Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908885662564352 |
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| author | Roth, Holger R. Tickoo, Sarthak Kumar, Mayank Yang, Isaac Liu, Andrew Varshney, Amit Kundu, Sayani Vintila, Iustina Madsgaard, Peter Milcak, Juraj Chen, Chester Cheng, Yan Feng, Andrew Savio, Jeff Singh, Vikram Stancill, Craig Wan, Gloria Powell, Evan Haq, Anwar Ul Upadhyay, Sudhir Lee, Jisoo |
| author_facet | Roth, Holger R. Tickoo, Sarthak Kumar, Mayank Yang, Isaac Liu, Andrew Varshney, Amit Kundu, Sayani Vintila, Iustina Madsgaard, Peter Milcak, Juraj Chen, Chester Cheng, Yan Feng, Andrew Savio, Jeff Singh, Vikram Stancill, Craig Wan, Gloria Powell, Evan Haq, Anwar Ul Upadhyay, Sudhir Lee, Jisoo |
| contents | Fraud-related financial losses continue to rise, while regulatory, privacy, and data-sovereignty constraints increasingly limit the feasibility of centralized fraud detection systems. Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative model training across institutions without sharing raw transaction data. Yet, its practical effectiveness under realistic, non-IID financial data distributions remains insufficiently validated. In this work, we present a multi-institution, industry-oriented proof-of-concept study evaluating federated anomaly detection for payment transactions using the NVIDIA FLARE framework. We simulate a realistic federation of heterogeneous financial institutions, each observing distinct fraud typologies and operating under strict data isolation. Using a deep neural network trained via federated averaging (FedAvg), we demonstrate that federated models achieve a mean F1-score of 0.903 - substantially outperforming locally trained models (0.643) and closely approaching centralized training performance (0.925), while preserving full data sovereignty. We further analyze convergence behavior, showing that strong performance is achieved within 10 federated communication rounds, highlighting the operational viability of FL in latency- and cost-sensitive financial environments. To support deployment in regulated settings, we evaluate model interpretability using Shapley-based feature attribution and confirm that federated models rely on semantically coherent, domain-relevant decision signals. Finally, we incorporate sample-level differential privacy via DP-SGD and demonstrate favorable privacy-utility trade-offs... |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_13617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE Roth, Holger R. Tickoo, Sarthak Kumar, Mayank Yang, Isaac Liu, Andrew Varshney, Amit Kundu, Sayani Vintila, Iustina Madsgaard, Peter Milcak, Juraj Chen, Chester Cheng, Yan Feng, Andrew Savio, Jeff Singh, Vikram Stancill, Craig Wan, Gloria Powell, Evan Haq, Anwar Ul Upadhyay, Sudhir Lee, Jisoo Machine Learning Computational Engineering, Finance, and Science Fraud-related financial losses continue to rise, while regulatory, privacy, and data-sovereignty constraints increasingly limit the feasibility of centralized fraud detection systems. Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative model training across institutions without sharing raw transaction data. Yet, its practical effectiveness under realistic, non-IID financial data distributions remains insufficiently validated. In this work, we present a multi-institution, industry-oriented proof-of-concept study evaluating federated anomaly detection for payment transactions using the NVIDIA FLARE framework. We simulate a realistic federation of heterogeneous financial institutions, each observing distinct fraud typologies and operating under strict data isolation. Using a deep neural network trained via federated averaging (FedAvg), we demonstrate that federated models achieve a mean F1-score of 0.903 - substantially outperforming locally trained models (0.643) and closely approaching centralized training performance (0.925), while preserving full data sovereignty. We further analyze convergence behavior, showing that strong performance is achieved within 10 federated communication rounds, highlighting the operational viability of FL in latency- and cost-sensitive financial environments. To support deployment in regulated settings, we evaluate model interpretability using Shapley-based feature attribution and confirm that federated models rely on semantically coherent, domain-relevant decision signals. Finally, we incorporate sample-level differential privacy via DP-SGD and demonstrate favorable privacy-utility trade-offs... |
| title | Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2603.13617 |