Believing the bot – model risk and deep learning | R Richman | CERA Global Risk Conference 2021



Published
Deep Learning models are currently being introduced into business processes to support decision-making in insurance companies. At the same time model risk is recognized as an increasingly relevant field within the management of operational risk that tries to mitigate the risk of poor business decisions because of flawed models or inappropriate model use. In this paper we try to determine how Deep Learning models are different from established actuarial models currently in use in insurance companies and how these differences might necessitate changes in the model risk management framework. We analyse operational risk in the development and implementation of Deep Learning models using examples from pricing and mortality forecasting to illustrate specific model risks and controls to mitigate those risks. We discuss changes in model governance and the role that model risk managers could play in providing assurance on the appropriate use of Deep Learning models.

Ronald Richman is a Fellow of the Institute of Actuaries with extensive experience in Life and General Insurance practice, focusing on reserving, capital modelling and ERM. Over the course of his career he has gained a deep understanding of actuarial techniques through exposure to international best practice.

The CERA Global Risk Conference was held in June 2021 and brought together high quality and topical presentations covering the risk management spectrum. Sponsored by Milliman and RGA, the CERA Global Association was delighted to offer this conference free of charge, so that CERAs and actuaries with an interest in Risk Management could gain new insights and pick up some valuable CPD. All of the presentations from this conference are available on our actuview page: https://www.actuview.com/partner/cera_global

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Category
Management
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