The chair develops research activities on insurance models and data analytics for insurance.
The research about models for insurance focuses on the impact of the regulatory and accounting environment on the development and management of insurance models. The question of measuring risks and performance is central to an insurance company being properly managed and is an important part of our research. Our aim is to define measures that are representative of the insurance ecosystem using relevant quantitative indicators so that decisions can be made by executive boards. Other topics that we are interested in are: the governance of internal models and attitudes of top management with respect to models, the use of proxies, model points and advanced simulation techniques for risk management.
The question of modelling policyholder behaviour is also of interest for us. Policyholder behaviour is one of the most difficult factors of risk and profit for an insurance company to study. This is due to the lack of baseline insurance data in a context of sudden leaps in rates (which could cause a wave of lapses) and the fact that it is a risk comprising a human element.
Data analytics in insurance research examines insurance sector changes through the use of new algorithms and data. The arrival of mass data with technology to store and analyse is now a reality for financial institutions and, in particular, insurance companies.
It is not just the regulatory and accounting framework that is changing in finance and in insurance. There are social networks and objects connectedor linked to new forms of technology that are going to profoundly change consumption patterns and relations
between insurers and policyholders. They might even alter the value chain in the insurance sector too.
Some topics that we study are:
– Governance for data analytics, new business models with Big Data and Analytics
– Risk-based pricing, Predictive analytics/Machine & Deep learning,
– Internet of things, connected ecosystems, and new opportunities for insurers
– Privacy concerns, Data anonymization, Open data.
Both subjects are more linked than one might think after a quick reading and there are several topics that are strongly connected. Let us give a short example. It is important to look at what data analytics techniques can offer in understanding the policyholder behaviour, but also reciprocally, to understand the effect of using data analytics methods on policyholder behaviour in a new era in which the use of personal data is potentially perceived by policyholders as invasive in their private lives.