Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation

• URL: http://arxiv.org/abs/2512.21866v1
• Date: Fri, 26 Dec 2025 05:00:35 GMT
• Title: Secure and Explainable Fraud Detection in Finance via Hierarchical Multi-source Dataset Distillation
• Authors: Yiming Qian, Thorsten Neumann, Xueyining Huang, David Hardoon, Fei Gao, Yong Liu, Siow Mong Rick Goh,
• Abstract summary: A trained random forest is converted into transparent, axis-aligned rule regions.<n>Synthetic transactions are generated by uniformly sampling within each region.<n>This produces a compact, auditable surrogate dataset.
• Score: 17.90471000973834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an explainable, privacy-preserving dataset distillation framework for collaborative financial fraud detection. A trained random forest is converted into transparent, axis-aligned rule regions (leaf hyperrectangles), and synthetic transactions are generated by uniformly sampling within each region. This produces a compact, auditable surrogate dataset that preserves local feature interactions without exposing sensitive original records. The rule regions also support explainability: aggregated rule statistics (for example, support and lift) describe global patterns, while assigning each case to its generating region gives concise human-readable rationales and calibrated uncertainty based on tree-vote disagreement. On the IEEE-CIS fraud dataset (590k transactions across three institution-like clusters), distilled datasets reduce data volume by 85% to 93% (often under 15% of the original) while maintaining competitive precision and micro-F1, with only a modest AUC drop. Sharing and augmenting with synthesized data across institutions improves cross-cluster precision, recall, and AUC. Real vs. synthesized structure remains highly similar (over 93% by nearest-neighbor cosine analysis). Membership-inference attacks perform at chance level (about 0.50) when distinguishing training from hold-out records, suggesting low memorization risk. Removing high-uncertainty synthetic points using disagreement scores further boosts AUC (up to 0.687) and improves calibration. Sensitivity tests show weak dependence on the distillation ratio (AUC about 0.641 to 0.645 from 6% to 60%). Overall, tree-region distillation enables trustworthy, deployable fraud analytics with interpretable global rules, per-case rationales with quantified uncertainty, and strong privacy properties suitable for multi-institution settings and regulatory audit.

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