Article written in collaboration with the law firm GALEA associés.
The car insurance market is now at a turning point. In this already saturated market, offering few margins and waiting for autonomous vehicles, the development of new players is increasing competition. In addition, the Hamon law (facilitating the change of insurer) and the growth of pay-as-you-drive offers are making it increasingly difficult to retain customers and good risks in particular.
In the coming years, motor insurers will therefore continue to refine their ability to individualise their rates, while respecting the principle of risk pooling, which is the basis of insurance, as much as possible. Those who succeed in making each policyholder pay his or her “fair price” will be able to build loyalty among their members while maintaining technical balance. Conversely, less adapted rates will lead to more and more anti-selection. The pricing process therefore appears to be the main lever for technical excellence.
As part of their work, Galea’s consultants conducted a study to test two ways of improving premium calculation:
Classically, premium calculation is based on a generalised linear model (GLM). The first idea is to compare the results obtained by this model with those from data science approaches. Do these different machine learning models (such as CART, Random Forest or XGBoost) improve predictions and refine pricing criteria? The model is enriched by the contribution of new external data, notably from telematics provided by our partner Ellis-Car. Does the integration of these data make it possible to isolate specific behaviours that the historical data available to insurers do not detect? This study was conducted in partnership with Ellis-Car, which offers a solution for vehicle fleets and private individuals that combines on-board telematics, training and profitability.
The start-up offers a geolocation and driving profiling solution using a simple smartphone for corporate fleets. Developed in the academic world, many times rewarded and finely tuned by hundreds of millions of driving kilometres, the self-learning algorithms proposed by the startup are capable of detecting in real time any deviation in driving behaviour in relation to all drivers. A system of voice and visual alerts makes it possible to modify driver behaviour in a very significant and beneficial way for the company. These improvements in driving behaviour are also sustainable thanks to the gamification of the user experience.
The Ellis-Car algorithm is based on a set of several cartographic layers, which are fed by numerous Open Data data: weather, traffic, road visibility, road signs, accident history, etc. These layers are also enriched by any journey made by a driver, with the aim of being able to compare driving behaviour with the entire knowledge base and estimate the risk.