Please update your browser.
Payments data for Fraud Detection
Data representing transactions from a subject-centric view with the goal of identifying fraudulent transaction. This data contains a large variety of transaction types representing normal activities as well as abnormal/fraudulent activities that are introduced with predefined probabilities. The data was generated by running an AI planning-execution simulator and translating the output planning traces into tabular format. Parameters of the data generation model include the number of clients, time duration and probabilities of fraud.
1. Generating Synthetic Data in Finance: Opportunities, challenges and pitfalls. S Assefa, D Dervovic, M Mahfouz, R Tillman, P Reddy, T Balch and M Veloso. Proceedings of the 1st International Conference on AI in Finance (ICAIF), 2020. Also in NeurIPS 2019 Workshop on AI in Financial Services
2. Domain-independent generation and classification of behavior traces. D Borrajo and M Veloso. arXiv preprint arXiv:2011.02918.
Would you like to know more about AI Research at J.P. Morgan?
You're now leaving J.P. Morgan
J.P. Morgan’s website and/or mobile terms, privacy and security policies don’t apply to the site or app you're about to visit. Please review its terms, privacy and security policies to see how they apply to you. J.P. Morgan isn’t responsible for (and doesn’t provide) any products, services or content at this third-party site or app, except for products and services that explicitly carry the J.P. Morgan name.