risk and uncertainty in insurance

Competition is fierce in standard risk business. Consequently, while risk can be covered by insurance, uncertainty normally is not. Policy Example: Flood Insurance Much insurance is provided by the private market, but one important exception is flood insurance, which is generally provided by the federal government in the United States. This link can briefly be summarized as follows: “Good decisions come from experience. This is the most common form of crop insurance, referred to generally as multi-peril insurance. Decision-making under Certainty: . The policyholder, for instance, prefers low risk, that is, high security, over return. Almost certainly these events will not materialize in reality, but some other will instead. The insurance market allows agents to cover themselves against risk. 15.1 is the domain of the unknown. There are currently boundaries to the transfer of risk that cut across uncertainty. Eller, E., Lermer, E., Streicher, B., & Sachs, R. (2013). Knowledge of Alternatives: In Risk: Nevertheless, there is evidence that people can learn from warnings and risk information, such Subjective and Objective: Risk is objective while uncertainty is subjective as Risk can be measured while Uncertainty can only be realised. 3 Types of Risk in Insurance are Financial and Non-Financial Risks, Pure and Speculative Risks, and Fundamental and Particular Risks. We relax the standard assumption of known probabilities for such defaults by allowing for uncertainty. This domain is not of any real relevance for the transfer of risk. Second, we could analyze the global risk landscape as a whole and rank all events according to measures like loss relevance, for example. The insurance market allows agents to cover themselves against risk. 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. Understanding risk is the foundation of the insurance industry. Difference between Risk and Uncertainty. What is more important to us is the application of causality concepts (Pearl, 2009). Strategies of uncertainty governance are outlined in Section “Governance of Uncertainty”. All these actions of individual persons are done under fear of uncertainty and unpredictability of future. The transfer of psychological research results into risk management applications was far from trivial. 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. This links “risk” to “uncertainty”, which is a broader term than chance or probability. The development of “macro threats” is one of the main research areas of the Cambridge Centre for Risk Studies (Coburn et al., 2014). This turned out to be neither possible nor feasible. First, uncertainty measures provide a basis for comparing the market’s assessment of risk with private information and research.  12 and Lermer, Streicher, & Raue, Chap. 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. Risk measures the uncertainty that an investor is willing to take to realise a gain from an investment. In times of rising complexity, forecasting and control are an illusion and existing risk management practices will not be useful. There is a need for more bridges between industry and research. Even if we are not able to predict the exact outcome in detail, these models allow us to forecast statistical results like mean values and confidence intervals. The article shall not constitute or be deemed to constitute a representation of the views of Munich Re. Yet we are able to identify specific trends or events and map their consequences. Lack of data and models will almost never lead to objective and statistically unbiased results. As has been explained in the previous section, the proven risk management methods are not readily available in the context of uncertainty. Technological and economic progress are the main drivers for increasing complexity in the global risk landscape. 15.1. In uncertainty, you completely lack the background information of an event, even though it has been identified. Psychological effects on subjective risk estimates do not happen on individual level only. 3. Part of Springer Nature. Section “Uncertainty Management and Emerging Risks” focuses on emerging risk management practices and their challenges. A subjective risk is uncertainty-based on an individual's condition. 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. University of Lodz (2000495008) - Polish Consortium ICM University of Warsaw (3000169041) - Polish Consortium ICM University of Warsaw (3003616166) The impact of target person and answer format on risk assessment. The comprehensive survey of both brokers and businesses connected to the insurance sector reveals … These models improved transparency and led to better informed decisions. In particular, we have developed methods to make expert judgment in the context of risk and uncertainty less biased (Lermer, Streicher, Sachs, & Frey, 2013). Too many unknown influence factors will render strictly deterministic and even stochastic models useless. In the case of an unknown risk, although you have the background information, you missed it during the identify risks process. 4. Investments under Uncertainty… The risk management functions act as risk supervisors while respecting the responsibilities of the business units. However, social loafing, information sharing, and polarization are not as easily eliminated (see also Eller & Frey, Chap. clear, the practitioner’s question “what can we do better and how?” remains largely unanswered. by Taboola. 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. These factors cannot be controlled by the businessmen and can result in a … 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. Third, the level of public and market uncertainty is indicative of risk premia offered across asset classes. Uncertainty is far less able to be managed, but rather be approached differently (Weick & Sutcliffe, 2007). The general principles of ERM frameworks can be applied, however. For an insurance company, the decision to offer risk transfer products for emerging technologies at adequate prices is equally difficult. And measurability is what ultimately makes it possible to transfer risk from an insured to an insurer. The global risk landscape is evolving to higher complexity. The National Flood Insurance Act of 1968 sought to reduce suffering and economic damage from floods. They support decision making under difficult conditions, for example, lack of data and high complexity. 