Abstract
In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size.
Highlights
Key business goals for claims models typically include predictive power, automation and ease of use, and interpretability
In actuarial modelling of risk pricing and loss reserving in general insurance, known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning
Predictive power is valuable in any model, but for risk pricing in insurance, higher accuracy leads to selecting lower cost risks and is a competitive advantage
Summary
Key business goals for claims models typically include predictive power, automation and ease of use, and interpretability. Machine learning approaches allow automated identification and fitting of non-linear effects These can contribute to a higher accuracy than GLM approaches, at the cost of transparency. Where there has been a significant mix shift in the risks insured—perhaps due to growth or shrinkage in particular segments—the portfolio approach breaks down and input from a granular view (often the pricing view) is needed. This suggests that a granular approach to reserving incorporating detailed policy or claims data should be able to model segmented results with better accuracy than simple allocation methods of portfolio Incurred But Not Reported (IBNR) reserves
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