Model Transparency and Interpretability : Survey and Application to the Insurance Industry
This work provides practical tools for ensuring non-discrimination and explainability in insurance models, but it is incremental as it applies existing interpretability methods to a specific domain.
The paper addresses the need for model transparency and interpretability in machine learning, particularly within the insurance industry, by demonstrating how interpretability methods can be applied to control actuarial models and adapt explanations for different audiences, using a simple car insurance loss frequency estimation example.
The use of models, even if efficient, must be accompanied by an understanding at all levels of the process that transforms data (upstream and downstream). Thus, needs increase to define the relationships between individual data and the choice that an algorithm could make based on its analysis (e.g. the recommendation of one product or one promotional offer, or an insurance rate representative of the risk). Model users must ensure that models do not discriminate and that it is also possible to explain their results. This paper introduces the importance of model interpretation and tackles the notion of model transparency. Within an insurance context, it specifically illustrates how some tools can be used to enforce the control of actuarial models that can nowadays leverage on machine learning. On a simple example of loss frequency estimation in car insurance, we show the interest of some interpretability methods to adapt explanation to the target audience.