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. Emerging risk scenarios are essentially stories, how a particular trend could evolve or a particular event could happen. Understanding risk is the foundation of the insurance industry. Social proof is one example: if my peers are engaged in a certain activity, I should do so as well. 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. This risk does not seem likely to be largely impacted by the pandemic. c. the roles that risk and uncertainty play in stress testing; and d. the most appropriate ways to manage insurance in a sustainable manner. 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. The Journal of Risk and Uncertainty features both theoretical and empirical papers that analyze risk-bearing behavior and decision-making under uncertainty. doi: 10.17226/9971. 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. Similarly risk of life, health or property is reduced by purchasing a proper insurance. Thus we could qualitatively assess the drivers and implications of individual events. Since there is no knowledge to be had here, we are clearly outside the remit of the insurance industry. Introducing contract nonperformance risk reduces insurance demand. Risks are events or conditions that may occur, and whose occurrence, if it does take place, has a harmful or negative effect. As has been already mentioned, the measurability of risks is a necessary condition for insurability. 2.3. … but different: An equally valid assumption could be that there is a regime shift in the risk landscape . Policy Example: Flood Insurance Much insurance is provided by the private market, but one important exception is flood insurance, which is generally provided by the federal government in the United States. Over 10 million scientific documents at your fingertips. Sponsored Links. Expected utility theory holds that the demand for insurance can be translated as a demand for certainty. A particularly important influence factor is human behavior and human decision making. All of these decisions require evaluation under conditions of uncertainty, which is where insurance — really, distributed risk — comes in. Academic and industry initiatives have already started to look into applications (Thoma, 2014). Some risks are insurable (for example, the risk of fire or theft of the firm's stock), but not the firm's ability to … Accepting and embracing uncertainty are relevant in particular for large firms. Furthermore, risk- and … 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. Actuarial models provide the basis for quantitative risk management in the insurance industry. Uncertainty is a condition where there is no knowledge about the future events. At Munich Re, we endeavor to map the global risk landscape and its mutual dependencies in a database in order to make complex events transparent that result from the ever-increasing global interconnectedness.  8). This links “risk” to “uncertainty”, which is a broader term than chance or probability. These arguments support the demand to research, develop, and implement concepts and methods to deal with uncertainty successfully. Resilience means the capability of an organization to recover from adverse events and continue with its operations. Financial risks can be measured in monetary terms. The models we have to implement are less of mathematical but of organizational and procedural nature (Weick & Sutcliffe, 2007). Deterministic and stochastic time series analyses are possibilities to address risk and uncertainty. 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. Here the starting point of scenario selection is not the trigger event, but a certain loss amount or impact, which would bring the organization to the brink of destruction. So uncertainty is a blanket concept that can be broken down into risk and ambiguity. In Fig. y> The History of Insurance: Risk, Uncertainty and Entrepreneurship. If, on the other hand, we systematically overestimated the risk, we would put ourselves out of the market, because the insurance premium we charge would be too high. Microeconomics CHAPTER 8. There is an increasing demand for risk transfer from the market. 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. The business of insurance is based on dealing with uncertainty. 1010a Lecture 13 Uncertainty, Risk, and Insurance L13 Overview 1. We depend increasingly on the assessments and views of experts and amateurs to identify and characterize such events and their connections. There is an important link between experience, learning, decision making, and error culture within an organization. With these models the insurance company is able to infer expected outcomes and a quantified description of the unexpected. Risk is typically measured at some maximal occurrence probability, for example, the 1-in-200 year (99.5% quantile) or the 1-in-1000 year (99.9% quantile) event. Washington, DC: The National Academies Press. “Uncertainty about loss”. For Munich Re it is important to understand the different influence factors and their impact on risk estimates. A common structure for this landscape is the STEEP framework, where STEEP is short for sociology, technology, economic, environment, and politics. Independent Oversight: Risk oversight occurs at the level of business units, board oversight, and supervisory board oversight. When we characterize the unexpected by the level of knowledge and understanding, we can in very simple terms distinguish between three categories (see Fig. 