A relatively new way of utilizing machine learning (chatbots have been around since the 1960’s) has emerged, spurred on by an increase in computational ability (GPUs) and new model architectures (Ian Goodfellow et al. 2014 GANs, Vishwani et al. 2017 Transformers).
This paper presents an overview of punitive damages, extra-contractual obligations (ECOs) and losses in excess of policy limits (XPLs). Our insights cover what they are, how they (adversely) impact loss experience, and how underwriters can seek to mitigate the exposure.
Re/insurers engage in a difficult style of trading with a capped upside (the premium) and a potentially unlimited downside (the loss) delivering asymmetric risk/returns. Machine learning creates optionality that helps rebalance the asymmetry.