We are convinced that transparency in qualitative terms about the risk situation is a benefit even if we are not—and probably will never be—able to exactly quantify the trigger-consequence diagrams. We collected and described a selection of psychological effects relevant for risk estimates in groups (Lermer, Streicher, Eller, & Sachs, 2014). This article expresses solely the opinion of the author. Risk and uncertainty are really two ends of a single spectrum. The Risk and Uncertainty Management Center is a proud sponsor of the RMI newsletter, a quarterly glimpse of news and events for risk management and insurance students, faculty and alumni, as well as Gamma Iota Sigma members. This means that all we have to do is gather enough data or wait long enough and we will be in a position to describe these uncertain events using risk management methods. Changing the resilience paradigm. Knight stresses that risk provides a basis for insurance. Resilience means the capability of an organization to recover from adverse events and continue with its operations. Even if we expected that none of these scenarios would materialize exactly as prescribed, we could still use them to test the risk management frameworks under dire circumstances . There is a growing demand in the risk industry for tools and methods to address uncertainty at various levels: methods, processes, organizational setup, and education. There are thousands of conceivable events which could trigger a large loss via direct and indirect consequences, feedback mechanisms, and so on. Reverse stress testing is a useful concept. Section “Enterprise Risk Management” contains a brief summary of enterprise risk management, based on an example from the insurance industry. We relax the standard assumption of known probabilities for such defaults by allowing for uncertainty. Knight argued that entrepreneurs who dare to act in the presence of the unknown future, emerged as a major response to fundamental uncertainty. This definition comes from Knight’s “Risk, Uncertainty and Profit” (1921). However, people perform quite well in this difficult task, often by using simple rules of thumb. In simple words, we can say business risk means a chance of incurring losses or less profit than expected. After thorough process analysis, we were in the position to redesign and improve our emerging risk process. As the global risk landscape is continuously changing, we will also see an evolution of the CARE system. These frameworks are holistic approaches to deal with all risks in the entire organization and simultaneously balance the expectations of the different stakeholders. After reading this article you will learn about Decision-Making under Certainty, Risk and Uncertainty. If we systematically underestimated the risk, we would offer risk transfer solutions at inadequate prices, that is, too cheap. Risk appetite and insurability increase with knowledge. We summarize some central aspects of the vast positive and normative literature on the role of various forms of insurance that attempt to smooth consumption, which can be uneven due to medical spending induced by health shocks. So how do we make decisions under risk versus uncertainty? At Munich Re the central platform to identify, analyze, and evaluate emerging risks is the emerging risk think tank. The Munich Re Emerging Risk Radar, a graphical tool to structure and monitor emerging risks, is designed accordingly. Two types of uncertainty faced by the individuals are examined. How much or how little we know about the events to be insured determines their measurability. Emerging risk scenarios are essentially stories, how a particular trend could evolve or a particular event could happen. Without uncertainty no … Hence we need to search for solutions in these two directions—data and models. Risk is when the odds or probabilities of future events can be estimated. There is no risk appetite for the unknown (for a more granular and entertaining description of levels of decreasing knowledge, see Lo & Mueller, 2010). Risk and Uncertainty. Consequently, while risk can be covered by insurance, uncertainty normally is not. Risk Analysis and Uncertainty in Flood Damage Reduction Studies. This links “risk” to “uncertainty”, which is a broader term than chance or probability. This links “risk” to “uncertainty”, which is a broader term than chance or probability. Such a scenario set would ideally cover the entire risk landscape. Stochastic models can be applied to random processes, as they are observed in nature, for example, heat transfer (Gardiner, 2002; van Kampen, 1992), and economics. Similar tools are applied to natural catastrophes like windstorms and earthquakes and also to biometric risks like morbidity and mortality rates. We’ll have a budget constraint, in terms of wealth, such as: The optimum point, given the budget constraint and being MU the marginal utility, is: Just until now, we’ve been using Morgenstern and von Neumann’s theory to analyse expected utility. Deterministic and stochastic time series analyses are possibilities to address risk and uncertainty. The definitions of risk and uncertainty were established by Frank H. Knight in his 1921 book, "Risk, Uncertainty, and Profit," where he defines risk as a measurable probability involving future events, and he argues that risk will not generate profit. A subjective risk is uncertainty-based on an individual's condition. This article provides an overview of risk management approaches from a practitioner’s point of view. Rather than simply waiting for and collecting more data, we need to develop the uncertainty management toolbox. This chapter shows that there is a welfare gain from health insurance because people are risk averse with respect to the financial implications of the prospect of ill health. risk and uncertainty a situation of potential LOSS of an individual's or firm's ASSETS and INVESTMENT resulting from the fact that they are operating in an uncertain economic environment. The Journal of Risk and Insurance, 2004, Vol. Risk averse individuals buy insurance by paying premium to reduce risks. Risks are events or conditions that may occur, and whose occurrence, if it does take place, has a harmful or negative effect. 