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. Risks are in general not accepted without any reward. The Munich Re Emerging Risk Radar, a graphical tool to structure and monitor emerging risks, is designed accordingly. This forms an excellent starting point for an emerging risk process. This congressional act created the National Flood Insurance Program (NFIP). Two types of uncertainty faced by the individuals are examined. The industry has developed practices and methods for risk transfer and risk management. In P. Badke-Schaub, G. Hofinger, & K. Lauche (Eds. The comprehensive survey of both brokers and businesses connected to the insurance sector reveals … 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. A specific challenge is the understanding of the global risk landscape and its interdependencies . This approach is limited, however. A Real-World Example of Risk and Uncertainty I also understand there is a debate on the meaning of these terms going back to Knight (1921) and Ellsberg (1961). Another reasonable strategy could be return maximization under certain risk restrictions. Emerging risk management is based on the idea that trends or indications for shock risks develop over a long period as depicted in Fig. An ideal think tank member is a specialist and generalist at the same time. The title simply serves to express its proximity to the term risk management. Lermer, E., Streicher, B., Eller, E., & Sachs, R. (2014). The occurrence probability and loss potential of emerging risks are highly uncertain. This article expresses solely the opinion of the author. 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 as reflected in required capital calculations address only adverse consequences, while provision for uncertainty in the valuation of liabilities or in Fit and Proper: All staff in charge of risk management needs to be appropriately trained and experienced in risk management techniques. 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. A condition of certainty exists when the decision-maker knows with reasonable certainty what the alternatives are, what conditions are associated with each alternative, and the outcome of each alternative. Similar tools are applied to natural catastrophes like windstorms and earthquakes and also to biometric risks like morbidity and mortality rates. The dynamics of emerging risks: typical course of signals and options for action with emerging risks. Sound policy neither hides this uncertainty, These decision making rules are often described as heuristics (see also Raue & Scholl, Chap. (1979). Possible consequences are lower predictability and higher relevance of systemic risk. New technologies and their associated risks for example can evolve over time horizons of several decades. We do not suggest to follow either one or the other. When we improved our emerging risk management and incorporated ideas and insights from psychology into our processes, we also experienced the gap between the intellectual world and the commercial world.  6). With the Complex Accumulation Risk Explorer (CARE), we want to establish a framework for the systematic collection and connection of knowledge from different disciplines. Complex risk management as described in Section “Complex Risk Management” is an example for the second strategy. Coming from quantitative risk management, such an outcome would clearly be desirable and could easily be integrated into our systems. Risk Analysis and Uncertainty in Flood Damage Reduction Studies. Insurance: Individuals transfer risks by buying insurance against financial loss under a variety of risks such as death, injury, theft, fire, etc. These limits will become more important going forward. The shareholder on the other side has higher return expectations and is ready to accept a certain amount of risk. Organizations need to find ways to better cope with surprises that will arise from a complex risk landscape. Rather than simply waiting for and collecting more data, we need to develop the uncertainty management toolbox. Risk Awareness: All employees need to be aware of the risks they face when performing their functions . An insurance contract is a particular type of risk transfer from the insured to the insurance company or from the insurance company to the reinsurance company. 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. Even local events can have global consequences. There is limited room for surprises, such as we will see consequences we would not have anticipated at all. The transfer of knowledge into applications needs to be strengthened. This definition comes from Knight’s “Risk, Uncertainty and Profit” (1921). There are two possible scenarios: S1, with a probability of happening p1: there is indeed a robbery, the individual loses R; S2, with a probability of happening p2: there is no robbery. Profits are their reward. For example, the question whether or not to start a particular career or engage in a relationship cannot be answered using mathematical models. The entire disclosure for any concentrations existing at the date of the financial statements that make an entity vulnerable to a reasonably possible, near-term, severe impact. Measurability and in particular quantification of risks depend on the level of knowledge, the availability of models, and data. How little we know about the risk also determines how high the price or risk premium for the transfer needs to be: the less we know, the more difficult and expensive the risk transfer. Depending how important these alternatives and how strongly our beliefs are, such scenarios can be used in a number of ways in ERM frameworks. Data and quantitative tools are replaced by experience and qualitative assessment. We will continue to enhance the CARE system and work with experts from both the insurance industry as well as outside to improve coverage and stability of the database. The Tohoku earthquake in Japan in 2011, for instance, was stronger than the models had anticipated. Another example is the systematic development of worst case scenarios. As the global risk landscape is continuously changing, we will also see an evolution of the CARE system. “Measurable uncertainty”. 