Data Science and Fraud Detection in Insurance

Scientific project

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:

  • A methodological reflection on insurance fraud detection algorithms;
  • Optimization of the processing of positive signals (suspected fraud) emitted by the model and sizing in relation to processing and control capacities;
  • A reflection on the stability and governance of fraud detection models;
  • The development of a typology of fraudsters and fraud in non-life insurance;
  • A reflection on the links with other risk modelling in finance (credit risk, for example)

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.

 

Scientific officers

Denisa Banulescu-Radu
Denisa Banulescu-Radu
See CV

Partenaires académiques

Partenaires économiques