Postdoctoral Researcher in Data Science & Machine Learning for Financial Fraud Detection

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Presentation of the project : . Financial fraud generates major costs and evolves constantly with digitalisation and new payment channels. This project develops interpretable and operationally relevant methods to detect fraud in insurance, social contributions and banking. The research uses large, heterogeneous and often sensitive datasets to identify anomalous behaviour, suspicious networks and emerging fraud patterns while controlling false positives and preserving model interpretability. Work will cover modelling under class imbalance, (semi-)supervised and cost-sensitive learning, network and graph methods to uncover coordinated schemes, and the development of explainable and robust models suited to regulated environments. Particular attention will be paid to adaptive strategies as fraud techniques evolve and to temporal dynamics. Presentation of the Service / laboratory : . The postdoctoral researcher will join the Laboratoire d'Économie d'Orléans (LEO) and collaborate with academic and institutional partners. The environment supports quality research, with access to high-performance computing, proprietary fraud datasets, and a multidisciplinary team. The laboratory supports scientific publication and international conference participation. Attention will be paid to data confidentiality, responsible use and research reproducibility. Presentation of the missions : . We seek a po