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. We feel that we have benefited tremendously by looking outside the obvious quantitative disciplines. Risk and uncertainty as reflected in required capital calculations address only adverse consequences, while provision for uncertainty in the valuation of liabilities or in Insurance companies take advantage of risk averse individuals to charge an extra surcharge to pay costs which are not covered by the premium. Demand for uncertain probabilistic insurance. 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. We will use an analytical example for a better understanding: Imagine an individual with an initial wealth of W0 who faces the possibility of getting robbed an amount of R. He or she has the option of insuring an amount of K for a risk premium of λK. There are essentially two different applications for such a system. In the production industry global, multi-tier supply chain networks are increasingly common. When everyone has comparable tools to quantify risk, the only way to remain competitive in the market is to become more cost efficient. 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. The journal serves as an outlet for important, relevant research in decision analysis, economics, and psychology. Manser, T. (2008). Risk strategies are an essential part of enterprise risk management (ERM) frameworks in the insurance industry. Not only that humans are loss averse in general—in economic terms having more is typically better than having less—but also changes in outcome are perceived differently in loss situations versus gain situations. © 2020 Springer Nature Switzerland AG. Uncertainty and risk are closely related concepts in economics and the stock market. This does not mean that predictions are not possible. Chapter 23: Uncertainty and Risk. Appropriate answer formats, for example, verbal and numerical scales, serve to obtain some minimal set of quantitative information from expert questionnaires (see also Bostrom et al., Chap. The world is in large parts not deterministic and foreseeable. There are opportunities for profitable growth, more than in the risk domain. In insurance we are quite often faced with emerging risks, which can be assessed only qualitatively due to lack of statistical data. After reading this article you will learn about Decision-Making under Certainty, Risk and Uncertainty. This can remove a stumbling block on the way to completely different concepts. Management of risk alone will most likely not be sufficient. The translation effectively occurs by making systematic use of expert judgment and intuition. Enterprise risk management is a continuously developing practice. Because then even more surprises will occur, and the organization’s fate is determined more by fortune rather than by responsible actions. The predictions of impacts, however, are fraught with significant uncertainty. At Munich Re, we are engaged in a number of areas in pushing back these boundaries and extending the insurability of risk. Do you want to read the rest of this publication? The reward in the financial industry is called return. The following list contains a list of principles used at Munich Re Group: There remains a large source of surprises, however, and it turns out that this source is by far the larger part of the unexpected. Each stakeholder has a different preference in the risk-return space. Oskar Morgenstern and John von Neumann’s expected utility theory, which analyses individuals’ risk aversion, proves that different individuals have different perspective towards risk. Lack of experience and data does not allow the application of established risk management practices. More information is not useful by itself. There are thousands of conceivable events which could trigger a large loss via direct and indirect consequences, feedback mechanisms, and so on. Insurance contracts may fail to perform, leading to a default on valid claims. A lot can be learned from these organizations, in particular about decision making in complex situations. These ideas were the basis for prospect theory (Kahneman & Tversky, 1979; see also Helm & Reyna, Chap. HAMILTON, Bermuda – September 26, 2018 – Argo Group International Holdings, Ltd. (NYSE: ARGO), an international underwriter of specialty insurance and reinsurance, today released the findings of The Future of Insurance – 2018 Insights: Risks, Uncertainty and a Looming Talent Gap. In contrast to time series analysis methods, these scenarios are by no means predictions but offer plausible alternatives how the future could look like. 6. 2. This is the most common form of crop insurance, referred to generally as multi-peril insurance. Higher complexity in the global risk landscape has two major consequences , which are particularly important for an insurance company (Sachs & Wadé. Section “Enterprise Risk Management” contains a brief summary of enterprise risk management, based on an example from the insurance industry. The goal of a risk management strategy could be to minimize risk given a particular return expectation. If there were no risks, there would be no need for insurance. This effect also works in the other direction and can lead to bubbles. Same, same …: Stricter and more comprehensive application of existing approaches will be the solution. It is obvious that not all risks can be insured. We put less emphasis on statistical methods due to lack of data and the impossibility to parametrize such a multidimensional network. Over time more and more risks could also be quantified, and highly sophisticated mathematical models were developed. To take up a somewhat more current example, let’s say that my Great … UNCERTAINTY AND RISK Exercise 8.2 You are sending a package worth 10 000AC. And complex is more than just complicated. It is an area that comprises events of substantial complexity. There will always remain the possibility of unexpected outcomes and surprises. The Journal of Risk and Uncertainty features both theoretical and empirical papers that analyze