Machine learning of surrender: Optimality and humanity

Abstract

We develop a novel machine learning (ML) framework to estimate a surrender charge for variable annuities (VAs) with the balance between human behavior and rational optimality. Optimality accounts for insurers' potential losses from strategic surrenders by policyholders who attempt to take advantage of the market situation. However, policyholders sometimes need to surrender a VA because of sudden personal financial distress or a terminal illness. The literature contains contributions for these two surrender decisions separately, but we consider them simultaneously using ML. The ML framework is a Bayesian mixture of a deep optimal stopping rule based on potentially high-dimensional financial variables and a statistical model with historical data. This framework can help insurers and pension funds to set surrender charges and perform stress testing in ways that balance profits and social responsibility by incorporating policyholders' behavioral data.