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Mohammadamin Dashti Moghaddam, Nick Sciarrilli · 2026-06-23
The paper introduces a Transformer-based model (MSFT) that processes multiple separate streams of banking activity — transactions, logins, and risk signals — encoding each stream separately then combining them. Tested on a large dataset (10M users, 1.5% fraud rate), it detects fraud far better than gradient-boosted trees on aggregated features (0.99 vs 0.74 AUROC), and keeping streams separate plus using time-aware encoding gave the best results.
Why it matters: Fraud detection matters mainly to banks, fintech risk teams, and platform operators rather than to portfolio investors directly. It suggests that sequence-aware, multi-stream deep learning can meaningfully improve anti-fraud systems, which could be relevant when assessing operational risk or technology at financial firms.
⚠ This is an operational fraud-detection model evaluated on (partly proprietary) datasets, not a trading or investment strategy, and results depend heavily on data access and engineering resources most investors lack.
Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns. We propose the Multi-Stream Fraud Transformer (MSFT), a unified architecture that encodes each event stream with independent Transformer encoders and fuses their representations through configurable mechanisms. We conduct a systematic ablation study comparing five fusion strategies: concatenation, gated fusion, time-aware positional encoding, cross-stream attention, and a full combination. On a large-scale dataset (10M users, 1.5% fraud rate) with 85M parameter models, we demonstrate that (1) sequence models significantly outperform gradient-boosted trees operating on aggregated features (0.74 vs. 0.99 AUROC), (2) per-stream encoding is essential -- a single-stream Transformer baseline with matched parameter budget reaches only 0.82 AUROC, an 18-point gap that confirms the multi-stream inductive bias is necessary, (3) time-aware positional encoding achieves the highest discrimination (0.9961 AUROC), (4) gated fusion yields the best precision (0.989) suitable for production deployment, and (5) the risk event stream provides the strongest individual signal contribution. We further validate on proprietary production data from a digital banking platform, showing over 22% relative AUROC improvement over the XGBoost baseline.
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AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.