Responses – developing options, selecting strategies and actions.
  6. Implement Risk Responses – implementing agreed-upon risk response plans. In the 4th Ed. of PMBoK, this process was included as an activity in the Monitor and Control process, but was later separated as a distinct process in PMBoK 6th Ed.[19]
  7. Monitor Risks – monitoring the implementation. This process was known as Monitor and Control in the previous PMBoK 4th Ed., when it also included the "Implement Risk Responses" process.

Principles

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The International Organization for Standardization (ISO) identifies the following principles for risk management:[5]

  • Create value – resources expended to mitigate risk should be less than the consequence of inaction.
  • Be an integral part of organizational processes.
  • Be part of the decision-making process.
  • Explicitly address uncertainty and assumptions.
  • Use a systematic and structured process.
  • Use the best available information.
  • Be flexible.
  • Take human factors into account.
  • Be transparent and inclusive.
  • Be dynamic, iterative and responsive to change.
  • Be capable of continual improvement and enhancement.
  • Continual reassessment.

Mild versus wild risk

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Benoit Mandelbrot distinguished between "mild" and "wild" risk and argued that risk assessment and management must be fundamentally different for the two types of risk.[20] Mild risk follows normal or near-normal probability distributions, is subject to regression to the mean and the law of large numbers, and is therefore relatively predictable. Wild risk follows fat-tailed distributions, e.g., Pareto or power-law distributions, is subject to regression to the tail (infinite mean or variance, rendering the law of large numbers invalid or ineffective), and is therefore difficult or impossible to predict. A common error in risk assessment and management is to underestimate the wildness of risk, assuming risk to be mild when in fact it is wild, which must be avoided if risk assessment and management are to be valid and reliable, according to Mandelbrot.

Process

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According to the standard ISO 31000, "Risk management – Guidelines", the process of risk management consists of several steps as follows:[5]

Establishing the context

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This involves:

  1. observing the context (the environment of the organization)
    • the social scope of risk management
    • the identity and objectives of stakeholders
    • the basis upon which risks will be evaluated, constraints.
  2. defining a framework for the activity and an agenda for identification
  3. developing an analysis of risks involved in the process
  4. mitigation or solution of risks using available technological, human and organizational resources

Identification

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After establishing the context, the next step in the process of managing risk is to identify potential risks. Risks are about events that, when triggered, cause problems or benefits. Hence, risk identification can start with the source of problems and those of competitors (benefit), or with the problem's consequences.

  • Source analysis[21] – Risk sources may be internal or external to the system that is the target of risk management (use mitigation instead of management since by its own definition risk deals with factors of decision-making that cannot be managed).

Some examples of risk sources are: stakeholders of a project, employees of a company or the weather over an airport.

  • Problem analysis[citation needed] – Risks are related to identified threats. For example: the threat of losing money, the threat of abuse of confidential information or the threat of human errors, accidents and casualties. The threats may exist with various entities, most important with shareholders, customers and legislative bodies such as the government.

When either source or problem is known, the events that a source may trigger or the events that can lead to a problem can be investigated. For example: stakeholders withdrawing during a project may endanger funding of the project; confidential information may be stolen by employees even within a closed network; lightning striking an aircraft during takeoff may make all people on board immediate casualties.

The chosen method of identifying risks may depend on culture, industry practice and compliance. The identification methods are formed by templates or the development of templates for identifying source, problem or event. Common risk identification methods are:

  • Objectives-based risk identification [citation needed] – Organizations and project teams have objectives. Any event that may prevent an objective from being achieved is identified as risk.
  • Scenario-based risk identification – In scenario analysis different scenarios are created. The scenarios may be the alternative ways to achieve an objective, or an analysis of the interaction of forces in, for example, a market or battle. Any event that triggers an undesired scenario alternative is identified as risk – see Futures Studies for methodology used by Futurists.
  • Taxonomy-based risk identification – The taxonomy in taxonomy-based risk identification is a breakdown of possible risk sources. Based on the taxonomy and knowledge of best practices, a questionnaire is compiled. The answers to the questions reveal risks.[22]
  • Common-risk checking[23] – In several industries, lists with known risks are available. Each risk in the list can be checked for application to a particular situation.[24]
  • Risk charting[25] – This method combines the above approaches by listing resources at risk, threats to those resources, modifying factors which may increase or decrease the risk and consequences it is wished to avoid. Creating a matrix under these headings enables a variety of approaches. One can begin with resources and consider the threats they are exposed to and the consequences of each. Alternatively one can start with the threats and examine which resources they would affect, or one can begin with the consequences and determine which combination of threats and resources would be involved to bring them about.

Assessment

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Once risks have been identified, they must then be assessed as to their potential severity of impact (generally a negative impact, such as damage or loss) and to the probability of occurrence. [26] These quantities can be either simple to measure, in the case of the value of a lost building, or impossible to know for sure in the case of an unlikely event, the probability of occurrence of which is unknown. Therefore, in the assessment process it is critical to make the best educated decisions in order to properly prioritize the implementation of the risk management plan.

Even a short-term positive improvement can have long-term negative impacts. Take the "turnpike" example. A highway is widened to allow more traffic. More traffic capacity leads to greater development in the areas surrounding the improved traffic capacity. Over time, traffic thereby increases to fill available capacity. Turnpikes thereby need to be expanded in a seemingly endless cycles. There are many other engineering examples where expanded capacity (to do any function) is soon filled by increased demand. Since expansion comes at a cost, the resulting growth could become unsustainable without forecasting and management.

The fundamental difficulty in risk assessment is determining the rate of occurrence since statistical information is not available on all kinds of past incidents and is particularly scanty in the case of catastrophic events, simply because of their infrequency. Furthermore, evaluating the severity of the consequences (impact) is often quite difficult for intangible assets. Asset valuation is another question that needs to be addressed. Thus, best educated opinions and available statistics are the primary sources of information. Nevertheless, risk assessment should produce such information for senior executives of the organization that the primary risks are easy to understand and that the risk management decisions may be prioritized within overall company goals. Thus, there have been several theories and attempts to quantify risks. Numerous different risk formulae exist, but perhaps the most widely accepted formula for risk quantification is: "Rate (or probability) of occurrence multiplied by the impact of the event equals risk magnitude."[vague]

Risk options

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Risk mitigation measures are usually formulated according to one or more of the following major risk options, which are:

  1. Design a new business process with adequate built-in risk control and containment measures from the start.
  2. Periodically re-assess risks that are accepted in ongoing processes as a normal feature of business operations and modify mitigation measures.
  3. Transfer risks to an external agency (e.g. an insurance company)
  4. Avoid risks altogether (e.g. by closing down a particular high-risk business area)

Later research has shown that the financial benefits of risk management are less dependent on the formula used but are more dependent on the frequency and how risk assessment is performed.

In business it is imperative to be able to present the findings of risk assessments in financial, market, or schedule terms. Robert Courtney Jr. (IBM, 1970) proposed a formula for presenting risks in financial terms. The Courtney formula was accepted as the official risk analysis method for the US governmental agencies. The formula proposes calculation of ALE (annualized loss expectancy) and compares the expected loss value to the security control implementation costs (cost–benefit analysis).

Potential risk treatments

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Planning for risk management uses four essential techniques. Under the acceptance technique, the business intentionally assumes risks without financial protections in the hopes that possible gains will exceed prospective losses. The transfer approach shields the business from losses by shifting risks to a third party, frequently in exchange for a fee, while the third-party benefits from the project. By choosing not to participate in high-risk ventures, the avoidance strategy avoids losses but also loses out on possibilities. Last but not least, the reduction approach lowers risks by implementing strategies like insurance, which provides protection for a variety of asset classes and guarantees reimbursement in the event of losses.[27]

Once risks have been identified and assessed, all techniques to manage the risk fall into one or more of these four major categories:[28]

  • Avoidance (eliminate, withdraw from or not become involved)
  • Reduction (optimize – mitigate)
  • Sharing (transfer – outsource or insure)
  • Retention (accept and budget)

Ideal use of these risk control strategies may not be possible. Some of them may involve trade-offs that are not acceptable to the organization or person making the risk management decisions. Another source, from the US Department of Defense (see link), Defense Acquisition University, calls these categories ACAT, for Avoid, Control, Accept, or Transfer. This use of the ACAT acronym is reminiscent of another ACAT (for Acquisition Category) used in US Defense industry procurements, in which Risk Management figures prominently in decision making and planning.

Similarly to risks, opportunities have specific mitigation strategies: exploit, share, enhance, ignore.

Risk avoidance

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This includes not performing an activity that could present risk. Refusing to purchase a property or business to avoid legal liability is one such example. Avoiding airplane flights for fear of hijacking. Avoidance may seem like the answer to all risks, but avoiding risks also means losing out on the potential gain that accepting (retaining) the risk may have allowed. Not entering a business to avoid the risk of loss also avoids the possibility of earning profits. Increasing risk regulation in hospitals has led to avoidance of treating higher risk conditions, in favor of patients presenting with lower risk.[29]

Risk reduction

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Risk reduction or "optimization" involves reducing the severity of the loss or the likelihood of the loss from occurring. For example, sprinklers are designed to put out a fire to reduce the risk of loss by fire. This method may cause a greater loss by water damage and therefore may not be suitable. Halon fire suppression systems may mitigate that risk, but the cost may be prohibitive as a strategy.

Acknowledging that risks can be positive or negative, optimizing risks means finding a balance between negative risk and the benefit of the operation or activity; and between risk reduction and effort applied. By effectively applying Health, Safety and Environment (HSE) management standards, organizations can achieve tolerable levels of residual risk.[30]

Modern software development methodologies reduce risk by developing and delivering software incrementally. Early methodologies suffered from the fact that they only delivered software in the final phase of development; any problems encountered in earlier phases meant costly rework and often jeopardized the whole project. By developing in iterations, software projects can limit effort wasted to a single iteration.

Outsourcing could be an example of risk sharing strategy if the outsourcer can demonstrate higher capability at managing or reducing risks.[31] For example, a company may outsource only its software development, the manufacturing of hard goods, or customer support needs to another company, while handling the business management itself. This way, the company can concentrate more on business development without having to worry as much about the manufacturing process, managing the development team, or finding a physical location for a center. Also, implanting controls can also be an option in reducing risk. Controls that either detect causes of unwanted events prior to the consequences occurring during use of the product, or detection of the root causes of unwanted failures that the team can then avoid. Controls may focus on management or decision-making processes. All these may help to make better decisions concerning risk.[32]

Risk sharing

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Briefly defined as "sharing with another party the burden of loss or the benefit of gain, from a risk, and the measures to reduce a risk."

The term 'risk transfer' is often used in place of risk-sharing in the mistaken belief that you can transfer a risk to a third party through insurance or outsourcing. In practice, if the insurance company or contractor go bankrupt or end up in court, the original risk is likely to still revert to the first party. As such, in the terminology of practitioners and scholars alike, the purchase of an insurance contract is often described as a "transfer of risk." However, technically speaking, the buyer of the contract generally retains legal responsibility for the losses "transferred", meaning that insurance may be described more accurately as a post-event compensatory mechanism. For example, a personal injuries insurance policy does not transfer the risk of a car accident to the insurance company. The risk still lies with the policyholder namely the person who has been in the accident. The insurance policy simply provides that if an accident (the event) occurs involving the policyholder then some compensation may be payable to the policyholder that is commensurate with the suffering/damage.

