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.
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