risk-bearing behavior and decision-making under uncertainty. Hence we are normally confronted with unexpected events and surprising consequences of our decisions. Naturally these worst case estimates depend on the quality of the models and data and are invalidated occasionally. Hence we can construct scenarios for each emerging risk and be fairly sure about its accuracy, if an emerging risk materialized. Even though we may be able to forecast many developments, much remains uncertain. This textbook incorporates the author’s previous book " The Economics of Uncertainty and Insurance " and extends it with the addition of several new chapters on risk sharing, asymmetric information, adverse selection, signaling and moral hazard. Thus it is extremely important that the analysis is not systematically biased. We now focus on our main area of interest, the effect of uncertainty with respect to contract nonperformance risk on insurance demand (i.e., r is unknown). People face risk and uncertainty in making decisions because of incomplete information. First, we are able to extract event trees, both forward and backward in time. They should be pursued simultaneously, as both have their individual merits. c. the roles that risk and uncertainty play in stress testing; and d. the most appropriate ways to manage insurance in a sustainable manner. 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. There is no return without risk. Well-known heuristics are the recognition heuristic, the anchor heuristic, or the availability heuristic. The underlying assumption is that the risk landscape has not changed fundamentally, but only evolved to be more complicated. We need to develop a different toolbox to cope with uncertainty. All of these can have significant impact on experts’ risk estimates. risk and uncertainty. There is even a dark side of diversification: more diversification in connected networks can actually increase the risk for systemic events, that is, the default of a single node can cause the collapse of the entire network (for an example, see: Battison, Gatti, & Gallegati, 2008). Risk, which is often used to mean uncertainty, creates both problems and opportunities for businesses and individuals in nearly every walk of life.Executives, employees, investors, students, householders, travelers, and farmers all confront risk and deal with it in various ways. Insights from organizational and social sciences offer promising paths forward. 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. Uncertainty is not measurable, and so cannot be quantified and handled through insurance or other arrangements. There can be feedback features, which can give rise to self-enforcing dynamics. While this is a plausible method to deal with uncertainty, we believe rising complexity and uncertainty will render long-term planning a futile exercise. This article provides an overview of risk management approaches from a practitioner’s point of view. The insurance industry would have put itself out of the competition. Linkov, I., Bridges, T., Creutzig, F., Decker, J., Fox-Lent, C., Kröger, W., … Nyer, R. (2014). The main differences to risk are the lack of data and lack of (mathematical) models in the context of uncertainty. They also need to have relevant business knowledge and understand the needs of the business units. With emerging risks and complex risks, we have given two examples and also demonstrated how risk management can be improved by using methods from uncertainty management and interdisciplinary cooperation between the insurance industry and psychological research. With experience and ongoing refinements of models, these surprises should in principle become less frequent. Risk, Liability and Insurance in Valuation Work, 2nd edition This guidance note is intended to assist both members and their clients in understanding the main risks and liabilities associated with valuation. The non-insurable may become insurable. At Munich Re the central platform to identify, analyze, and evaluate emerging risks is the emerging risk think tank. Request PDF | Risk and uncertainty in the insurance industry | Understanding risk is the foundation of the insurance industry. It is based on detection of early warning signals, so we would expect useful results from current Big Data initiatives. In D. Helbing (Ed.). The discovery of these fundamental laws of nature made natural sciences a very successful undertaking. The second approach is typical for risk appetite strategies in the insurance industry. Emerging risks can and will arise from virtually any part of the global risk landscape. Reinsurers are reacting to the changing nature of risk by exercising more caution as they seek to quantify the impact of shifts in previous assumptions. What about uncertainty? Risk and uncertainty are really two ends of a single spectrum. Yet there are strong reasons to believe that if they materialized the consequences would be significant for the organization. Risks can be managed while uncertainty is uncontrollable. Section “Complex Risk Management” contains a specific example of how to deal with complex risks and their inherent uncertainty. To the best of our knowledge, this is the first attempt in the insurance industry to analyze the global risk landscape with trigger -consequence diagrams. Keywords: risk, uncertainty, insurance, complex risks, emerging risks, enterprise risk management, expert judgment, intuition. However, we’ll see in the following entries alternative approaches. The search for the needle in a haystack is not improved by adding more hay. So how do we make decisions under risk versus uncertainty? For example, deterministic nonlinear models and complex systems theory have been used in a wide range of applications since the 1990s (Casdagli & Eubank, 1992; Kantz & Schreiber, 1997). Emerging risks can be either developing trends or shock events. Essentially we would need only two parameters: the occurrence probability and the corresponding loss amount. Group think, that is, the tendency to arrive at suboptimal decisions