Description: This newsletter provides a regulation outlook for the use of AI/ML in insurance industry, interpretations of regulatory expectations and what insurers should consider when managing risks associated with the development, implementation and use of AI/ML models/tools.

Description: The present crisis in global financial markets has created an impression that enterprise risk management ERM has failed broadly to protect the safety and soundness of the financial system as well as that of many institutions, including insurance companies.

Description: Crises are devastating. They leave behind a high amount of entropy unemployment, poverty, lack of safety. But this is half the truth. The other half says that crises are to some extend good, because they provide a natural crash test to discover current practices’ risks and limitations, and in response, give rise to more efficient regulation, to financial innovations and to the wakening of the vitalizing powers of society, as the old and corrupted die and new ideas emerge and give us the opportunity to reflect upon the future we want to have.

Description: Arguably, many of the fundamental contributing factors to the 2008 mortgage crisis involved misdirected incentives and misinformation available to key participants in the housing finance process. The incentives discussed here include those of the mortgage holders, mortgage intermediaries, mortgage providers, securitizers, raters and CEOs and other highly paid staff of those involved. Unless similar incentives are recognized, future public policy decisions will again fail to avoid this system-wide risk. Since I am most familiar with the U.S. situation, my comments will be limited accordingly.

Description: This crisis first started with subprime loans and quickly morphed into a credit crisis that eventually engulfed a lot of financial institutions around the world. If a AAA were really a AAA, this crisis would probably have been contained and localized. As the crisis developed, evidence emerged from the shadows that revealed the questionable quality of some AAA-rated MBS or ABS. Subprime loans were the weakest link. As the credit boom came to an end and the economy slowed down further, the Alt-A, prime and credit card loans were also affected. The subprime crisis is only the symptom of a much larger underlying problem. Both consumers and corporations were overleveraged.

Description: The liquidity crisis and the insufficient depth of the market led to a strong correlation crisis: many risks that could be considered as close to mutually independent in the classical regime suddenly became strongly positively dependent. More correlation crisis may happen in the future. We need to be more careful with black-box tools and to train what I would define as “quanlitative analysts” “Quanls” in the Enterprise Risk Management ERM process, that is risk managers who are able to lead interdisciplinary ERM studies from a jointly qualitative and quantitative point of view, with an emphasis on dynamics.

Description: Models are imperfect approximations of reality. Once a bubble has been recognized, that’s the time for mechanisms of caution and prudence to kick in, even in the face of exuberance – especially in the face of exuberance. Liquidity and trust is plentiful until it’s not – it can evaporate with blazing speed and only returns slowly. Risk is contagious, which means every single employee and citizen has a vested interest in taking on the role of chief risk officer.

Description: Modern economies are based on the premise that all the main players look after themselves. The bankruptcy and limited liability laws gave property speculators and banks a put option on society so they could walk away from losses above their capital base, yet enjoy all the gains of such speculation. They acted in what they believed were their own interests. It seems that society – well aware of what was happening – did not effectively look after its own interest and now must pay the price.

Description: The costly, time-consuming and complicated process of enterprise risk management (ERM) can be improved in many companies and made less tedious for managers by using reasonable data and templates obtained from peer group entities. The models used to calculate economic capital (EC) often underestimate its value because they do not consider decision-maker perception about risk. We assume here that managers, as the decision-makers, have appropriate business understanding and they can provide substantial information about risk characteristics regarding all business processes. Therefore we are focused, in this paper, on collecting data from managers across the different businesses to derive the appropriate knowledge about risky events, the importance of particular types of risk, the relationship between the risk outcomes and the level of risk control in a particular industry. We conclude that the collected data has a high potential for use as a benc hmarking reference and analysis for improving ERM models for individual businesses.

Description: Analyzes the need for enterprise risk management for small businesses and the unique principles governing such a discipline. The paper describes a case study done by Goldenson Actuarial Research Center at the University of Connecticut and concludes with some suggestions on how ERM for small businesses can be done on a larger and sustainable scale using student resources supported by large corporations, banks and government organizations.