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Synthetic Data
Anti-Money Laundering (AML)
Money laundering is the process of introducing money coming from illegal activities into the financial system in order to use it for legal or illegal purposes. This data represents sequence of high level interactions, with a financial institution, of legitimate clients and clients that are engaged in money laundering activities. The current data contains state and action pairs of bank customer related activities. Examples are opening an account, making transactions, payments, withdrawals, purchases etc. The data was generated by running an AI planning-execution simulator.
Sample AML Trace data
References
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. Simulating and classifying behavior in adversarial environments based on action-state traces: An application to money laundering, D Borrajo, M Veloso, S Shah. Proceedings of the 1st International Conference on AI in Finance (ICAIF), 2020. Also in arXiv preprint arXiv:2011.01826, 2020
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