Methods of managing risk fall into multiple categories. Risk-retention pools are technically retaining the risk for the group, but spreading it over the whole group involves transfer among individual members of the group. This is different from traditional insurance, in that no premium is exchanged between members of the group upfront, but instead, losses are assessed to all members of the group.

Risk retention

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Risk retention involves accepting the loss, or benefit of gain, from a risk when the incident occurs. True self-insurance falls in this category. Risk retention is a viable strategy for small risks where the cost of insuring against the risk would be greater over time than the total losses sustained. All risks that are not avoided or transferred are retained by default. This includes risks that are so large or catastrophic that either they cannot be insured against or the premiums would be infeasible. War is an example since most property and risks are not insured against war, so the loss attributed to war is retained by the insured. Also any amounts of potential loss (risk) over the amount insured is retained risk. This may also be acceptable if the chance of a very large loss is small or if the cost to insure for greater coverage amounts is so great that it would hinder the goals of the organization too much.

Risk management plan

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Select appropriate controls or countermeasures to mitigate each risk. Risk mitigation needs to be approved by the appropriate level of management. For instance, a risk concerning the image of the organization should have top management decision behind it whereas IT management would have the authority to decide on computer virus risks.

The risk management plan should propose applicable and effective security controls for managing the risks. For example, an observed high risk of computer viruses could be mitigated by acquiring and implementing antivirus software. A good risk management plan should contain a schedule for control implementation and responsible persons for those actions. There are four basic steps of risk management plan, which are threat assessment, vulnerability assessment, impact assessment and risk mitigation strategy development.[33]

According to ISO/IEC 27001, the stage immediately after completion of the risk assessment phase consists of preparing a Risk Treatment Plan, which should document the decisions about how each of the identified risks should be handled. Mitigation of risks often means selection of security controls, which should be documented in a Statement of Applicability, which identifies which particular control objectives and controls from the standard have been selected, and why.

Implementation

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Implementation follows all of the planned methods for mitigating the effect of the risks. Purchase insurance policies for the risks that it has been decided to transferred to an insurer, avoid all risks that can be avoided without sacrificing the entity's goals, reduce others, and retain the rest.

Review and evaluation of the plan

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Initial risk management plans will never be perfect. Practice, experience, and actual loss results will necessitate changes in the plan and contribute information to allow possible different decisions to be made in dealing with the risks being faced.

Risk analysis results and management plans should be updated periodically. There are two primary reasons for this:

  1. to evaluate whether the previously selected security controls are still applicable and effective
  2. to evaluate the possible risk level changes in the business environment. For example, information risks are a good example of rapidly changing business environment.

Areas

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Enterprise

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Enterprise risk management (ERM) defines risk as those possible events or circumstances that can have negative influences on the enterprise in question, where the impact can be on the very existence, the resources (human and capital), the products and services, or the customers of the enterprise, as well as external impacts on society, markets, or the environment. There are various defined frameworks here, where every probable risk can have a pre-formulated plan to deal with its possible consequences (to ensure contingency if the risk becomes a liability). Managers thus analyze and monitor both the internal and external environment facing the enterprise, addressing business risk generally, and any impact on the enterprise achieving its strategic goals. ERM thus overlaps various other disciplines - operational risk management, financial risk management etc. - but is differentiated by its strategic and long-term focus.[34] ERM systems usually focus on safeguarding reputation, acknowledging its significant role in comprehensive risk management strategies.[35]

Finance

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Risk in Banking

As applied to finance, risk management concerns the techniques and practices for measuring, monitoring and controlling the market- and credit risk (and operational risk) on a firm's balance sheet, due to a bank's credit and trading exposure, or re a fund manager's portfolio value; for an overview see Finance § Risk management.

Contractual risk management

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The concept of "contractual risk management" emphasises the use of risk management techniques in contract deployment, i.e. managing the risks which are accepted through entry into a contract. Norwegian academic Petri Keskitalo defines "contractual risk management" as "a practical, proactive and systematical contracting method that uses contract planning and governance to manage risks connected to business activities".[36] In an article by Samuel Greengard published in 2010, two US legal cases are mentioned which emphasise the importance of having a strategy for dealing with risk:[37]

  • UDC v. CH2M Hill, which deals with the risk to a professional advisor who signs an indemnification provision including acceptance of a duty to defend, who may thereby pick up the legal costs of defending a client subject to a claim from a third party,[38]
  • Witt v. La Gorce Country Club, which deals with the effectiveness of a limitation of liability clause, which may, in certain jurisdictions, be found to be ineffective.[39]

Greengard recommends using industry-standard contract language as much as possible to reduce risk as much as possible and rely on clauses which have been in use and subject to established court interpretation over a number of years.[37]

Customs

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Customs risk management is concerned with the risks which arise within the context of international trade and have a bearing on safety and security, including the risk that illicit drugs and counterfeit goods can pass across borders and the risk that shipments and their contents are incorrectly declared.[40] The European Union has adopted a Customs Risk Management Framework (CRMF) applicable across the union and throughout its member states, whose aims include establishing a common level of customs control protection and a balance between the objectives of safe customs control and the facilitation of legitimate trade.[41] Two events which prompted the European Commission to review customs risk management policy in 2012-13 were the September 11 attacks of 2001 and the 2010 transatlantic aircraft bomb plot involving packages being sent from Yemen to the United States, referred to by the Commission as "the October 2010 (Yemen) incident".[42]

Memory institutions (museums, libraries and archives)

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Enterprise security

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ESRM is a security program management approach that links security activities to an enterprise's mission and business goals through risk management methods. The security leader's role in ESRM is to manage risks of harm to enterprise assets in partnership with the business leaders whose assets are exposed to those risks. ESRM involves educating business leaders on the realistic impacts of identified risks, presenting potential strategies to mitigate those impacts, then enacting the option chosen by the business in line with accepted levels of business risk tolerance[43]

Medical devices

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For medical devices, risk management is a process for identifying, evaluating and mitigating risks associated with harm to people and damage to property or the environment[44]. Risk management is an integral part of medical device design and development, production processes and evaluation of field experience, and is applicable to all types of medical devices. The evidence of its application is required by most regulatory bodies such as the US FDA. The management of risks for medical devices is described by the International Organization for Standardization (ISO) in ISO 14971:2019, Medical Devices—The application of risk management to medical devices, a product safety standard. The standard provides a process framework and associated requirements for management responsibilities, risk analysis and evaluation, risk controls and lifecycle risk management. Guidance on the application of the standard is available via ISO/TR 24971:2020.

The European version of the risk management standard was updated in 2009 and again in 2012 to refer to the Medical Devices Directive (MDD) and Active Implantable Medical Device Directive (AIMDD) revision in 2007, as well as the In Vitro Medical Device Directive (IVDD). The requirements of EN 14971:2012 are nearly identical to ISO 14971:2007. The differences include three "(informative)" Z Annexes that refer to the new MDD, AIMDD, and IVDD. These annexes indicate content deviations that include the requirement for risks to be reduced as far as possible, and the requirement that risks be mitigated by design and not by labeling on the medical device (i.e., labeling can no longer be used to mitigate risk).

Typical risk analysis and evaluation techniques adopted by the medical device industry include hazard analysis, fault tree analysis (FTA), failure mode and effects analysis (FMEA), hazard and operability study (HAZOP), and risk traceability analysis for ensuring risk controls are implemented and effective (i.e. tracking risks identified to product requirements, design specifications, verification and validation results etc.). FTA analysis requires diagramming software. FMEA analysis can be done using a spreadsheet program. There are also integrated medical device risk management solutions.

Through a draft guidance, the FDA has introduced another method named "Safety Assurance Case" for medical device safety assurance analysis. The safety assurance case is structured argument reasoning about systems appropriate for scientists and engineers, supported by a body of evidence, that provides a compelling, comprehensible and valid case that a system is safe for a given application in a given environment. With the guidance, a safety assurance case is expected for safety critical devices (e.g. infusion devices) as part of the pre-market clearance submission, e.g. 510(k). In 2013, the FDA introduced another draft guidance expecting medical device manufacturers to submit cybersecurity risk analysis information.

Project management

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Project risk management must be considered at the different phases of acquisition. At the beginning of a project, the advancement of technical developments, or threats presented by a competitor's projects, may cause a risk or threat assessment and subsequent evaluation of alternatives (see Analysis of Alternatives). Once a decision is made, and the project begun, more familiar project management applications can be used:[45][46][47]

  • Planning how risk will be managed in the particular project. Plans should include risk management tasks, responsibilities, activities and budget.
  • Assigning a risk officer – a team member other than a project manager who is responsible for foreseeing potential project problems. Typical characteristic of risk officer is a healthy skepticism.
  • Maintaining live project risk database. Each risk should have the following attributes: opening date, title, short description, probability and importance. Optionally a risk may have an assigned person responsible for its resolution and a date by which the risk must be resolved.
  • Creating anonymous risk reporting channel. Each team member should have the possibility to report risks that he/she foresees in the project.
  • Preparing mitigation plans for risks that are chosen to be mitigated. The purpose of the mitigation plan is to describe how this particular risk will be handled – what, when, by whom and how will it be done to avoid it or minimize consequences if it becomes a liability.
  • Summarizing planned and faced risks, effectiveness of mitigation activities, and effort spent for the risk management.

Megaprojects (infrastructure)

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Megaprojects (sometimes also called "major programs") are large-scale investment projects, typically costing more than $1 billion per project. Megaprojects include major bridges, tunnels, highways, railways, airports, seaports, power plants, dams, wastewater projects, coastal flood protection schemes, oil and natural gas extraction projects, public buildings, information technology systems, aerospace projects, and defense systems. Megaprojects have been shown to be particularly risky in terms of finance, safety, and social and environmental impacts. Risk management is therefore particularly pertinent for megaprojects and special methods and special education have been developed for such risk management.[48]

Natural disasters

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It is important to assess risk in regard to natural disasters like floods, earthquakes, and so on. Outcomes of natural disaster risk assessment are valuable when considering future repair costs, business interruption losses and other downtime, effects on the environment, insurance costs, and the proposed costs of reducing the risk.[49][50] The Sendai Framework for Disaster Risk Reduction is a 2015 international accord that has set goals and targets for disaster risk reduction in response to natural disasters.[51] There are regular International Disaster and Risk Conferences in Davos to deal with integral risk management.

Several tools can be used to assess risk and risk management of natural disasters and other climate events, including geospatial modeling, a key component of land change science. This modeling requires an understanding of geographic distributions of people as well as an ability to calculate the likelihood of a natural disaster occurring.

Wilderness

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The management of risks to persons and property in wilderness and remote natural areas has developed with increases in outdoor recreation participation and decreased social tolerance for loss. Organizations providing commercial wilderness experiences can now align with national and international consensus standards for training and equipment such as ANSI/NASBLA 101-2017 (boating),[52] UIAA 152 (ice climbing tools),[53] and European Norm 13089:2015 + A1:2015 (mountaineering equipment).[54][55] The Association for Experiential Education offers accreditation for wilderness adventure programs.[56] The Wilderness Risk Management Conference provides access to best practices, and specialist organizations provide wilderness risk management consulting and training.[57]

The text Outdoor Safety – Risk Management for Outdoor Leaders,[58] published by the New Zealand Mountain Safety Council, provides a view of wilderness risk management from the New Zealand perspective, recognizing the value of national outdoor safety legislation and devoting considerable attention to the roles of judgment and decision-making processes in wilderness risk management.

