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

arXiv: https://arxiv.org/abs/2207.02799 · HTML: https://mlsanity.com/paper/2207.02799/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [A Fair Pricing Model via Adversarial Learning](https://mlsanity.com/paper/2202.12008/) (2022)
- [How to ask sensitive multiple choice questions](https://mlsanity.com/paper/1803.10568/) (2018)

## Built on by (most cited first, 4 of 4)

- [Enhancing Actuarial Non-Life Pricing Models via Transformers](https://mlsanity.com/paper/2311.07597/) (2023)
- [A Discussion of Discrimination and Fairness in Insurance Pricing](https://mlsanity.com/paper/2209.00858/) (2022)
- [Discrimination-free Insurance Pricing with Privatized Sensitive Attributes](https://mlsanity.com/paper/2504.11775/) (2025)
- [α-Fair Insurance Pricing: A Fairness Continuum](https://mlsanity.com/paper/2606.14898/) (2026)

## Abstract (arXiv)

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.