Automatic fraud detection is a very specific domain of statistical modeling. Unlike anomaly detection in the industrial domain for example, it aims at detecting fraudulent transactions resulting from rational agent behavior. Therefore, the detection of fraud cases requires not only skills in data science and econometrics (knowledge of statistical models and their properties), but also economic and legal skills to understand the motivations and strategic behaviors of fraudsters. The motivations of the “Data Science and Fraud Detection in Insurance” IoR are mainly related to (i) the modeling of the strategic behavior of fraudsters in the insurance domain; (ii) the need to set up efficient detection systems given the huge financial losses linked to fraud; (iii) the exploitation of new databases allowing to identify the mechanisms of insurance fraud.
The project is organized around several actions:
a) Active research in the field of insurance fraud detection which includes:
b) The establishment of a program of visiting researchers and a cycle of bimonthly seminars.
c) The promotion and dissemination of research through publications in peer-reviewed journals.
d) The training of data scientists in the specific problems of fraud detection in insurance.
e) The recruitment of a doctoral student who will work on the research themes of the research initiative.