3 Trends Creating Uncertainty in the Insurance Market, and Where Risk Managers Can Find Stability Increasing loss frequency and severity – spurred by several global trends – has made finding affordable coverage more difficult. Individuals will prefer to buy insurance in order to assure a certain amount of money (or to have a guarantee of lower losses), instead of its actuarial equivalent uncertain one. So in this talk, a16z general partner Angela Strange describes how pooling risk changes as we reinvent a legacy business like insurance … Risks and Uncertainties. The concept of resilience may be a promising way forward. Education and training of people, who take decisions under uncertainty, will be a success factor in the risk industry. Uncertainty is when the list of possible future events is unknown, so their odds of occurring cannot be estimated. This approach is based on the analysis of high-reliability organizations , which cannot afford to fail under uncertainty. Group think, that is, the tendency to arrive at suboptimal decisions in homogeneous groups, can be reduced by staffing the group appropriately. In the beginning we started to search for some sort of “fudge factor,” which would transform subjective into objective risk estimates. It is an area that comprises events of substantial complexity. There can be feedback features, which can give rise to self-enforcing dynamics. This may even lead to risk-seeking behavior as an attempt to recover from a loss situation, as can be observed in casinos and the stock market. In uncertainty, you completely lack the background information of an event, even though it has been identified. Therefore, it is important to understand how experts arrive at their conclusions, in particular how experts judge risks. For most practical purposes of our daily lives, both on individual as well as organizational level, resorting to the fundamental laws of nature or mathematical models will not be possible or feasible. In that sense the existence of risks is the foundation of the insurance industry. As has been already mentioned, the measurability of risks is a necessary condition for insurability. (1979). Let us suppose, data is in principle available, but scarce. Uncertainty cannot be insured against. This would form the basis for any detailed follow-up study and already contains the condensed knowledge of a heterogeneous expert group. Thus it is extremely important that the analysis is not systematically biased. Komplexität handhaben - Handeln vereinheitlichen Organisationen sicher gestalten. Emerging risks can be either developing trends or shock events. Progress comes with the introduction of new products and technologies with their own new risks. Even if the title of this section implies that uncertainty is something that can be managed, it should be emphasized that this need not be the case. The occurrence probability and loss potential of emerging risks are highly uncertain. Financial networks have long reached global dimensions. By learning from disciplines outside classical (i.e., mathematical) risk management, we could develop a better model for uncertainty management. Interdependency in the global risk landscape increases complexity. Emerging risks can and will arise from virtually any part of the global risk landscape. Risk Awareness: All employees need to be aware of the risks they face when performing their functions . The challenge in any emerging risk process is to cover the entire spectrum of potential emerging risks and provide sound and detailed knowledge from every discipline to the process. Uncertainty is not an unknown risk. Almost certainly these events will not materialize in reality, but some other will instead. Emerging risk management is based on the idea that trends or indications for shock risks develop over a long period as depicted in Fig. Ignoring the white or gray spots on the risk map is not really an option. Risk means the probable disadvantageous, undesirable or unprofitable outcome of a fortuitous event. 1010a Lecture 13 Uncertainty, Risk, and Insurance L13 Overview 1. Looking for new business opportunities and being confronted with an increasingly interconnected risk landscape, the industry sees the need for complementary methods to assess both risk and uncertainty. Understanding risk is the foundation of the insurance industry. The transfer of psychological research results into risk management applications was far from trivial. Even though we may be able to forecast many developments, much remains uncertain. However, we’ll see in the following entries alternative approaches. Applications range from creating risk awareness for staff and stakeholders, input for strategic business planning, background for tactical business decisions to the validation of quantitative enterprise risk models. Well-known heuristics are the recognition heuristic, the anchor heuristic, or the availability heuristic. Psychological research can offer theories to explain human risk judgment and its pitfalls. Same, same …: Stricter and more comprehensive application of existing approaches will be the solution. Eller, E., Lermer, E., Streicher, B., & Sachs, R. (2013). There is an increasing demand for risk transfer from the market. The second approach is typical for risk appetite strategies in the insurance industry. 