Applying Machine Learning to Life Insurance: some knowledge sharing to master it
This work addresses the problem of integrating machine learning into the life insurance industry, which has relied on statistical models, by providing practical tools for insurers to leverage data, though it is incremental as it builds on existing methods.
The paper tackles the challenge of applying machine learning to life insurance by extending traditional actuarial survival models to handle censored data, resulting in a Python library that adapts open-source algorithms for accurate risk modeling.
Machine Learning permeates many industries, which brings new source of benefits for companies. However within the life insurance industry, Machine Learning is not widely used in practice as over the past years statistical models have shown their efficiency for risk assessment. Thus insurers may face difficulties to assess the value of the artificial intelligence. Focusing on the modification of the life insurance industry over time highlights the stake of using Machine Learning for insurers and benefits that it can bring by unleashing data value. This paper reviews traditional actuarial methodologies for survival modeling and extends them with Machine Learning techniques. It points out differences with regular machine learning models and emphasizes importance of specific implementations to face censored data with machine learning models family. In complement to this article, a Python library has been developed. Different open-source Machine Learning algorithms have been adjusted to adapt the specificities of life insurance data, namely censoring and truncation. Such models can be easily applied from this SCOR library to accurately model life insurance risks.