One popular models for risk assessment is the Risk Assessment and Safety Management (RASM) Model developed by Rick Curtis, author of The Backpacker's Field Manual.[59] The formula for the RASM Model is: Risk = Probability of Accident × Severity of Consequences. The RASM Model weighs negative risk—the potential for loss, against positive risk—the potential for growth.

Information technology

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IT risk is a risk related to information technology. This is a relatively new term due to an increasing awareness that information security is simply one facet of a multitude of risks that are relevant to IT and the real world processes it supports. "Cybersecurity is tied closely to the advancement of technology. It lags only long enough for incentives like black markets to evolve and new exploits to be discovered. There is no end in sight for the advancement of technology, so we can expect the same from cybersecurity."[60]

ISACA's Risk IT framework ties IT risk to enterprise risk management. Duty of Care Risk Analysis (DoCRA) evaluates risks and their safeguards and considers the interests of all parties potentially affected by those risks.[61] The Verizon Data Breach Investigations Report (DBIR) features how organizations can leverage the Veris Community Database (VCDB) to estimate risk. Using HALOCK methodology within CIS RAM and data from VCDB, professionals can determine threat likelihood for their industries.

IT risk management includes "incident handling", an action plan for dealing with intrusions, cyber-theft, denial of service, fire, floods, and other security-related events. According to the SANS Institute, it is a six step process: Preparation, Identification, Containment, Eradication, Recovery, and Lessons Learned.[62]

Operations

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Operational risk management (ORM) is the oversight of operational risk, including the risk of loss resulting from: inadequate or failed internal processes and systems; human factors; or external events. Given the nature of operations, ORM is typically a "continual" process, and will include ongoing risk assessment, risk decision making, and the implementation of risk controls.

Petroleum and natural gas

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For the offshore oil and gas industry, operational risk management is regulated by the safety case regime in many countries. Hazard identification and risk assessment tools and techniques are described in the international standard ISO 17776:2000, and organisations such as the IADC (International Association of Drilling Contractors) publish guidelines for Health, Safety and Environment (HSE) Case development which are based on the ISO standard. Further, diagrammatic representations of hazardous events are often expected by governmental regulators as part of risk management in safety case submissions; these are known as bow-tie diagrams (see Network theory in risk assessment). The technique is also used by organisations and regulators in mining, aviation, health, defence, industrial and finance.

Pharmaceutical sector

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The principles and tools for quality risk management are increasingly being applied to different aspects of pharmaceutical quality systems. These aspects include development, manufacturing, distribution, inspection, and submission/review processes throughout the lifecycle of drug substances, drug products, biological and biotechnological products (including the use of raw materials, solvents, excipients, packaging and labeling materials in drug products, biological and biotechnological products). Risk management is also applied to the assessment of microbiological contamination in relation to pharmaceutical products and cleanroom manufacturing environments.[63]

Supply chain

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Supply chain risk management (SCRM) aims at maintaining supply chain continuity in the event of scenarios or incidents which could interrupt normal business and hence profitability. Risks to the supply chain range from everyday to exceptional, including unpredictable natural events (such as tsunamis and pandemics) to counterfeit products, and reach across quality, security, to resiliency and product integrity. Mitigation of these risks can involve various elements of the business including logistics and cybersecurity, as well as the areas of finance and operations.

Travel

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Travel risk management is concerned with how organisations assess the risks to their staff when travelling, especially when travelling overseas. In the field of international standards, ISO 31030:2021 addresses good practice in travel risk management.[64]

The Global Business Travel Association's education and research arm, the GBTA Foundation. found in 2015 that most businesses covered by their research employed travel risk management protocols aimed at ensuring the safety and well-being of their business travelers.[65] Six key principles of travel risk awareness put forward by the association are preparation, awareness of surroundings and people, keeping a low profile, adopting an unpredictable routine, communications and layers of protection.[66] Traveler tracking using mobile tracking and messaging technologies had by 2015 become a widely used aspect of travel risk management.[65]

Risk communication

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Risk communication is a complex cross-disciplinary academic field that is part of risk management and related to fields like crisis communication. The goal is to make sure that targeted audiences understand how risks affect them or their communities by appealing to their values.[67][68]

Risk communication is particularly important in disaster preparedness,[69] public health,[70] and preparation for major global catastrophic risk.[69] For example, the impacts of climate change and climate risk effect every part of society, so communicating that risk is an important climate communication practice, in order for societies to plan for climate adaptation.[71] Similarly, in pandemic prevention, understanding of risk helps communities stop the spread of disease and improve responses.[72]

Risk communication deals with possible risks and aims to raise awareness of those risks to encourage or persuade changes in behavior to relieve threats in the long term. On the other hand, crisis communication is aimed at raising awareness of a specific type of threat, the magnitude, outcomes, and specific behaviors to adopt to reduce the threat.[73]

Risk communication in food safety is part of the risk analysis framework. Together with risk assessment and risk management, risk communication aims to reduce foodborne illnesses. Food safety risk communication is an obligatory activity for food safety authorities[74] in countries, which adopted the Agreement on the Application of Sanitary and Phytosanitary Measures.

Risk communication also exists on a smaller scale. For instance, the risks associated with personal medical decisions have to be communicated to that individual along with their family.[75]

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Risk management is the coordinated activities to direct and control an organization with regard to risk, encompassing the identification, , , treatment, monitoring, and communication of risks that could affect the achievement of objectives. It applies first-principles reasoning to uncertainties inherent in , prioritizing empirical assessment of potential adverse effects over speculative narratives. The practice emerged systematically after , evolving from insurance-focused techniques to broader enterprise-wide frameworks that integrate of threats across operations, , and . Central to effective risk management are principles such as integration into organizational processes, structured and comprehensive approaches tailored to context, inclusivity of stakeholder input, dynamic adaptation to changes, reliance on the best available , and continual improvement through . The core process is iterative: risks are identified through systematic scanning of internal and external factors, analyzed for likelihood and impact using quantitative models where feasible, evaluated against tolerance thresholds, treated via avoidance, , transfer, or , and monitored with ongoing communication to ensure alignment with goals. This framework, as outlined in standards like , emphasizes human and cultural influences on risk perception, countering biases that can distort assessments in institutional settings. In business and finance, risk management mitigates exposures such as market volatility, credit defaults, operational disruptions, and liquidity shortfalls, enabling informed resource allocation and resilience against shocks. Notable applications include hedging derivatives in trading portfolios and stress-testing balance sheets to quantify tail risks, with failures—such as overlooked correlations in the 2008 crisis or siloed oversight in scandals like —highlighting consequences of inadequate causal modeling and transparency. Despite advancements, persistent challenges arise from overreliance on historical data ignoring non-linear dynamics or institutional incentives favoring short-term gains, underscoring the need for robust, evidence-based over compliant formalities.

Fundamentals

Definition and Core Concepts

Risk management refers to the coordinated activities to direct and control an organization with regard to risk, encompassing the identification, analysis, evaluation, treatment, monitoring, and review of risks to achieve objectives while considering uncertainty's effects. This process is iterative and integrates into organizational governance, strategy, and operations, accounting for external and internal contexts such as human behavior and cultural influences. The ISO 31000 standard, published in 2018 by the International Organization for Standardization, provides voluntary guidelines rather than certifiable requirements, emphasizing its application across sectors to enhance decision-making and resilience. At its core, is defined as the "effect of on objectives," where effects can be positive or negative deviations from expected outcomes, distinguishing it from mere by linking it directly to goal attainment. Key concepts include , which combines qualitative and quantitative analysis to determine risk likelihood and consequences, and risk treatment, involving options like avoidance, mitigation, transfer, or acceptance to align with organizational risk criteria. denotes the types and amount of an is willing to pursue, while risk tolerance specifies acceptable variation levels around objectives; these guide prioritization and resource allocation. Monitoring and review ensure ongoing effectiveness, adapting to changes in context or emerging risks, with communication fostering stakeholder understanding and continual improvement. —the level persisting after treatment—represents a foundational , as complete elimination is often impractical, requiring balanced trade-offs between potential losses and control costs based on and impact . These elements underscore risk management's role in causal realism, prioritizing verifiable threats over speculative ones through structured, evidence-based approaches rather than intuitive judgments.

Principles and Standards

Risk management principles establish foundational guidelines for organizations to systematically address uncertainties that could affect objectives. The International Organization for Standardization's :2018 delineates eight core principles, emphasizing integration into organizational processes to enhance decision-making and value creation. These principles derive from empirical observations of successful risk practices across industries, prioritizing causal linkages between risk handling and outcomes over responses. The , integration, requires embedding risk management into all activities, from to operations, rather than treating it as a siloed function; this approach has been shown to reduce unforeseen disruptions by aligning risks with business drivers. Second, risk management must be structured and comprehensive, applying a consistent that covers identification, , treatment, and monitoring across the to avoid fragmented efforts. Customization, the third principle, tailors the framework to the organization's context, size, and risk profile, acknowledging that uniform applications fail in diverse settings like multinational firms versus small enterprises. Inclusivity, the fourth principle, involves stakeholders at all levels to leverage diverse insights and foster ownership, mitigating blind spots from top-down impositions. Dynamism, the fifth, demands adaptability to evolving internal and external factors, such as technological shifts or regulatory changes, with evidence from post-2008 financial analyses indicating static frameworks amplify vulnerabilities. The sixth principle relies on the best available , integrating quantitative , qualitative judgments, and external while transparently addressing uncertainties and biases in sources. Human and cultural factors form the seventh principle, recognizing that behavioral influences and drive risk perceptions and responses; studies in underscore how cognitive biases, like overconfidence, undermine without cultural alignment. Finally, continual , the eighth principle, mandates iterative refinement through reviews and feedback loops, drawing from precedents where iterative processes yield measurable reductions in incident rates. Standards formalize these principles into actionable frameworks. provides generic guidelines applicable beyond specific sectors, updated in 2018 to emphasize leadership commitment and iterative processes based on global practitioner input. The Committee of Sponsoring Organizations of the Treadway Commission (COSO) (ERM) framework, revised in 2017, integrates risk with strategy and performance through five components—governance and culture, strategy and objective-setting, performance, review and revision, and information, communication, and reporting—primarily for financial and operational contexts. The National Institute of Standards and Technology (NIST) (RMF), outlined in SP 800-37 Revision 2 (2018), offers a seven-step process tailored for information systems but extensible to broader risks, focusing on preparation, categorization, control selection, implementation, assessment, authorization, and monitoring to ensure repeatable, evidence-based security outcomes. These standards, while not legally binding universally, have influenced regulations like the Sarbanes-Oxley Act for COSO and FISMA for NIST, with adoption correlating to lower audit findings in empirical compliance studies. Organizations select frameworks based on scope, with favored for its flexibility across non-financial risks.

Distinguishing Risks from Opportunities

In risk management, risks are uncertainties that could adversely affect objectives, potentially leading to losses, disruptions, or failure to achieve goals, whereas opportunities are uncertainties that could favorably impact objectives, enabling gains, improvements, or enhanced value creation. This distinction arises from the directional nature of outcomes under : risks represent downside variability, such as financial shortfalls or operational failures, while opportunities embody upside potential, like market expansions or technological advancements. Effective requires recognizing that the same uncertain factors—such as economic shifts or regulatory changes—can manifest as either, depending on context and response. The (PMBOK) Guide, published by the , defines as "an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives," thereby including both threats (negative effects requiring or avoidance) and opportunities (positive effects warranting exploitation or enhancement). In contrast, traditional (ERM) frameworks often treat risks as threats focused on loss prevention, separating opportunities for integration into strategic to avoid conflating protective measures with growth-oriented actions. For example, a disruption poses a risk of cost overruns but an opportunity for supplier diversification if proactively pursued. ISO 31000:2018, the international standard for management, adopts a broader view by defining as "the effect of on objectives," where effects can be positive (opportunities), negative (), or neutral, emphasizing that organizations should address both to optimize outcomes rather than solely minimizing threats. This inclusive approach counters earlier siloed practices, where downside focus—prevalent in financial sectors post-2008 crisis—led to overlooked upsides, as evidenced by studies showing firms with integrated opportunity management achieving 20-30% higher returns on strategic initiatives. However, distinguishing remains essential for : demand controls to alter probabilities or impacts downward, while opportunities require actions to increase likelihoods or amplify benefits, ensuring resources are not misallocated in pursuit of neutrality over directed .