4. A specific challenge is the understanding of the global risk landscape and its interdependencies . Uncertainty will be more relevant than in the past. The translation effectively occurs by making systematic use of expert judgment and intuition. By consciously working these boundaries of insurability, for example, by developing new methods or generating knowledge, rather than focusing only on risks that we know and understand well, we can gradually push back these boundaries and tap into new business opportunities. Our approach is forward looking and focuses on thinkable yet plausible consequences of significant events. There are no reasonable approaches to deal with the unknown, in particular in the insurance industry. The difference between risk and uncertainty can be drawn clearly on the following grounds: The risk is defined as the situation of winning or losing something worthy. Experience comes from bad decisions” (Tremper, 2008; Manser, 2008). The quantification will be based on subjective risk estimates. Management Accountability: The management team is ultimately responsible for the active management of the respective risk exposures and achievement of a sufficient return for the risks taken. clear, the practitioner’s question “what can we do better and how?” remains largely unanswered. We also suggested specific settings for different steps in the emerging risk process. The industry has developed practices and methods for risk transfer and risk management. Insurance companies take advantage of risk averse individuals to charge an extra surcharge to pay costs which are not covered by the premium. University of Lodz (2000495008) - Polish Consortium ICM University of Warsaw (3000169041) - Polish Consortium ICM University of Warsaw (3003616166)  11; Hoffrage & Garcia-Retamero, Chap. This is the most common form of crop insurance, referred to generally as multi-peril insurance. Lack of data and models will almost never lead to objective and statistically unbiased results. A risk averse individual may be willing to assure against a potential loss, but will pay only up to a certain price for this insurance: if the price exceeds this amount he will not acquire the insurance. In some cases we have a very accurate idea of the odds of an event happening, such as the McDonalds example above. This is the most common form of crop insurance, referred to generally as multi-peril insurance. Examples in the recent past are the subprime crisis in 2007 in the USA, which led to a global economic crisis, or the Thailand floods in 2012, which impacted key hardware suppliers and hence the IT industry on a global scale. At Munich Re we have been looking into these topics for several years. Even local events can have global consequences. For example, the question whether or not to start a particular career or engage in a relationship cannot be answered using mathematical models. Psychological effects of perception and distorted assessments play an important role. Complex risks are governed not only by their individual trigger events and foreseeable consequences, but by their internal dependency structure. University of Rome. We have to accept the fact that even with the best models and accurate data, we will not be able to predict the exact outcomes of our decisions and the consequences of events. The Journal of Risk and Uncertainty features both theoretical and empirical papers that analyze risk-bearing behavior and decision-making under uncertainty. We review and extend the economic analysis of risk and uncertainty as it relates to behavior mitigating health shocks. For validation purposes scenarios are particularly suited if we were able to arrive at a minimum quantitative characterization of the scenario. With the Complex Accumulation Risk Explorer (CARE), we want to establish a framework for the systematic collection and connection of knowledge from different disciplines. Hence the industry developed more and more refined tools to identify risks, to model and evaluate them, and finally to manage and steer risks. Uncertainty is not measurable, and so cannot be quantified and handled through insurance or other arrangements. At Munich Re, we therefore approach uncertainty due to lack of data with scenarios that describe potential and conceivable major loss events and with emerging risk processes. Risk averse individuals have, by definition, a greater preference to avoid risky situations than risk-loving individuals and, to this end, they will be willing to pay an extra amount of money in order to mitigate (or eliminate) the bad consequences of such a risk. 15.1 is the domain of the unknown. In order to improve our decisions and behavior in the risk-return space, we need risk management . Unexpected consequences from new technologies, for example, artificial intelligence or genetic engineering, are examples for uncertainty. How individuals