in homogeneous groups, can be reduced by staffing the group appropriately. Cite as. × Save. After thorough process analysis, we were in the position to redesign and improve our emerging risk process. These frameworks are holistic approaches to deal with all risks in the entire organization and simultaneously balance the expectations of the different stakeholders. HAMILTON, Bermuda – September 26, 2018 – Argo Group International Holdings, Ltd. (NYSE: ARGO), an international underwriter of specialty insurance and reinsurance, today released the findings of The Future of Insurance – 2018 Insights: Risks, Uncertainty and a Looming Talent Gap. Risk and uncertainty are two faces of the unexpected. It has always been influenced by related fields of research, in particular economics, mathematics, and physics. Likewise in business and commerce also an element of fear of loss always exists if … Risk and Uncertainty. The leftmost part in Fig. How risky? Progress comes with the introduction of new products and technologies with their own new risks. For instance, we aim to estimate the likelihood to trigger certain consequences and their relevance to an insurance company for each event in the database. By learning from disciplines outside classical (i.e., mathematical) risk management, we could develop a better model for uncertainty management. Two types of uncertainty faced by the individuals are examined. There are no reasonable approaches to deal with the unknown, in particular in the insurance industry. What we aim to achieve is the translation of an emerging risk from the uncertainty domain into the risk domain of Fig. 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. Resilience is studied intensively in the academia (e.g., Linkov et al., 2014). This relationship is commonly expressed as “there is no free lunch.” In addition, there is a positive correlation between risk and return. 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. It is neither very promising nor economically feasible to single out a few events, try to develop scenarios, and prepare for those. Second, changes in uncertainty indicators often predict near-term flows in and out of risky asset classes. The Journal of Risk and Insurance, 2004, Vol. There are a number of principles, which are deduced from regulatory requirements and give guidance for the design of risk management structure and tools. Request file Hence the industry developed more and more refined tools to identify risks, to model and evaluate them, and finally to manage and steer risks. 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. Emerging risks in the section above are characterized by their high uncertainty regarding occurrence probability and loss severity. Risks and Uncertainties. If you are risk-neutral, should you buy insurance? In an organizational context, it is equally important to consider group effects, as many risk assessment processes involve groups. 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. So that covers risk. This awareness implies an openness to regularly monitor and if necessary challenge existing concepts, procedures, and rules. This risk does not seem likely to be largely impacted by the pandemic. Risk and uncertainty are really two ends of a single spectrum. This definition comes from Willett’s “Economic Theory of Risk and Insurance” (1901).  11; Hoffrage & Garcia-Retamero, Chap. Stakeholders for an insurance company are the insured or policyholder, the shareholders and the regulator, or financial supervisory authority. Reverse stress testing is a useful concept. Therefore, it is important to understand how experts arrive at their conclusions, in particular how experts judge risks. Psychological effects of perception and distorted assessments play an important role. This definition comes from Knight’s “Risk, Uncertainty and Profit” (1921). These boundaries separate the insurable from the non-insurable. 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. Within a large behavioral experiment, we show that introducing risk and uncertainty each leads to significant reductions in insurance demand and that the effects are comparable in magnitude (17.1 and 14.5 percentage points). Risk can be measured and quantified, through theoretical models. Psychological research can offer theories to explain human risk judgment and its pitfalls. II, no.1, March 1966. Uncertainty will be more significant than risk in the future. Knight stresses that risk provides a basis for insurance. Chapter 23: Uncertainty and Risk. 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. Many biases in risk assessment and regulation, such as the conservatism bias in risk assessment and the stringent regulation of synthetic chemicals, reflect a form of ambiguity aver-sion. We would make a loss in the long run as premium income would not be sufficiently high for the loss experienced. In such a globally networked society and economy, local events can have global consequences. Education and training of people, who take decisions under uncertainty, will be a success factor in the risk industry. Accordingly, we also refer to a principle-based approach for dealing with uncertainty (Weick & Sutcliffe, 2007). Unlike in the past, there is no amount of waiting or data collection that would prove helpful here. Risk Transparency: Risk transparency is essential so that risks are well understood by senior management and can be balanced against business goals which are recorded in the business plans. In the previous sections, we have demonstrated that risk management in the insurance industry has its limits when we do not have adequate data and models for proper quantification. The following are a few differences between risk and uncertainty: 1. We relax the standard assumption of known probabilities for such defaults by allowing for uncertainty. At Munich Re we have been looking into these topics for several years. Implementation of new ideas and concepts in organizations will always be a challenge. Insurance contracts may fail to perform, leading to a