Mild versus Wild Risks

In risk management, risks are distinguished as mild or based on the underlying probability distributions governing their occurrences and magnitudes. Mild risks conform to Gaussian or near-normal distributions, characterized by thin tails where extreme deviations from the mean are exceedingly rare and their impacts are limited by the . Wild risks, conversely, arise from fat-tailed distributions such as power laws or Pareto, where extreme events—though infrequent—exhibit disproportionately large magnitudes, rendering standard statistical tools inadequate for prediction or mitigation. This dichotomy, formalized by and elaborated with , highlights how mild randomness suits aggregation and averaging, as in human heights or measurement errors, where outliers do not dominate outcomes. In contrast, wild randomness prevails in domains like financial markets, , or wealth accumulation, where a single event can overwhelm the system, as evidenced by the 1987 stock market crash, which exceeded Gaussian predictions by over 20 standard deviations. Such distributions imply infinite or undefined variance in theoretical models, underscoring the fragility of assuming normality in . Risk management practices often falter with wild risks because tools like (VaR), calibrated on historical Gaussian-like data, systematically underestimate tail events; for instance, Long-Term Capital Management's collapse ignored fat-tail dependencies, leading to a $4.6 billion loss despite sophisticated hedging. Effective strategies for wild risks thus emphasize robustness over precise forecasting, such as diversification via the "" approach—combining safe assets with high-upside speculations while avoiding middling exposures—or stress-testing against extreme scenarios rather than probabilistic averages. Empirical evidence from power-law phenomena, including earthquake magnitudes following the Gutenberg-Richter law (with exponents around 1.5-2.5), reinforces that wild risks demand causal focus on vulnerabilities rather than reliance on ergodic assumptions inherent in mild models.

Historical Development

Ancient and Early Modern Origins

The , promulgated around 1750 BCE in ancient , contained early mechanisms for risk distribution in , such as provisions that absolved merchants of repayment if shipments were lost to perils like storms, robbery, or enemy action, effectively sharing losses between lenders and borrowers. These clauses, including Laws 100–104 on carrier liability and innkeeper responsibilities, required partial compensation for damaged or stolen , fostering mutual in caravan and river transport to mitigate uncertainties in . Such arrangements represented primitive risk pooling, prioritizing verifiable causation over fault to sustain economic exchange amid environmental and human threats. In and , bottomry loans advanced this practice by tying repayment to voyage success: lenders financed ships and with high interest rates—often 20–30%—but forfeited repayment if the vessel was lost to sea perils, transferring maritime risks to investors who assessed routes and seasons probabilistically. Originating with Phoenician traders and formalized under by the 1st century BCE, these contracts, akin to modern hull insurance, enabled long-distance trade expansion by aligning capital provision with hazard exposure, as evidenced in legal texts like the Digest of Justinian. Unlike punitive Babylonian codes, Roman variants emphasized contractual contingency, reducing trade paralysis from fear of . By the , Italian maritime republics like and evolved these into standalone policies, decoupling coverage from loans and standardizing premiums based on voyage-specific hazards, with the earliest documented contract issued in in 1347. Notarial records from and show insurers pooling risks across multiple underwriters, often at 5–15% rates calibrated to distance and threats, enabling sustained Mediterranean trade volumes that grew 2–3 times over the century. This shift to probabilistic pricing, informed by empirical loss data rather than judgments, marked a transition toward transfer, influencing early modern commerce despite regulatory curbs like Venice's 1435 premium caps to curb .

Industrial and Post-War Evolution

The , beginning in Britain around 1760 and spreading to and by the early , generated novel hazards from mechanized factories, steam engines, and , necessitating rudimentary risk controls focused on property damage and worker injuries. Early responses included the UK's 1802 Health and Morals of Apprentices Act, which regulated ventilation and hours in cotton mills to curb labor risks, though enforcement was limited. By the 1830s, factory inspectors were appointed under the 1833 Factory Act to mitigate machinery-related accidents, reflecting causal links between unguarded equipment and high injury rates—such as the frequent limb amputations documented in textile operations. Concurrently, markets adapted; fire expanded post-1666 precedents, with mutual societies like the Hand-in-Hand forming in 1696 to pool industrial property risks, while boiler explosion data from the 1820s onward spurred engineering inspections by groups like the UK's Boiler Makers Society in 1834. These measures prioritized loss prevention over comprehensive analysis, driven by empirical accident tallies rather than probabilistic models. In the late 19th and early 20th centuries, industrial risks intensified with railroads and chemicals, prompting statutory and safety bureaucracies. The UK's 1897 Workmen's Compensation Act mandated employer liability for occupational injuries, shifting from fault-based torts to no-fault systems based on aggregated claims data showing annual fatalities exceeding 1,000 in alone by . In the , railroad accident rates peaked at 25 deaths per million train-miles in the 1880s, catalyzing state-level safety commissions and the 1907 Monongah mine disaster (362 deaths), which led to federal Bureau of Mines creation for hazard inspections. Firms like implemented internal from , using incident logs to redesign processes, prefiguring systematic risk assessment amid electrification hazards like burns. These developments emphasized reactive over proactive quantification, as data scarcity limited foresight, though actuaries began applying early statistical methods to premium setting. Post-World War II, risk management coalesced as a distinct profession, leveraging wartime for business applications amid economic expansion and technological perils like nuclear energy. The term "risk management" gained currency in the late 1940s for holistic insurance procurement and loss control, diverging from pure actuarial transfer. In 1950, the Risk and Insurance Management Society (RIMS) formed in New York to professionalize practices, initially emphasizing physical asset protection for conglomerates facing disruptions. By the mid-1950s, and deductibles emerged as alternatives to costly policies, informed by post-war data on claim volatility; for instance, US manufacturing firms reduced premiums 20-30% via captive insurers analyzing historical loss distributions. Military-derived tools, such as simulations from the , influenced industrial forecasting, enabling probabilistic evaluation of wild risks like chemical spills—evidenced by the 1956 supply shocks. This era marked a transition to integrated frameworks, prioritizing causal identification over ad-hoc mitigation, though biases in corporate reporting understated tail risks until later crises.

Contemporary Milestones and Standards

The shift toward (ERM) gained momentum in the early 2000s, integrating risk considerations into strategic decision-making across organizations, spurred by scandals such as in 2001 and regulatory responses like the Sarbanes-Oxley Act of 2002, which mandated enhanced internal controls. This evolution emphasized holistic risk oversight beyond traditional and financial hazards, incorporating operational, strategic, and reputational risks. A pivotal milestone was the publication of the COSO —Integrated Framework by the Committee of Sponsoring Organizations of the Treadway Commission, which outlined eight components—including , , and monitoring— to align risk management with organizational objectives and performance. Updated in 2017, this framework shifted focus from controls to broader value creation through risk-informed strategy, influencing globally. In 2009, the released , a voluntary providing principles, framework, and process guidelines for managing risks in any context, emphasizing iterative and communication without prescribing specific tools. Revised in 2018 to enhance clarity on leadership commitment and integration, has been adopted by over 100 countries, promoting consistency while allowing customization. The 2008 global financial crisis prompted sector-specific advancements, notably , finalized by the in 2010 and phased in from 2013 to 2019, which imposed higher capital buffers, liquidity ratios, and to mitigate systemic banking risks. These standards collectively underscore a data-driven, forward-looking approach, with from post-implementation studies showing reduced volatility in adopting firms, though challenges persist in quantifying non-financial risks.

Core Processes

Establishing Context

Establishing the context serves as the foundational step in the risk management process, defining the parameters within which risks are identified, assessed, and treated. According to :2018, this involves articulating the organization's objectives, the internal and external environment influencing those objectives, and the stakeholders involved, thereby ensuring that subsequent risk activities align with the entity's strategic goals and operational realities. This step customizes the to the specific organization, avoiding generic approaches that fail to account for unique circumstances, such as varying regulatory landscapes or resource constraints. Key components include delineating the internal context—encompassing organizational culture, governance structures, capabilities, and processes—and the external context, which covers economic conditions, legal requirements, technological trends, and societal expectations. Risk criteria are also established here, specifying the nature and types of risks deemed acceptable, the organization's risk appetite, and thresholds for evaluation, such as quantitative measures like financial loss limits or qualitative scales for likelihood and impact. For instance, criteria might differentiate between tolerable risks that support innovation versus intolerable ones threatening viability, informed by stakeholder input to reflect diverse perspectives on risk tolerance. The scope and boundaries of the risk management effort are defined concurrently, limiting the focus to relevant functions, projects, or assets while excluding irrelevant areas to optimize . Failure to rigorously establish context can lead to misaligned risk priorities, as evidenced in cases where organizations overlook external disruptions like vulnerabilities, resulting in inadequate preparedness. This initial phase thus enables causal realism by grounding risk management in verifiable organizational realities rather than assumptions, facilitating evidence-based decision-making throughout the process.

Risk Identification

Risk identification is the initial and critical phase of the risk management process, focused on systematically discovering, recognizing, and documenting potential risk sources, events, causes, and consequences that could affect an organization's ability to achieve its objectives. As defined in ISO 31000:2018, this step generates an inventory of risks by examining internal factors such as operational processes and human resources, alongside external factors like market volatility or regulatory changes, to establish a foundation for risk analysis and evaluation. Failure to thoroughly identify risks can result in unmitigated exposures, as evidenced by historical incidents where overlooked threats led to significant losses, such as the 2008 financial crisis where subprime mortgage risks were underappreciated due to incomplete identification frameworks. The process emphasizes an iterative and consultative approach, involving stakeholders across levels to mitigate blind spots from siloed perspectives. In (ERM), best practices recommend integrating risk identification into ongoing business activities rather than treating it as a periodic exercise, enabling early detection of emerging threats like cybersecurity vulnerabilities or disruptions. Techniques must balance qualitative insights with empirical data to avoid overreliance on , which academic studies have shown can inflate perceived risks while missing causal precursors. Key methods for risk identification include:
  • Brainstorming and workshops: Group sessions leveraging collective expertise to generate risk ideas without initial judgment, proven effective in for surfacing .
  • Checklists and historical reviews: Standardized lists based on past incidents or industry benchmarks, such as those from regulatory bodies, to ensure consistency and coverage of recurrent risks.
  • SWOT analysis: Evaluation of strengths, weaknesses, opportunities, and threats to identify strategic risks tied to organizational capabilities.
  • Expert judgment and interviews: Consultations with subject matter experts or technique iterations to refine risk perceptions through anonymous feedback, reducing biases.
  • Scenario analysis and failure mode effects analysis (FMEA): Forward-looking simulations of adverse events or systematic breakdown of process failures to uncover low-probability, high-impact risks.
Challenges in risk identification arise from inherent uncertainties, including "unknown unknowns" that evade structured methods, necessitating hybrid approaches combining historical data with causal modeling to trace root causes rather than symptoms. of identified risks in a centralized register, including descriptions, categories, and initial likelihood assessments, facilitates and integration with broader ERM frameworks.

