Beyond Yes or No: Making Reliable Decisions and improving personalized customer service

This white paper explores how uncertainty tools can be used to improve personalized customer service.
Uncertainty is inherent in any machine learning predictive model. There are no perfect models, partly due to the curse of dimensionality and the challenges of avoiding any biases and misclassifications.

We aim to demonstrate how an insurance company can benefit from the uncertainty of machine learning predictions in order to develop methods that allow for the allocation of an uncertainty parameter to the predictions provided for a given profile/customer x.
The benefits of scrutinizing uncertainty are numerous and often aligned with customer interests:
1. It can help to appreciate the weak points of a predictive model and thus improve them.
2. It enables the definition of the Next Best Action (NBA) with a full understanding of the facts.
3. It facilitates the analysis of marketing actions' results by providing a deeper appreciation of the heterogeneity within portfolios.
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This white paper, therefore, delves into the benefits of understanding uncertainty, its applications, and practical considerations for end customers.
All illustrations and results presented in this paper are derived from an internal Ethias dataset.
We will also explore how the uncertainty measures discussed in this paper (Epistemic vs Aleatoric, Conformal) can be useful in managing the uncertainty of Large language models (LLMs) and their propensity to hallucinate.