Long-term Life Insurance valuations meet deep learning

Abstract

Traditional actuarial methods for valuing insurance portfolios, while established, are often time consuming, complex, and prone to manual error. This study investigates the potential of machine learning techniques to enhance and streamline these traditional methods, offering improved efficiency and accuracy.

Utilising a dataset from a commercial European life insurance company, this research designs and implements a deep neural network to predict policy reserve values. A combination of actuarial pricing and valuation bases is employed to prepare the data for training and evaluation, focusing on developing models capable of accurately predicting expected present values.

The results demonstrate that machine learning models can effectively predict policy reserve values, providing valuations comparable to those obtained through traditional actuarial methods. Notably, the developed ”Midway” model consistently predicts accurate and efficient reserve estimates. These models demonstrate an ability to capture complex relationships between inputs and policy reserve values, even with combinations of previously unseen data within valid ranges.

This research has significant implications for the insurance industry, offering the potential for improved efficiency, enhanced risk management, and more informed decision-making. The ability to rapidly and accurately value large policy portfolios can lead to improved pricing strategies and investment decisions. Furthermore, machine learning techniques can reduce the time and resources required for traditional valuations and provide an independent check on accuracy for auditors and regulators.

Beyond its practical applications, this study contributes to the machine learning community by demonstrating a novel combination of methods for fitting supervised regression models and establishing a framework for data preparation, model training, and validation. This work provides valuable guidance for future research in applying machine learning to diverse categories of life insurance products.

In conclusion, this study provides a compelling proof-of-concept for leveraging machine learning techniques to value a commercial book of life insurance policies, highlighting the potential for significant improvements in risk management within the insurance industry.