default on valid claims. In some cases we have a very accurate idea of the odds of an event happening, such as the McDonalds example above. For validation purposes scenarios are particularly suited if we were able to arrive at a minimum quantitative characterization of the scenario. In large organizations like insurance companies, there is typically a large, heterogeneous, and multidisciplinary staff. With ERM the company strives for transparency of its risk situation and a balance between the different stakeholders’ expectations. In a psychological context, reward is usually some kind of positive sensation. Uncertainty cannot be insured against. Consequently, while risk can be covered by insurance, uncertainty normally is not. So in this talk, a16z general partner Angela Strange describes how pooling risk changes as we reinvent a legacy business like insurance … Risk implies future uncertainty about deviation from expected earnings or expected outcome. Examples are professional fire-fighting teams and operators of power plants or airlines. 212.191.64.7. 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 market would have long forgotten about insurance and would have developed other ways to manage the risk. By using no or only few observations, we try to extrapolate possible paths into the future. In simple words, we can say business risk means a chance of incurring losses or less profit than expected. This would form the basis for any detailed follow-up study and already contains the condensed knowledge of a heterogeneous expert group. The industry has developed practices and methods for risk transfer and risk management. Over time more and more risks could also be quantified, and highly sophisticated mathematical models were developed. 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. Two types of uncertainty faced by the individuals are examined. The journal serves as an outlet for important, relevant research in decision analysis, economics, and psychology. But while the relevance is intuitively (sic!) Knight argued that entrepreneurs who dare to act in the presence of the unknown future, emerged as a major response to fundamental uncertainty.  4; and Birnbaum, Chap. Let us suppose, data is in principle available, but scarce. Embedding: Risk management functions are embedded in the operation at all levels. Risk, Liability and Insurance in Valuation Work, 2nd edition It guides members in the negotiation of equitable contracts with clients and the avoidance of major risks … In an article by Eller, Lermer, Streicher, and Sachs (2013), we provided an overview of psychological influence factors on individual level and how these can be coped with in risk management. Over time more and more risks could also be quantified, and highly sophisticated mathematical models were developed. The ongoing globalization leads to increasing interconnectedness in the global risk landscape. University of Rome. 1, 41-61 THE EFFECTS OF UNCERTAINTY ON THE DEMAND FOR HEALTH INSURANCE Cagatay Koc ABSTRACT This article analyzes the effects of uncertainty and increases in risk aversion on the demand for health insurance using a theoretical model that highlights In order to improve our decisions and behavior in the risk-return space, we need risk management . Psychological literature is full of extremely important results for risk managers. However, we are confident that we can do a lot better than in the past by rigorously questioning and improving our risk management processes. Risk is when the odds or probabilities of future events can be estimated. You estimate that there is a 0.1 percent chance that the package will be lost or destroyed in tran-sit. Trade credit networks and systemic risk. Let’s first see how the original expected utility theory works. Experience comes from bad decisions” (Tremper, 2008; Manser, 2008). In the context of risk and uncertainty, COVID-19 was a foreseeable risk. Lermer, E., Streicher, B., Sachs, R., & Frey, D. (2013). This risk also sometimes includes the uncertainty … Big Data tries to find answers by analyzing huge, unstructured data sets. 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. The classical retrospective approach is thus supplemented by a prospective one. The Risk and Uncertainty Management Center takes a holistic approach to managing risk, emphasizing decision-making skills that are applicable across a wide variety of risks confronting organizations. There is a need for uncertainty competence, rooted in the belief that uncertainty is to be approached with a positive stance. 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. Complex events take place in interconnected, strongly interdependent structures and are characterized by a low degree of predictability. Expert judgment is the basic input into the emerging risk management process. 1. The Spanish flu (1918–1920), Asian flu (1957–1958), Hong Kong flu (1968–1969), and Swine flu (2009-2010) are among the best known. Heuristics have been researched extensively for comprehensive and accessible overviews (see Gigerenzer, 2007; Kahnemann, 2011). The development process started with rather intuitive, experience-based methods. A better understanding of the psychology of risk and uncertainty among individuals and in groups is an area in which Munich Re is collaborating with the academia. Hypothesis 1. We review and extend the economic analysis of risk and uncertainty as it relates to behavior mitigating health shocks. Such a scenario set would ideally cover the entire risk landscape. While it will never be a world model of risks, CARE can complement the existing and well-established quantitative risk management tools. 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. By using a large set of expert knowledge, this helps to avoid idiosyncratic biases, but group effects remain. There are also important effects in groups. Hence we need to search for solutions in these two directions—data and models. 