Proxy Modelling and Use of Management Actions  Two areas we have reviewed recently are proxy modelling and use of management actions in internal models. I mentioned earlier that life insurers build proxy models as an approximation of more complex valuation models. They use them to calculate and rank losses under a very large number of one-year scenarios, to create a distribution of possible losses, to identify the 99.5th percentile loss and thus to calculate the regulatory capital requirement. If an insurer calibrates a proxy model poorly, regulatory capital will be wrong, even if the underlying valuation models are broadly correct. Weaknesses could also lead to bad decisions in other contexts where insurers use proxy models, including capital allocation, pricing and risk management. For example, insurers often employ proxy models for stress and scenario testing. In May 2018, the PRA issued a survey to a number of life insurers with a proxy model. The responses revealed that over the previous couple of years, some insurers had improved the quality of their proxy modelling considerably. Others had not made the same investment. Standards of validation also varied. The best firms had increased the number of validation tests by improving the speed of valuation models, were placing the validation tests at points carefully selected to challenge the proxy model calibration and were conducting more validation after the reporting date. Another area that we have reviewed recently is what future management actions insurers assume when calculating their best estimate of reserves and internal model capital.

One example is real time information about customer behaviour taken from computing devices embedded in everyday objects, such as mobile phones, home appliances and cars – the so-called internet of things. Together with digital distribution of insurance, these changes bring huge opportunities. For example, better information may bring improved pricing, wider availability of insurance, more tailored and relevant products, enhanced fraud detection and better customer service. At the same time, they are raising new questions and risks. Many are ethical and conduct related. For example, when designing a pricing algorithm what characteristics and behaviours of people is it acceptable to include? How transparent do insurers need to be to their customers about these algorithms? Where is the boundary between collecting data on behaviour to improve pricing and unacceptable interference in privacy? Is it acceptable to charge a customer more because their behaviour suggests they might be willing to pay more? All these questions highlight the growing importance of managing model risk, including these ethical issues. The US Federal Reserve System and Office of the Comptroller of the Currency published the seminal guidance on model risk management in 2011.4 Although targeted at banks, most of it is also relevant to any organisation making extensive use of models. Its recommendations cover model development, implementation and use; independent model validation, comprising evaluation of conceptual soundness, ongoing monitoring and backtesting; and effective governance, policies and controls, including the importance of a comprehensive model inventory and good documentation.