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Interpretability of Complex Models

Understanding and interpreting the results of complex models is essential to avoid a black-box effect. Sia Partners supports its clients in the development of interpative methods and adapted tools.

Given the boom in the volume of data available, insurers must innovate and implement new methods of data processing. The latter aim to improve risk assessment and quantification while allowing market players to adapt their products offering to increasingly demanding customers.

However, if these methods allow an improvement in predictive performance, this is generally obtained at the expense of interpretability. Aware of the importance of this concept, the Protection & Social Care team decided to develop studies responding to the need for transparency on the choice and use of complex algorithms.

Thus, to provide solutions adapted to our Customers, Sia Partners offers to leverage the tools offered by Machine Learning, by identifying new risk factors, integrating external data, or even limiting the number of pricing variables to simplify the processes for example. There are many interpretability solutions which must be selected according to the underlying models:

The tools used are mainly dependent on the objective sought, the client and the business constraints of the company. Thus, the solutions retained and the tools developed intend to be adapted to the problems encountered by our customers.

A source of value that insurers have not much explored

It is now important for insurers to analyze the risks and opportunities of the new digital context to define an overall strategy, proving to be consistent with changes in the sector and the needs of society. This improvement in risk assessment and quantification capacities is found on several pillars:

Customer experience: products and services customization, customer segmentation, identification of expectations, ...

Business processes: reduction in processing time for customer requests, automation of repetitive tasks with low added value, etc.

Optimization of the pricing process: analysis of policyholder behavior, use of external data (open-data, data-capture), the study of customer value, etc.

Prevention: quantification/optimization of a prevention plan, in particular according to behavioral analyzes, prioritization/calibration of prevention needs, ...

This way, our internal work makes it possible to enhance our services using methods similar to Artificial Intelligence to ensure perfect interpretability of the models developed for our clients.