2. The concept of resilience may be a promising way forward. pp 329-344 | How individuals perceive insurances depends on their prices, and on the individuals’ preferences and budget constrain. Our approach is forward looking and focuses on thinkable yet plausible consequences of significant events. 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 . How individuals perceive insurances depends on their prices, and on the individuals’ preferences and budget constrain. This approach is based on the analysis of high-reliability organizations , which cannot afford to fail under uncertainty. The emerging risk process, if designed appropriately, can be regarded as a method to manage uncertainty. Trying to understand the global risk landscape is an active process, during which management options may be detected. Viral infections kill lots of people. 7 Levee Certification. 7. 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. Financial networks have long reached global dimensions. The other major risk category is called price or market risk and is caused by the uncertainty of what the price of the product will be at the time it is produced and ready for sale. Any risk transfer solution with such a long development phase would be too late for the market. How much or how little we know about the events to be insured determines their measurability. Globalized trade flows and financial markets are examples of such structures. Changing the resilience paradigm. The industry has developed practices and methods for risk transfer and risk management. Unexpected consequences from new technologies, for example, artificial intelligence or genetic engineering, are examples for uncertainty. In some cases we have a very accurate idea of the odds of an event happening, such as the McDonalds example above. Coburn, A., Bowman, G., Ruffle, S., Foulser-Piggott, R., Ralph, D., & Tuveson, M. (2014). Prospect theory: An analysis of decision under risk. We also suggested specific settings for different steps in the emerging risk process. Even if the underlying system is not deterministic but of random nature, the mathematical science can offer tools to peek into the future. With these changes arises the need to develop established enterprise risk management practices further. Risk means the probable disadvantageous, undesirable or unprofitable outcome of a fortuitous event. There will be a competitive advantage for organizations, who are capable to deal with both risk and uncertainty. 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. Uncertainty, Risk and Insurance Policy summary Decisions on the most appropriate climate change policies should be proportionate to the risk posed by the impacts of climate change. We realized however, while we cannot simply correct subjective risk estimates directly, we can change the way how we arrive at those estimates. An insurance company o⁄ers you insurance against this eventuality for a premium of 15AC. Not logged in Komplexität handhaben - Handeln vereinheitlichen Organisationen sicher gestalten. Falling stock markets lead to even more sales lead to even lower prices and so on. Quantitative risk management, © Springer International Publishing AG, part of Springer Nature 2018, Psychological Perspectives on Risk and Risk Analysis, http://cambridgeriskframework.com/getdocument/4, https://www.munichre.com/site/corporate/get/documents/mr/assetpool.shared/Documents/0_Corporate%20Website/1_The%20Group/Focus/Emerging%20Risks/2013-09-emerging-risk-discussion-paper-en.pdf, https://www.munichre.com/site/corporate/get/documents_E1286451571/mr/assetpool.shared/Documents/0_Corporate%20Website/1_The%20Group/Focus/Emerging%20Risks/Emerging-Risk-Discussion-Paper-2014-10-en.pdf, https://www.munichre.com/site/corporate/get/documents_E-1170441588/mr/assetpool.shared/Documents/0_Corporate%20Website/1_The%20Group/Focus/Emerging%20Risks/302-07873_en.pdf, http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Stellungnahmen/acatech_STUDIE_RT_WEB.pdf, Munich Reinsurance Company, Integrated Risk Management, https://doi.org/10.1007/978-3-319-92478-6_15, Uncertainty Management and Emerging Risks, University of Lodz (2000495008) - Polish Consortium ICM University of Warsaw (3000169041) - Polish Consortium ICM University of Warsaw (3003616166). See for example Dannenberg et al (2014) . Diversification, that is, spreading the risk and hence balancing the portfolio, in a tightly interconnected risk landscape is difficult. It provides a comprehensive introduction to the analysis of economic decisions under uncertainty and to the role of asymmetric information in contractual … But in a combination with smart algorithms and clever framing of questions, this will provide an added value. Proportionality: The principle of proportionality implies that risk management should focus on significant risks, that is, risks with a potential to have a sustained negative impact on the company. Measurable risks are also the main focus of ERM systems, as they can be modeled, evaluated, and steered. Higher returns come with higher risk. Financial markets, for instance, provide huge amount of data, and many mathematical models are extensively researched and applied for risk management purposes (for an overview, see McNeil, Frey, & Embrechts, 2005). The model of the global risk landscape allows the identification of critical, multi-line risks (top left area). Together with social psychologists, we improved our own understanding of heuristics and their impact on subjective risk estimates significantly. This input from outside was highly relevant and led to the development and implementation of highly sophisticated quantitative models. As has been mentioned earlier, at Munich Re, the emerging risk think tank is the central platform in the process. After several decades of successful applications, the industry starts to realize the limitation of these models. Decisions taken under the conditions of uncertainty are more important than the Risk decisions taken under the conditions of Risk because measurement of alternatives is not possible in case of uncertainty.

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