A multi-task network approach for calculating discrimination-free insurance prices

TL;DR

This paper introduces a multi-task neural network approach for insurance pricing that effectively prevents proxy discrimination even with incomplete protected characteristic data, maintaining accuracy and improving fairness.

Contribution

The paper presents a novel multi-task neural network architecture that estimates insurance prices without requiring full protected characteristic data, addressing proxy discrimination issues.

Findings

Comparable predictive accuracy to conventional models with full data

Superior performance with partial protected characteristic data

Effectively reduces proxy discrimination in insurance pricing

Abstract

In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.