Risk Analysis and Evaluation

Risk analysis entails the systematic examination of identified risks to comprehend their underlying causes, probability of occurrence, and potential impacts on objectives. This step typically employs either qualitative or quantitative techniques to estimate risk levels, providing inputs for . According to :2018 guidelines, risk analysis refines understanding of risks by considering factors such as uncertainty, variability, and interdependencies, often distinguishing between threats and opportunities. Qualitative risk analysis uses descriptive scales to assess likelihood (e.g., rare, unlikely, possible, likely, almost certain) and consequence severity (e.g., insignificant, minor, moderate, major, catastrophic), frequently visualized in a probability-impact matrix to categorize risks as low, medium, or high. This approach relies on expert judgment and historical data, making it suitable for early-stage assessments where numerical data is scarce. Quantitative analysis, conversely, applies statistical models to derive numerical estimates, such as (probability multiplied by impact magnitude) or simulations like methods, which generate probability distributions of outcomes based on input variables. For instance, in , value-at-risk (VaR) models calculate potential losses at a given , such as 95% VaR estimating the maximum loss over a 10-day horizon not exceeded with 95% probability. Risk evaluation follows analysis by comparing estimated risk levels against predefined criteria, such as organizational risk appetite or tolerance thresholds, to prioritize risks for treatment. This determines whether a risk is acceptable, requires mitigation, or demands avoidance, often involving multi-criteria decision analysis to weigh factors like cost-benefit trade-offs. ISO 31000 emphasizes that evaluation accounts for residual risks after controls and aligns with strategic goals, ensuring decisions reflect the organization's context and external obligations. In practice, evaluation may reveal that a risk with high likelihood but low impact ranks below one with moderate likelihood and severe consequences, guiding resource allocation.
AspectQualitative AnalysisQuantitative Analysis
BasisSubjective scales and expert opinionObjective data and mathematical models
OutputRisk ratings (e.g., high/medium/low)Numerical metrics (e.g., probabilities, monetary values)
Use CaseInitial screening, resource-limited scenariosDetailed ,
AdvantagesQuick, low-cost, handles incomplete dataPrecise, supports statistical validation
LimitationsProne to , less granularData-intensive, assumes model accuracy
Evaluation outcomes inform risk treatment but require ongoing review, as assumptions in analysis—such as stable probabilities—may not hold amid changing conditions, underscoring the iterative nature of the process. Peer-reviewed studies highlight that overreliance on qualitative methods can underestimate tail risks in complex systems, advocating hybrid approaches for robustness.

Risk Treatment Strategies

Risk treatment refers to the selection and implementation of options for modifying risks to align with an organization's risk criteria, following the identification, analysis, and evaluation of risks. This process aims to either reduce potential adverse effects or exploit opportunities, though for negative risks, the focus is typically on minimization or elimination. According to :2018 guidelines, effective treatment involves balancing costs against benefits, considering legal, regulatory, and ethical factors, and documenting decisions in a treatment plan that specifies actions, responsibilities, timelines, and resources. The primary strategies include avoidance, , transfer, and , each applied based on the risk's assessed level, organizational tolerance, and feasibility of controls. Empirical studies across industries indicate that structured treatment planning correlates with improved project outcomes, such as reduced overruns and higher success rates, though effectiveness depends on proactive implementation rather than reactive measures. Avoidance entails completely eliminating exposure to a by ceasing or altering the activity that generates it, such as discontinuing a high-hazard product line or forgoing entry into volatile markets. This strategy is most suitable for risks with severe potential impacts where the probability of occurrence is non-negligible and no viable alternatives exist, as it achieves zero from the source. However, avoidance may incur opportunity costs, such as lost revenue, and is impractical for unavoidable operational risks like . In practice, firms in regulated sectors, such as banking post-2008 , have employed avoidance by divesting non-compliant assets to evade regulatory penalties exceeding billions in fines. Mitigation, also termed reduction, involves implementing controls or measures to lessen a risk's likelihood or severity, such as installing redundancies, programs, or technological safeguards. Common tactics include preventive actions (e.g., firewalls to curb cyber threats) and detective measures (e.g., audits to identify early). This approach preserves the activity while lowering to acceptable levels, though it requires ongoing ; for instance, mitigation in supply chains via diversified sourcing has empirically reduced disruption impacts by 20-30% during events like the 2021 Suez Canal blockage. Effectiveness hinges on rigorous monitoring, as partial controls can create false security without addressing root causes. Transfer shifts the risk's financial or operational burden to third parties through mechanisms like , , or contractual indemnities, without eliminating the underlying hazard. , for example, covers losses from events such as , with premiums calibrated to actuarial on historical claims. Hedging in financial markets or bonds in exemplify transfer, where empirical from multinational firms shows up to 50% reduction in net losses from transferred risks like currency fluctuations or contractor defaults. Limitations include incomplete coverage (e.g., deductibles or exclusions) and reliability, necessitating on providers to avoid . Acceptance involves consciously retaining a risk without further action beyond monitoring, applied to low-priority threats where treatment costs exceed benefits or risks fall within tolerance thresholds. This can be active (with contingency reserves) or passive, as seen in enterprises accepting minor IT downtime risks via self-insured funds rather than redundant systems. Studies of project portfolios reveal acceptance succeeds when paired with clear thresholds—e.g., risks under 5% impact probability—but fails if thresholds are unrealistically lax, leading to unmitigated escalations as in the 2010 incident. Post-treatment, all strategies require reassessment and integration into monitoring processes.

Monitoring, Review, and Adaptation

Monitoring and review in risk management involve the systematic observation of risks, controls, and the overall framework to detect deviations, emerging threats, or changes in context, ensuring ongoing alignment with organizational objectives. According to :2018, this process assures the quality and effectiveness of risk management design, implementation, and outcomes by tracking whether risks remain within acceptable levels and whether treatments are performing as intended. Adaptation follows as an iterative response, involving adjustments to risk strategies, such as revising treatments or reallocating resources, based on review findings to maintain resilience against evolving conditions. The implementation of continuous risk monitoring begins by mapping risk scenarios to key risk indicators (KRIs), building on prior risk identification and analysis to target specific risks with relevant, forward-looking metrics that provide early warnings, while aligning with the organization's risk appetite and thresholds before selecting tools or initiating data collection. Key techniques for monitoring include the use of these KRIs, which are measurable metrics signaling potential risk escalations, such as financial thresholds for risks or operational rates. Regular risk audits and review meetings facilitate periodic evaluations, often conducted quarterly or annually, to validate data accuracy and control efficacy. In practice, dashboards and automated tools enable real-time tracking, allowing organizations to respond proactively; for instance, deviations in trigger predefined escalation protocols. Failures in monitoring underscore its causal importance, as unaddressed changes in risk profiles can amplify losses. The 2016 Wells Fargo scandal, involving millions of unauthorized accounts due to unchecked sales pressures, exemplified how inadequate oversight of behavioral risks led to regulatory fines exceeding $3 billion and , highlighting the need for continuous behavioral and compliance surveillance. Similarly, General Electric's 2018-2020 financial reporting issues stemmed from poor monitoring of assumptions and exposures, contributing to a decline of over $100 billion, as undisclosed risks eroded trust. These cases demonstrate that static risk assessments without adaptation invite systemic failures, reinforcing the principle that risk environments are dynamic and require evidence-based recalibration. Effective adaptation integrates lessons from reviews into the broader framework, such as updating risk appetites post-incident or incorporating new regulatory requirements, as outlined in ISO 31000's emphasis on continual improvement. Best practices advocate involving cross-functional stakeholders in reviews to mitigate blind spots from siloed perspectives, ensuring adaptations are realistic and enforceable. Organizations that embed these processes report enhanced , with studies indicating up to 20-30% reductions in unexpected losses through vigilant monitoring.

Tools and Methodologies

Qualitative Approaches

Qualitative approaches in risk management emphasize subjective evaluation and expert judgment to identify, assess, and prioritize risks using descriptive scales rather than numerical metrics. These methods categorize risk likelihood and impact through ordinal terms such as "low," "medium," or "high," enabling rapid prioritization when quantitative data is scarce or preliminary analysis is required. They are foundational in standards like , which advocates structured techniques to ensure consistency despite inherent subjectivity. By leveraging human expertise, qualitative methods facilitate early risk screening, though they risk inconsistencies from individual biases unless facilitated rigorously. Key techniques include brainstorming, where multidisciplinary teams generate potential risks in unstructured sessions to uncover diverse threats without initial judgment. Interviews with stakeholders elicit detailed insights on risk sources and controls, often structured to probe causes and consequences systematically. The refines these inputs through iterative, anonymous rounds of expert questionnaires, aggregating opinions to converge on consensus estimates of risk severity. Risk matrices represent a core tool, plotting risks on a grid of probability against impact to visually prioritize them for treatment; for instance, a 5x5 matrix has been applied in healthcare to rank clinical hazards by severity and . Checklists standardize identification by drawing from historical or industry benchmarks, reducing omissions in repetitive processes like project planning. Scenario analysis extends this by constructing narrative "what-if" pathways, evaluating qualitative shifts in risk exposure under varied assumptions, as seen in assessments. These approaches integrate via workshops or root cause analysis, such as the "5 Whys" technique, to trace risks to underlying factors without quantification. In practice, they precede quantitative refinement, as qualitative outputs guide ; a 2021 analysis notes their role in identifying controls for high-priority risks in IT . Limitations arise from inter-analyst variability, addressed by and , ensuring outputs remain analytically defensible rather than purely intuitive.

Quantitative Models and Techniques

Quantitative risk management employs statistical and mathematical models to numerically assess risk probabilities, impacts, and uncertainties, enabling more precise compared to qualitative methods. These techniques rely on historical data, probability distributions, and simulations to quantify potential outcomes, often integrating variables like volatility, correlations, and extreme events. Common applications span , , and operations, where models convert qualitative risks into measurable metrics such as expected losses or confidence intervals. One foundational technique is (VaR), which estimates the maximum potential loss in value of a portfolio or asset over a defined time horizon at a specified level. For instance, a 95% one-day VaR of $1 million indicates a 5% probability that losses will exceed $1 million in the next day. VaR can be computed via historical simulation, variance-covariance methods assuming normal distributions, or approaches, though it assumes past patterns predict future risks and ignores losses beyond the threshold. Regulators like the Basel Committee have incorporated VaR into capital requirements for banks since the 1990s, but critics note its underestimation of tail risks during crises, as evidenced by the 2008 financial meltdown where VaR models failed to capture correlated defaults. To address VaR's limitations, (ES), also known as Conditional VaR, measures the average loss in the worst-case scenarios exceeding the VaR threshold, providing a fuller picture of severity. For a 95% VaR, ES calculates the mean loss among the 5% most adverse outcomes, making it more sensitive to extreme events and coherent under properties, unlike VaR. Empirical studies show ES better incentivizes risk mitigation in portfolios with fat-tailed distributions, though it requires robust data to avoid estimation errors. The mandated ES over VaR for certain regulatory stress tests post-2013 to enhance resilience against systemic shocks. Monte Carlo simulation is a versatile probabilistic method that generates thousands of random scenarios based on input probability distributions for variables like costs, durations, or market returns, yielding a distribution of possible outcomes to estimate metrics such as probabilistic cost overruns or value ranges. In , it supports by modeling dependencies and uncertainties, often revealing a 90% for completion times wider than deterministic estimates. This technique excels in handling non-linear relationships and multivariate correlations but demands significant computational resources and accurate input distributions; miscalibrated assumptions can amplify errors, as noted in validations against historical data. Stress testing complements these by subjecting models to hypothetical extreme scenarios, such as market crashes or geopolitical shocks, to evaluate resilience beyond normal conditions. Unlike VaR's probabilistic focus, stress tests apply deterministic shocks to assess capital adequacy or operational thresholds, with the U.S. Federal Reserve's annual exercises since 2009 requiring banks to withstand scenarios like a 35% equity drop. Quantitative variants incorporate elements, but results depend on scenario plausibility; the 2011 European sovereign debt crisis highlighted how under-stressed correlations led to underestimations. Other techniques include , which isolates the impact of varying single inputs on outputs to identify key drivers, and scenario analysis, which evaluates discrete "what-if" paths with assigned probabilities. These integrate into broader frameworks like decision trees for expected monetary value calculations, prioritizing risks by net present value impacts. Empirical validations, such as those in projects, confirm quantitative models reduce estimation biases when calibrated with real-world data, though they assume stationarity and independence that real systems often violate. Overall, these methods enhance foresight but require validation against historical events to mitigate model risk.

