Today, electronic payments by credit card have become widespread on the planet thanks to the Internet. These payments represent an ever-increasing volume of more than US$500 billion annually, and fraud accounts for less than 0.5% of the number of these transactions. Globally, this represents an estimated US$25 billion in losses per year and this loss is increasing.
Irrespective of the amount of this fraud, all possible detection measures must of course be implemented to limit its spread. This is why a great deal of research has been carried out on this problem over the last 20 years.
As the publisher of a high-performance transactional platform for payment systems, LUSIS is therefore primarily interested in fraud countermeasures. Within the framework of the LUSIS chair, we wish to study fraud detection both from the point of view of algorithm performance, but also with a constraint of realism of implementation and this on real data.
From a technical point of view, the difficulties and locks are at different levels :
This project explores the use of anomaly detection techniques, such as One-Class SVM, to identify the conditions under which algorithmic trading strategies are likely to succeed, ensuring their applicability and extending the approach to portfolio-wide strategy optimization.
This project develops a hybrid fraud detection model by combining graph-based learning and time-series analysis to capture complex transaction relationships, aiming to enhance fraud prediction accuracy while minimizing false positives in large-scale payment datasets.
The Junior Conference on DataScience and Engeneering 2022 (JDSE) took place on september 15-16, on the Polytechnique campus (Palaiseau).
Hugo Thimonier has the opportunity to present “TracInAD: Measuring Influence for Anomaly Detection” to the audience.
Marc Velay won the best poster contest with his poster about “Robustness Analysis of Deep RL for Portfolio Selection”.
Hopular applied to Fraud Detection.
Inverse Reinforcement Learning for portfolio allocation.
Constructing probabilistic rule list.
Constructing probabilistic rule list.
Constructing probabilistic rule list.
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