Machine learning techniques are now more and more popular in the insurance industry and have a lot of applications (pricing, reserving, claims management, underwriting). Whereas the advanced techniques (e.g. random forest or neural networks) usually have a better predictive power than statistical models (e.g. Generalized Linear Models), their main drawback is that they are black-box and their results are difficult to understand/interpret which doesn’t always provide sufficient comfort to take business decisions.
In this webinar, we introduce some model interpretability tools and describe how they can be used to boost insights from data in insurance applications (thanks to adequate features selection, features engineering and results interpretation).
These interpretability tools make the use of machine learning techniques much more relevant in insurance as it allows to improve the predictive power while understanding the drivers of the results; which is fundamental to take relevant business decisions.