PhD thesis
Call for PhD fraud detection.pdfWe are delighted to announce that the chair has facilitated the recruitment of a doctoral student to delve into topics related to insurance fraud detection. Following a recruitment campaign held in the summer of 2021, Yannick Kougblenou was selected for this position (please refer to the Call for Applications for further details).
His doctoral thesis, fully funded by the chair, aims to introduce innovative technical models with strong predictive capabilities for detecting insurance fraud, while also prioritizing interpretability.
Start date: October 1st, 2021
Supervisors: D. Banulescu-Radu (50%), C. Hurlin (25%), B. Baesens (25%)
Invited researchers
Full professor of Big Data & Analytics at KU Leuven (Belgium) and an international expert in the field of fraud detection.
Bart has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics.
Visiting period: December 2022
Full professor of Statistics and Data Science in the Department of Mathematics of University of Antwerp (Belgium).
His research is situated in the field of statistical data science and is driven by real-life business applications such as fraud, non-life insurance and financial risk management.
Visiting period: December 2022
Participation to national/international conferences
IFABS, International Finance and Banking Society Conference, Oxford, 24-26 July 2023
CaLiBank Workshop, LAPE, Limoges, 7 June 2023
AFSE, 71st Congress of the French Economic Association, Paris, 14-16 June 2023
FEBS, 12th International Conference of the Financial Engineering and Banking Society, Crete, Greece, 1-4 June 2023
21st Journée d’Econométrie Développements récents de l’économétrie appliquée à la finance, Paris, 16 November, 2022
38th International Conference of the French Finance Association (AFFI), Saint-Malo, France, 23-25 May 2022
14th Journées de Méthodologie Statistique de l’Insee, Paris, France, 29-31 March, 2022
Articles associated to the project
Banulescu-Radu, D., Kougblenou, Y., (2023). Data science for insurance fraud detection: a review. Forthcoming in Handbook of Insurance, Springer
Banulescu-Radu, D., Yankol-Schalck, M., (2023). Practical guideline to efficiently detect insurance fraud in the era of machine learning: a household insurance case. Journal of Risk and Insurance. Online version, November 2023
Banulescu-Radu, G. D., Bénoît, S., Hurlin, C., (2023). Shortfall in Tax Revenue: Evaluating the Social Security Contribution Fraud. Working paper.
Banulescu-Radu, D., Hansen, P.R., Huang, Z., Matei, M., (2023). Volatility during the financial crisis through the lens of high frequency data: a Realized GARCH approach. R&R in Journal of Financial Econometrics.
Baesens, B., Banulescu-Radu, D., Hurlin, C., Kougblenou, Y., Verdonck, T., (2023). Benchmarking state-of-the-art resampling techniques for classification models: do optimal ratios exist? Working paper.
Kougblenou, Y., (2023). Omission errors in binary classification tasks: impact and mitigation. Working paper.
Banulescu-Radu, D., Kougblenou, Y., (2023). Machine Learning and cost sensitive learning for insurance fraud detection. Ongoing project
Coté, O., Coté, MP., Charpentier, A., (2024). A Fair price to pay: exploiting causal graphs for fairness in insurance. Working paper (winner of the 1st Call for papers).
Pei, J., Lu, Y., (2024). Forecasting natural disaster frequencies using nonstationary count time series models. Working paper (winner of the 2nd Call for papers).
Dotta, M., Milhaud, X., Pommeret, D., (2024). Copulas based fraud detection. Ongoing project (winner of the 2nd Call for papers).