Integration of Emerging Technologies

Artificial intelligence (AI) and machine learning (ML) have transformed risk identification and analysis by enabling predictive modeling and pattern recognition from large datasets, allowing organizations to forecast potential disruptions with greater precision than traditional methods. For instance, ML algorithms process historical data to identify anomalies in financial transactions, reducing credit risk exposure by up to 20-30% in some banking applications through enhanced fraud detection. In supply chain management, AI integrates with real-time data streams to simulate scenarios, improving agility against disruptions like those seen in global logistics post-2020. Big data analytics complements AI by aggregating diverse sources—such as IoT sensor data and market feeds—for comprehensive risk evaluation, facilitating dynamic adjustments in (ERM). This integration supports quantitative techniques, where algorithms quantify probabilities and impacts more accurately, as evidenced by financial forecasting models that incorporate for volatility predictions. However, reliance on these tools demands robust to mitigate errors from incomplete inputs, with studies showing that poor can amplify model inaccuracies by 15-25%. Blockchain technology enhances treatment and monitoring by providing immutable ledgers that ensure transparency and , particularly in operational and financial domains. In mitigation, combined with AI verifies transaction histories in real-time, reducing disputes and risks; a 2024 analysis demonstrated its efficacy in decentralizing trust, cutting settlement times from days to minutes. For supply chains, it integrates with IoT to track assets, minimizing and enabling proactive adaptation to risks like goods. Despite these advancements, integration introduces challenges, including from skewed training data, which can perpetuate discriminatory outcomes in risk assessments, as highlighted in NIST's AI Risk Management Framework. Cybersecurity vulnerabilities escalate with AI deployment, where adversarial attacks can manipulate models, potentially leading to flawed decisions; reports from 2023-2025 note increased threats like data poisoning, necessitating layered defenses. Explainability remains a hurdle, as "" models hinder causal understanding, prompting calls for hybrid approaches blending AI with human oversight to align with first-principles risk evaluation. Recent trends emphasize AI governance in ERM, with platforms emerging to audit models and manage third-party tech risks by 2025.

Domain-Specific Applications

Enterprise Risk Management

In contemporary organizational contexts, risk management increasingly incorporates dynamic monitoring, cross-functional coordination, and real-time scenario adaptation to address complex and interdependent threats. Enterprise risk management (ERM) encompasses a holistic, organization-wide for identifying, assessing, prioritizing, and mitigating risks that could impede the achievement of strategic objectives, integrating risk considerations into and performance . Unlike siloed risk approaches, ERM seeks to align with , fostering resilience and value creation across functions. The Committee of Sponsoring Organizations of the Treadway Commission (COSO) defines ERM as a effected by an entity's , , and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity and manage risk to be within its to provide reasonable assurance regarding the achievement of entity objectives. Prominent frameworks guiding ERM implementation include the COSO ERM—Integrating with Strategy and Performance (updated 2017) and :2018. The COSO framework emphasizes five interrelated components: and culture, which establish oversight and ethical tone; strategy and objective-setting, linking risks to goals; performance, involving and prioritization; review and revision, for ongoing evaluation; and information, communication, and reporting, ensuring effective data flow. provides principles, a framework, and process for managing risk generically, stressing leadership commitment, integration, and continual improvement without prescriptive components. These standards differ in scope, with COSO more aligned to internal controls and strategy in U.S. contexts, while offers broader, international applicability. Empirical studies on ERM effectiveness yield mixed results, with some evidence of positive associations with firm performance metrics like and , particularly in insurers during disruptions such as , where mature ERM correlated with greater resilience. However, broader reviews highlight limited causal proof of value creation, noting that many implementations prioritize compliance reporting over strategic integration, yielding negligible impacts on decision quality or risk-adjusted returns. Factors influencing adoption include firm size, industry volatility, and regulatory pressure, but surveys of global firms indicate only partial , often stalling at basic risk registers without enterprise-wide embedding. Criticisms of ERM center on implementation pitfalls, including overreliance on quantitative models that overlook behavioral and emergent risks, failure to bridge functional silos leading to blind spots, and high costs without proportional benefits in stable environments. Organizational factors, such as denial or , often undermine protections, as seen in cases where ERM frameworks existed yet failed to avert crises like the 2008 financial meltdown. Moreover, ERM's emphasis on downside risks can inadvertently stifle by promoting excessive caution, and empirical gaps persist in measuring long-term causal impacts beyond correlations. Effective ERM demands strong buy-in and cultural shifts, yet many programs devolve into bureaucratic exercises disconnected from core operations.

Financial Risk Management

Financial risk management involves the systematic identification, measurement, and mitigation of uncertainties arising from financial transactions and positions, primarily in banking and investment contexts. Key objectives include preserving capital, ensuring , and maintaining amid market fluctuations and failures. Institutions employ frameworks aligned with international standards, such as those from the , to integrate risk considerations into processes. Principal types of financial risks include , the potential for borrower default leading to loss of principal or interest; , stemming from adverse changes in asset prices, interest rates, or exchange rates; , the inability to meet short-term obligations without incurring significant costs; and , arising from inadequate internal processes, systems, or external events. Credit risk constitutes a core concern for banks, with global non-performing loan ratios reaching 2.3% in 2023 according to IMF data, underscoring persistent vulnerabilities. Market risk exposure has intensified with rising volatility, as evidenced by the index spiking above 80 during the March 2020 market turmoil. Mitigation techniques encompass quantitative models like Value at Risk (VaR), which estimates potential losses over a specified period at a given confidence level—typically 99%—using historical or parametric methods, though it underperforms in extreme events by ignoring tail dependencies. Hedging via derivatives such as futures, options, and swaps transfers risk to counterparties, with global derivatives notional amounts exceeding $600 trillion as of 2022 per BIS statistics. Stress testing simulates adverse scenarios, such as a 30% equity drop or 200 basis point interest rate shock, to assess capital adequacy; post-2008 mandates require annual tests for U.S. banks with assets over $100 billion. Diversification across asset classes reduces idiosyncratic risks but cannot eliminate systemic exposures. In trading contexts, key practices include limiting risk to no more than 1-2% of total capital per trade, targeting risk/reward ratios of at least 1:2 or 1:3, and cutting losses quickly while letting winners run, which protects capital during losing streaks and enables long-term profit compounding. Regulatory frameworks, notably the , enforce minimum capital requirements tied to risk-weighted assets. (1988) targeted with an 8% capital ratio; (2004) incorporated market and operational risks via internal models; (2010, phased through 2019) introduced liquidity coverage ratios (LCR) mandating high-quality liquid assets to cover 30-day stress outflows and countercyclical buffers to curb procyclicality. These reforms raised global bank capital by approximately 2-3 percentage points from pre-crisis levels. The 2008 global financial crisis exposed deficiencies in , including overreliance on flawed rating models for mortgage-backed securities, inadequate buffers amid funding market freezes, and failure to stress test for correlated defaults across subprime exposures. U.S. subprime losses totaled over $500 billion, triggering ' bankruptcy on September 15, 2008, and necessitating $700 billion in TARP bailouts. Such events highlighted causal links between mispriced risks and systemic contagion, prompting enhanced emphasis on scenario analysis over static VaR. Despite advancements, empirical critiques persist regarding model assumptions' detachment from real-world nonlinearities and behavioral factors.

Operational and Supply Chain Risks

Operational risks refer to potential losses arising from deficiencies in an organization's internal processes, actions, technological systems, or external incidents beyond direct control. The defines as "the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events," a framework adopted widely in financial and non-financial sectors for its emphasis on quantifiable loss events. Empirical analyses indicate that such risks manifest systemically, with events like IT failures or process breakdowns propagating across interconnected firms, amplifying threats through direct losses and spillovers. Common subcategories include errors or , which accounted for significant portions of reported losses in banking datasets from 2001–2019; process inefficiencies, such as inadequate maintenance leading to equipment failures; system disruptions like cyberattacks; and external shocks including . In non-financial businesses, operational risks often stem from outdated technologies or poor , contributing to up to 7% of annual revenue losses in affected firms based on loss event databases. Supply chain risks, often overlapping with operational risks as external or process-related vulnerabilities, arise from dependencies on global networks for sourcing, , and distribution, exacerbated by just-in-time inventory practices that minimize buffers but heighten fragility to interruptions. Disruptions in 2020–2022, driven by , resulted in widespread shortages, with global supply chain pressure indices peaking at levels 2.5 times historical averages, correlating to slowed and elevated . Recent data from 2024 show 80% of organizations experiencing at least one supply chain disruption, many involving multiple incidents such as factory fires, labor strikes, or port congestions, leading to 3–5% higher operational expenses and 7% sales declines on average. Key examples include the 2021 blockage, which delayed over 400 vessels and cost global trade $9.6 billion daily in lost revenue; ongoing Red Sea attacks since late 2023, rerouting 90% of affected maritime and increasing emissions by up to 40% due to longer routes; and shortages from 2021–2023, which reduced automotive production by 11 million vehicles worldwide. Geopolitical factors, including tariffs and sanctions, further intensified risks in 2024–2025, with 16% of firms citing cybersecurity breaches in supplier networks as primary concerns, up from 5% pre-2020. Single-sourcing strategies, prevalent in 60% of multinationals for cost efficiency, have empirically increased , as evidenced by clustered failures during regional events like U.S. weather extremes in 2024.

