# Applying Machine Learning to Life Insurance: some knowledge sharing to master it

**Authors:** Antoine Chancel, Laura Bradier, Antoine Ly, Razvan Ionescu, Laurene Martin, Marguerite Sauce  
**arXiv:** [2209.02057](https://arxiv.org/abs/2209.02057)  
**Date:** 2022-09-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.02057v3/

## Key points

- Machine Learning (ML) is increasingly used in many industries, but the life insurance industry has been slow to adopt it.
- Traditional statistical models for risk assessment are still efficient, which makes insurers reluctant to use ML.
- A group of authors published a paper titled "Applying Machine Learning to Life Insurance: some knowledge sharing to master it" that reviews traditional actuarial methodologies for survival modeling and extends them with ML techniques.
- The paper emphasizes the importance of specific implementations to deal with censored data using machine learning models family.
- A Python library has been developed that adjusts different open-source ML algorithms to adapt to the specificities of life insurance data such as censoring and truncation.
- By incorporating ML into their practices, insurers can unlock valuable insights from their data sets and improve risk assessments.
- The implementation of these techniques could lead to more accurate predictions about policyholder mortality rates and better pricing strategies for policies.

## Summary

The use of Machine Learning (ML) has become increasingly prevalent in many industries, providing companies with new sources of benefits. However, the life insurance industry has been slow to adopt ML due to the efficiency of traditional statistical models for risk assessment. This reluctance may lead insurers to face difficulties in assessing the value of artificial intelligence. The evolution of the life insurance industry over time highlights the potential benefits that ML can bring by unlocking data value. To address this issue, a group of authors including Antoine Chancel, Laura Bradier, Antoine Ly, Razvan Ionescu, Laurene Martin and Marguerite Sauce have published a paper titled "Applying Machine Learning to Life Insurance: some knowledge sharing to master it". The paper reviews traditional actuarial methodologies for survival modeling and extends them with ML techniques. It points out differences between regular machine learning models and emphasizes the importance of specific implementations to deal with censored data using machine learning models family. In addition to the article, a Python library has been developed that adjusts different open-source ML algorithms to adapt to the specificities of life insurance data such as censoring and truncation. These models can be easily applied from this SCOR library to accurately model life insurance risks. The authors suggest that by incorporating ML into their practices, insurers can unlock valuable insights from their data sets and improve risk assessments. The implementation of these techniques could lead to more accurate predictions about policyholder mortality rates and better pricing strategies for policies. Overall, this paper provides an insightful guide for insurers looking to incorporate ML into their operations while highlighting its potential benefits in improving risk assessments within the life insurance industry.