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Machine Learning permeates many industries, which brings new source ofbenefits for companies. However within the life insurance industry, MachineLearning is not widely used in practice as over the past years statisticalmodels have shown their efficiency for risk assessment. Thus insurers may facedifficulties to assess the value of the artificial intelligence. Focusing onthe modification of the life insurance industry over time highlights the stakeof using Machine Learning for insurers and benefits that it can bring byunleashing data value. This paper reviews traditional actuarial methodologiesfor survival modeling and extends them with Machine Learning techniques. Itpoints out differences with regular machine learning models and emphasizesimportance of specific implementations to face censored data with machinelearning models family. In complement to this article, a Python library hasbeen developed. Different open-source Machine Learning algorithms have beenadjusted to adapt the specificities of life insurance data, namely censoringand truncation. Such models can be easily applied from this SCOR library toaccurately model life insurance risks.

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