Information Technology and Cybersecurity

Risk management in encompasses strategies to identify, assess, and mitigate threats to hardware, software, networks, and , including system failures, , and operational disruptions. In cybersecurity, it specifically addresses adversarial threats such as unauthorized access, data breaches, and , where vulnerabilities in or configurations can be exploited by ranging from nation-states to cybercriminals. The process begins with to map potential attack vectors and vulnerability assessments to quantify weaknesses, followed by based on likelihood and impact. Common cybersecurity threats include , which accounted for 36% of breaches in analyzed incidents, malware deployment, and supply chain compromises, with over 30,000 new vulnerabilities disclosed in alone, marking a 17% increase from prior years. Insider threats, often stemming from or malice, contribute to 20% of incidents, while external attacks like distributed denial-of-service (DDoS) can cause widespread . Global cyber attacks rose 30% in the second quarter of , reaching 1,636 weekly attempts per on average. The average cost of a data breach reached $4.44 million in 2025, underscoring the financial imperative for proactive controls. Established frameworks guide these efforts. The NIST Risk Management Framework (RMF) provides a seven-step process—categorize, select, implement, assess, authorize, monitor, and continuous improvement—for federal and private sector systems, integrating risk assessments with security controls. ISO/IEC 27001 offers a certifiable standard for information security management systems (ISMS), emphasizing risk treatment plans, including preventive measures like encryption and multi-factor authentication. Best practices include regular vulnerability scanning, patch management to address known exploits, and incident response planning to contain breaches within hours, as delays beyond 200 days correlate with 50% higher costs. Employee training reduces phishing success rates by up to 70%, while zero-trust architectures limit lateral movement in networks. Monitoring involves continuous threat intelligence feeds and automated tools for , enabling adaptation to evolving tactics like AI-enhanced attacks observed in 2024 nation-state operations. Compliance with regulations such as GDPR or HIPAA integrates risk management with legal requirements, though over-reliance on checklists can overlook novel zero-day vulnerabilities. Empirical evidence from breach analyses shows that organizations with mature programs detect incidents 28 days faster than laggards, reducing overall impact.

Project and Infrastructure Management

Risk management in project and infrastructure contexts focuses on identifying, analyzing, and mitigating uncertainties that threaten objectives such as schedule adherence, cost control, and quality delivery. According to the Institute's PMBOK Guide, encompasses planning risk management, identifying risks, performing qualitative and quantitative analyses, planning and implementing responses, and monitoring risks throughout the project lifecycle. This structured approach addresses threats like scope changes, resource shortages, and technical failures, which empirical studies link to higher success rates when rigorously applied; for instance, a 2013 analysis of construction projects found that adopting risk management practices significantly improved metrics including on-time completion and budget adherence. In infrastructure projects, which often span decades and involve public funds, risks extend to geopolitical, environmental, and regulatory factors, necessitating a value-chain-integrated framework that embeds from through operations. McKinsey emphasizes comprehensive risk-informed management, including early identification of site-specific hazards and stakeholder misalignments, to curb common overruns; large-scale projects without such integration face average cost escalations of 50-100%. Quantitative tools like simulations model schedule and cost variances by running thousands of iterations based on probabilistic inputs, proving effective in where delays from or supply disruptions can exceed 20% of timelines without . Qualitative methods, such as probability-impact matrices, prioritize risks by categorizing them into high, medium, and low based on likelihood and consequence, often visualized in risk registers that assign ownership and response strategies like avoidance, transfer via , or acceptance with contingencies. Software tools including Primavera Risk Analysis automate these processes, integrating with project schedules to forecast overruns; in , they facilitate for events like material price volatility, which contributed to the Channel Tunnel's costs ballooning from an estimated £4.7 billion in 1985 to £12 billion by 1994 due to inadequate initial risk provisioning. Empirical evidence underscores effectiveness: a meta-analysis of project data across industries revealed that robust risk planning correlates with up to 20% better outcomes in meeting triple constraints (scope, time, ), though failures persist from overlooked tail risks or poor implementation, as in U.S. projects where 30% exceed budgets by over 25% due to underestimated geotechnical issues. in infrastructure often involves public-private partnerships to share financial risks, with ongoing monitoring via key performance indicators ensuring adaptive responses to emerging threats like impacts on asset .

Health, Safety, and Environmental Risks

Health, safety, and environmental (HSE) risks in risk management encompass threats to worker well-being, operational safety, and ecological integrity arising from organizational activities. These risks are managed through systematic identification, evaluation, and mitigation to prevent injuries, illnesses, regulatory violations, and environmental damage. Frameworks such as for occupational health and safety and ISO 14001 for environmental management provide structured approaches, emphasizing proactive hazard control and compliance with legal requirements. Occupational health risks include exposure to hazardous substances, ergonomic strains, and biological agents, while safety risks involve physical hazards like machinery failures or falls. According to (ILO) estimates, nearly 3 million workers die annually from work-related accidents and diseases, with an additional 374 million suffering non-fatal injuries. Effective management relies on risk assessments that identify hazards, evaluate likelihood and severity, and implement controls such as engineering safeguards or . Environmental risks stem from emissions, waste generation, and resource consumption, potentially leading to , disruption, or climate contributions. ISO 14001 requires organizations to assess these risks and opportunities, integrating them into operations to minimize impacts and achieve goals. In practice, methods like hazard and operability studies (HAZOP) or failure modes and effects analysis (FMEA) are applied to process industries to anticipate environmental releases. Integrated HSE management systems combine these elements, as outlined in guidelines from bodies like the , which address common issues across sectors through pollution prevention, occupational health programs, and emergency preparedness. Notable failures, such as the 1984 where inadequate safety risk controls led to over 3,000 immediate deaths from a chemical release, underscore the causal link between deficient assessments and catastrophic outcomes. Regular audits and worker training enhance resilience, though empirical data shows persistent underreporting, with up to 62% of incidents going undocumented in some sectors.
HSE Risk Assessment StepsDescription
Identify hazardsExamine workplaces, processes, and substances for potential dangers to health, safety, or environment.
Assess risksEvaluate probability, severity, and vulnerable populations using qualitative or quantitative tools like risk matrices.
Control risksPrioritize elimination, substitution, , administrative measures, or PPE as a .
Record findingsDocument assessments for those with five or more employees, including actions and responsibilities.
Review and updateReassess periodically or after incidents, changes, or new regulations to ensure ongoing effectiveness.

Criticisms and Limitations

Empirical Evidence on Effectiveness

Empirical studies on risk management effectiveness yield mixed results, with some demonstrating positive associations between risk practices and outcomes like firm , while others reveal insignificant or context-dependent impacts. A 2023 meta-analysis of prior concluded that various forms of risk management exert a substantial positive influence on corporate financial , aggregating findings from multiple empirical investigations to support enhanced profitability and stability. Similarly, a 2020 meta-analytic review of practices found a strong overall contribution to firm , based on synthesized data from diverse studies emphasizing strategies' role in operational resilience. These positive effects often hinge on implementation quality, such as integrated (ERM) systems, which a 2021 study of Peruvian firms linked to improved managerial control and metrics like . However, broader reviews highlight inconsistencies, particularly for comprehensive ERM frameworks. A 2020 analysis of ERM's impact on non-financial Spanish listed companies reported mixed empirical outcomes, with no uniform reduction in or enhancement of across the sample, attributing variability to firm-specific factors like and sector. Earlier contingency-based research from 2009 similarly found that ERM's relation to firm depends on alignment with organizational contingencies, such as industry volatility, yielding positive results only in matched scenarios and neutral or negative effects otherwise. Quantitative assessments often struggle to isolate causal impacts, as self-reported data from surveys—common in these studies—may inflate perceived benefits due to among practitioners. In project contexts, evidence is particularly inconclusive. A of recent publications on risk management in projects concluded that assumptions linking practices to lack robust empirical backing, with no clear causal contribution demonstrated across analyzed datasets. A of IT project risk management similarly identified scant evidence of effective knowledge application in practice, despite theoretical advocacy, based on aggregated findings from multiple empirical sources indicating persistent implementation gaps. These limitations persist even in agile environments, where a study of software development projects found risk management tools effective only when tailored, but broadly underutilized, leading to no consistent uplift in rates. Critics note that much empirical work relies on correlational designs prone to endogeneity, where high-performing firms adopt better risk practices rather than vice versa, confounding attribution. A 2020 synthesis observed mixed correlations between ERM and financial metrics, with some studies detecting none after controlling for confounders like governance quality. Peer-reviewed critiques further argue that ERM's holistic approach, while theoretically sound, empirically falters in dynamic environments, as evidenced by pre-2008 data where advanced risk models failed to avert systemic losses despite widespread adoption. Overall, while domain-specific applications show targeted benefits, comprehensive risk management's enterprise-wide efficacy remains empirically contested, warranting cautious interpretation of proponent claims from consulting or industry sources.

Notable Failures and Case Studies

The 2008 global financial crisis exemplified systemic failures in , where institutions underestimated tail risks and correlations among asset classes despite using models like (VaR), which focused on historical data and normal distributions rather than extreme events. Banks such as maintained excessive leverage ratios exceeding 30:1, amplifying losses when subprime mortgage-backed securities defaulted en masse, leading to Lehman's bankruptcy filing on September 15, 2008, and triggering a freeze that erased $8-10 in global . Weaknesses in and overreliance on short-term funding exposed liquidity vulnerabilities, as regulators and firms failed to stress-test for correlated defaults across housing markets. In the BP Deepwater Horizon disaster on April 20, 2010, risk assessments prioritized cost savings over safety protocols, resulting in an explosion that killed 11 workers and spilled 4.9 million barrels of oil into the Gulf of Mexico over 87 days. BP's decisions, including using a long-string production casing and nitrogen foam instead of seawater for cement testing, increased blowout probability, while the blowout preventer's faulty seals and inadequate testing were overlooked in favor of expediting operations to avoid delays estimated at $100,000 per day. A U.S. government panel attributed the catastrophe to a "culture of every dollar counts," where risk management was subordinated to production pressures, leading to BP's $20.8 billion in settlements and fines. The Boeing 737 MAX crashes highlighted deficiencies in engineering and regulatory risk oversight, with the Maneuvering Characteristics Augmentation System (MCAS) software—designed to counteract aerodynamic issues from larger engines—relying on a single angle-of-attack sensor without redundancy, contributing to the Lion Air Flight 610 crash on October 29, 2018 (189 fatalities) and Ethiopian Airlines Flight 302 on March 10, 2019 (157 fatalities). Boeing's internal risk analyses underestimated pilot confusion from unbriefed MCAS activations during certification, driven by competitive pressures to match Airbus without full recertification as a new aircraft, resulting in a 20-month grounding, $20 billion in costs, and revelations of flawed hazard assessments that ignored prior simulator data on similar failures. Systemic shortcomings in Boeing's risk culture, including siloed engineering decisions and inadequate disclosure to the FAA, amplified these issues. The Silicon Valley Bank collapse in March 2023 demonstrated lapses in and management amid rapid growth, as the bank held $40 billion in long-duration bonds purchased at low yields, which lost 80% of value when rates rose, eroding $1.8 billion in unrealized losses not adequately hedged or provisioned. Despite warnings from internal risk teams, management pursued asset concentration in uninsured deposits from tech firms (over 90% uninsured), failing to diversify or extend liabilities, leading to a $ on March 9-10 and FDIC seizure—the second-largest U.S. bank failure. This case underscored overconfidence in historical low-rate environments and inadequate for deposit outflows exceeding 100% daily. These failures collectively reveal recurring patterns, such as overreliance on quantitative models without qualitative judgment, cultural prioritization of short-term gains, and insufficient integration of enterprise-wide , often exacerbated by lapses where boards deferred to without independent verification. Empirical reviews post-crisis indicate that enhanced and buffers, as mandated by reforms like Dodd-Frank, have mitigated some vulnerabilities but not eliminated them, as evidenced by persistent underestimation of non-linear in dynamic environments.