perceive insurances depends on their prices, and on the individuals’ preferences and budget constrain. Request PDF | Risk and uncertainty in the insurance industry | Understanding risk is the foundation of the insurance industry. Big Data tries to find answers by analyzing huge, unstructured data sets. In the mathematical sciences, the development of statistical time series models helped tremendously to understand and model relationships between different variables and enabled us to predict future outcomes (e.g., Box, Jenkins, & Reinsel, 2008; Harvey, 1989). Its staff consists of experienced specialists with both deep knowledge in their own field and the ability to connect and communicate with other disciplines. American Risk and Insurance Association, Bulletin of the Commission on Insurance Terminology, vol. Likewise in business and commerce also an element of fear of loss always exists if … Risk implies future uncertainty about deviation from expected earnings or expected outcome. There is a fundamental difference between risk and uncertainty, which is explained in Section “Risk and Uncertainty”. Some risks are insurable (for example, the risk of fire or theft of the firm's stock), but not the firm's ability to survive and prosper. The development process started with rather intuitive, experience-based methods. They are in most circumstances restricted to limited time horizons, e.g., weather forecasts, or are governed by fundamental laws of nature, e.g., sunrise or chemical reactions. And measurability is what ultimately makes it possible to transfer risk from an insured to an insurer. Some risks are insurable (for example, the risk of fire or theft of the firm's stock), but not the firm's ability to … But while the relevance is intuitively (sic!) This does not mean that predictions are not possible. What we aim to achieve is the translation of an emerging risk from the uncertainty domain into the risk domain of Fig. These ideas were the basis for prospect theory (Kahneman & Tversky, 1979; see also Helm & Reyna, Chap. In insurance we are quite often faced with emerging risks, which can be assessed only qualitatively due to lack of statistical data. Trying to understand the global risk landscape is an active process, during which management options may be detected. They cannot make maximising decisions if they are not properly informed about the things they are buying and selling. Insurability is based on a number of principles, for example, that potential losses must be fortuitous and independent, that the number of comparable events must be large, and, above all, that potential losses must be measurable. Rarely any profit-oriented organization, and undoubtedly no insurance company, can simply wait for such a long time to gather data and knowledge for a comfortable and statistically valid risk assessment. Since there is no knowledge to be had here, we are clearly outside the remit of the insurance industry. A particularly important influence factor is human behavior and human decision making. These two strategies should not be regarded as mutually exclusive. Cross-company cooperation—for instance, as part of industry initiatives—helps improve the available data, but is often restricted by competition and legal requirements. The general principles of ERM frameworks can be applied, however. Academic and industry initiatives have already started to look into applications (Thoma, 2014). It is based on detection of early warning signals, so we would expect useful results from current Big Data initiatives. Naturally these worst case estimates depend on the quality of the models and data and are invalidated occasionally. This holds true for the risk industry in particular and for the economy and society in general, too. We also believe that it is crucial to accept uncertainty, rather than trying to manage it with (enhanced) risk management tools. While some steps are better performed on individual level (e.g., collection of information), others work better in a group setting (e.g., evaluation). The professional management of risks is at the very heart of the insurance industry. 212.191.64.7. The journal serves as an outlet for important, relevant research in decision analysis, economics, and psychology. Washington, DC: The National Academies Press. We do not suggest to follow either one or the other. Yet we are able to identify specific trends or events and map their consequences. Possible consequences are lower predictability and higher relevance of systemic risk. Uncertainty occurs in circumstances that cannot be analyzed either on a priori grounds, because they are too irregular, or through empirical observation, because they are too unique. This article analyzes the effects of uncertainty and increases in risk aversion on the demand for health insurance using a theoretical model that highlights the interdependence between insurance and health care demand decisions. Hypothesis 1. Section “Uncertainty Management and Emerging Risks” focuses on emerging risk management practices and their challenges. Risk can be measured and quantified, through theoretical models. … but different: An equally valid assumption could be that there is a regime shift in the risk landscape . The search for the needle in a haystack is not improved by adding more hay. The underlying assumption is that the risk landscape has not changed fundamentally, but only evolved to be more complicated. 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