Philosophical and Systemic Critiques

Philosophical critiques emphasize the inadequacy of risk management's foundational assumptions in confronting irreducible and ethical complexities. Frank Knight's 1921 framework distinguishes ""—measurable via known probabilities—from "," where outcomes lack quantifiable likelihoods, rendering probabilistic tools ineffective for real-world decisions involving novel or complex phenomena. Standard practices, predicated on expected utility and Gaussian assumptions, thus foster overconfidence by conflating model outputs with reality, ignoring epistemic limits in forecasting non-stationary environments. Nassim Nicholas Taleb extends this by decrying quantitative metrics like Value-at-Risk for their fragility to fat-tailed distributions, where rare extremes dominate yet evade normal statistical capture, as demonstrated in historical market crashes. Such models, Taleb argues, invert by treating past data as predictive while suppressing variance that builds resilience, prioritizing precision over robustness. Ethically, risk assessments embed implicit value judgments—such as tolerability thresholds spanning orders of magnitude from detection (10^{-2}) to (10^{-5} to 10^{-6})—without rigorous , often sidelining distributional and in favor of aggregate . Critics like Kristin Shrader-Frechette highlight how technical definitions neglect qualitative dimensions, such as fairness in imposing involuntary s, leading to philosophically shallow policies that mask societal trade-offs. Systemically, risk management induces by insulating actors from consequences, as when insurers or regulators absorb losses, incentivizing excessive leverage—as seen in pre- banking, where hedged positions masked underlying exposures. This dynamic distorts incentives, elevating baseline risk-taking under the . Furthermore, mitigation strategies optimized for frequent, minor threats can amplify overall fragility by eroding ; Taleb contends that suppressing volatility—via interventions like just-in-time supply chains—renders systems convex to shocks, contrasting with antifragile designs that thrive on stressors. Interconnected risk models exacerbate this, propagating localized failures into cascades, as in the where correlated hedging failed under stress. Centralized oversight compounds the issue, substituting organic error-correction with brittle uniformity that ignores evolutionary feedback.

Recent Developments

AI and Data-Driven Advancements

and algorithms have enhanced risk management by processing vast datasets to identify patterns and predict potential disruptions with greater precision than traditional statistical methods. A of scientific literature from 2023 to 2025 indicates exponential growth in applications for , particularly in and operations, where models like random forests and neural networks outperform conventional approaches in detecting anomalies and probabilities. For instance, in evaluation, AI-driven models analyze such as transaction histories and market signals to generate dynamic risk scores, reducing default prediction errors by up to 20-30% in empirical tests conducted on banking datasets. Data-driven techniques, including analytics, facilitate real-time risk monitoring by integrating diverse sources like IoT sensors and market feeds, enabling proactive mitigation in sectors such as and cybersecurity. Studies on demonstrate that powered by AI can shorten response times to emerging threats, with one analysis of tramp shipping firms showing improved hedging effectiveness through data-optimized strategies that align financial and operational risks. In cybersecurity, models have advanced threat detection by learning from historical breach data, achieving detection rates exceeding 95% in controlled simulations while minimizing false positives compared to rule-based systems. Case studies from financial institutions, such as JPMorgan Chase's deployment of AI tools like IndexGPT, illustrate practical gains: these systems processed client data to enhance risk-adjusted planning, yielding $1.5 billion in cost savings and 20% revenue uplift by 2025 through refined predictive forecasting. Despite these benefits, empirical evidence underscores the need for robust validation to counter model , as cross-jurisdictional studies reveal variances in AI performance across regulatory environments, prompting calls for standardized benchmarks in risk analytics. Advancements continue with hybrid AI frameworks that incorporate to disentangle correlations from true risk drivers, as seen in applications where models reduced downtime risks by 15-25% in case studies. Overall, these data-centric innovations shift risk management from reactive to anticipatory paradigms, supported by peer-reviewed validations of enhanced decision-making under uncertainty.

Responses to Global Disruptions

Global disruptions, encompassing events like pandemics, geopolitical conflicts, and , challenge organizational continuity by amplifying interconnected vulnerabilities in supply chains and operations. Risk management responses emphasize building resilience through proactive identification, scenario-based planning, and adaptive to minimize cascading effects. (ERM) frameworks integrate these elements by assessing high-impact, low-probability events and aligning responses with organizational . The , declared a by the on January 30, 2020, illustrated the need for robust (SCRM) practices. Disruptions led to widespread shortages, with firms experiencing up to 40% delays in key inputs by mid-2020, prompting strategies like end-to-end transparency and multi-sourcing to enhance resilience. Empirical studies confirm that SCRM integration reduced disruption impacts on performance, though pre-pandemic reliance on just-in-time inventory exacerbated vulnerabilities in concentrated supplier networks. Geopolitical events, such as Russia's invasion of on February 24, 2022, triggered and shocks, with global oil prices surging over 30% in the following weeks due to sanctions and export halts. Risk responses involved rapid scenario analysis to evaluate exposure, portfolio diversification to against regional dependencies, and compliance assessments for sanctions adherence, enabling firms to sustain operations amid volatility. Organizations unable to exit affected markets adopted localized hedging and alternative to manage locked-in assets. For and pandemics, frameworks like the all-hazards approach guide comprehensive by treating diverse threats uniformly, incorporating bowtie to map prevention, , and recovery barriers. The U.S. National Response Framework, updated in 2025, coordinates federal responses to such events, emphasizing scalable and community-level capacities. Post-event reviews, such as those following in 2005 or , highlight the causal role of siloed risk assessments in failures, underscoring the need for integrated ERM to address systemic interdependencies. Common strategies across disruptions include:
  • Diversification: Shifting to multiple suppliers across regions to avoid single-point failures, as evidenced by reduced outage durations in diversified chains during COVID-19.
  • and simulation: Modeling extreme scenarios to quantify potential losses, with MIT research showing it identifies 20-30% more vulnerabilities than static audits.
  • Technology-enabled monitoring: for early warning, though implementation gaps persist in smaller firms.
Despite these measures, 2024 surveys indicate that evolving risks like protracted conflicts outpace many U.S. organizations' processes, necessitating continuous ERM updates for causal foresight over reactive fixes.

Evolving Frameworks and Global Risks

Risk management frameworks have evolved from siloed, reactive approaches focused on financial and hazard risks to holistic enterprise risk management (ERM) systems that integrate strategic, operational, and emerging threats across organizational boundaries. Early frameworks, such as those developed in the mid-20th century for insurance and finance, emphasized quantitative tools like Value-at-Risk (VaR) models reliant on historical data, but these proved inadequate during events like the 2008 financial crisis, prompting shifts toward qualitative assessments and scenario planning. By the 2010s, standards like COSO ERM (updated 2017) and ISO 31000 (revised 2018) formalized principles for identifying, assessing, and treating risks in a coordinated manner, incorporating governance and culture as core elements. Post-2020, frameworks adapted to pandemics and supply chain disruptions by embedding resilience testing and third-party risk evaluations, as seen in enhanced NIST Cybersecurity Framework updates (version 2.0, 2024) that address supply chain vulnerabilities. Global risks have driven further evolution, with interconnected threats like geopolitical conflicts, climate extremes, and technological disruptions necessitating multi-horizon frameworks that balance immediate shocks against decade-long challenges. The World Economic Forum's Global Risks Report 2025, based on surveys of over 900 experts, identifies state-based armed conflict as the top short-term risk (next two years), followed by extreme weather events and societal polarization, while long-term priorities include biodiversity loss and natural resource shortages. These reports highlight a "bleak" outlook, with economic risks like inflation receding in perceived severity due to stabilization measures, yet persistent underestimation of cyber and misinformation risks in non-Western contexts. Frameworks now incorporate horizon scanning—e.g., Deloitte's identified trends like AI-augmented decision-making and climate-adaptive strategies—to model cascading effects, such as how geopolitical tensions exacerbate energy transitions. In response, organizations are adopting integrated platforms that leverage AI for real-time risk quantification and geopolitical , as evidenced by Aon's 2023 Global Risk Management Survey where 70% of executives prioritized amid rising state-sponsored attacks. Frameworks like the U.S. Chamber of Commerce's 2025 geopolitical model emphasize decision trees for scenario-based mitigation, prioritizing diversification over reliance on single regions vulnerable to conflicts. However, challenges persist in quantifying "" events, with empirical critiques noting that even advanced ERM often fails to capture tail due to over-dependence on probabilistic models, as demonstrated by underpreparedness for despite prior simulations. This evolution underscores a causal shift toward proactive, data-driven architectures that align with global interdependencies, though implementation gaps remain in smaller entities lacking resources for comprehensive adoption.

Risk Communication

Key Principles and Methods

Effective risk communication prioritizes timeliness, delivering information promptly to counter and enable timely , as delays can amplify public anxiety and erode trust. Transparency requires openly acknowledging uncertainties and limitations in , avoiding over-reassurance that could undermine if contradicted later. Accuracy demands verifiable facts without exaggeration or minimization, supported by to maintain long-term trust. These elements align with Covello's seven cardinal rules, which include involving the public as partners, planning and evaluating efforts, listening to concerns, being honest and frank, collaborating with credible sources, addressing media needs, and communicating clearly with compassion. Clarity and simplicity form foundational methods, employing at a 6th-8th grade reading level, short sentences (under 27 words per key message), and repetition of core facts to overcome "mental noise" during crises. Messages should be actionable, providing specific steps such as preparatory actions (e.g., stocking supplies) or contingent plans (e.g., evacuation triggers), which empirical studies show reduce anxiety more effectively than vague reassurances. is a critical method, segmenting groups by demographics, trust levels, and prior experiences to tailor content—e.g., using culturally sensitive examples for minority communities or simplified visuals for low-literacy audiences—enhancing comprehension and compliance. Two-way engagement methods, such as public forums, social media interactions, and feedback loops, foster dialogue and adapt messaging based on real-time responses, as one-way broadcasts often fail to address underlying concerns. Message mapping structures communication hierarchically: three primary messages, each with three supporting facts, pre-tested for resonance to ensure brevity and relevance. For probabilistic risks, evidence-based formats like natural frequencies (e.g., "1 in 10 people" vs. "10% chance") and visual aids (e.g., icon arrays) improve understanding and decision-making over percentages alone, per systematic reviews of patient communication studies. Multiple channels—traditional media, digital platforms, and trusted spokespersons—extend reach, with empirical data indicating that consistent repetition across outlets boosts retention and action. Evaluation methods, including post-event surveys and behavioral metrics (e.g., compliance rates), inform iterative improvements, confirming that adaptive strategies outperform static ones in dynamic hazards.

Barriers and Empirical Challenges

Cognitive biases represent a primary barrier to effective risk communication, as individuals systematically deviate from rational assessment of probabilities and impacts. For instance, leads people to underestimate personal vulnerability to hazards, while the ambiguity effect causes avoidance of options with unknown probabilities, complicating efforts to convey uncertain risks. These distortions persist despite communication attempts, as evidenced by studies showing that probabilistic information often fails to override innate heuristics in . Institutional and procedural constraints further impede clear messaging, including legal restrictions that limit the scope of disclosures and inadequate allocation of resources such as staffing or funding for tailored campaigns. Technical jargon exacerbates this by alienating non-expert audiences, reducing comprehension and trust in conveyed information. Organizational or bureaucratic silos can also hinder consistent delivery, as seen in analyses of and environmental agencies where internal priorities conflict with public needs. Empirically, evaluating communication's impact poses significant challenges due to difficulties in establishing causation amid variables like media influence or pre-existing beliefs. A notable gap exists in practical , with programs rarely grounded in field-tested , resulting in unproven strategies that fail to alter behaviors during crises. Recent reviews highlight how digital-era and conflicting sources amplify misperception, as individuals struggle to discern credible signals from noise, often leading to polarized responses rather than informed action. inherent in risks themselves—coupled with public aversion to —further undermines message efficacy, as communicators cannot fully eliminate discomfort without oversimplifying facts.

References

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