Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML
arXiv SecurityArchived Apr 27, 2026✓ Full text saved
arXiv:2604.22096v1 Announce Type: new Abstract: In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world incidents show that fraud often persists not because detection algorithms fail, but because the audit trail itself is controllable by privileged operators. This exposes a fundamental trust gap: *who audits the auditor?* We present a tamper-evident fraud detection system that anchors both ML prediction
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Computer Science > Cryptography and Security
[Submitted on 23 Apr 2026]
Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML
Zhaohui Wang
In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world incidents show that fraud often persists not because detection algorithms fail, but because the audit trail itself is controllable by privileged operators. This exposes a fundamental trust gap: *who audits the auditor?*
We present a tamper-evident fraud detection system that anchors both ML predictions and workflow execution to an immutable blockchain ledger. Rather than using blockchain as passive storage, we enforce the entire approval process through smart contracts, ensuring that every transaction, prediction, and explanation is atomically recorded and cannot be retroactively modified. Our detection module achieves competitive accuracy (F1 = 0.895, PR-AUC = 0.974) while providing cryptographically verifiable decision trails that support regulatory auditability requirements (e.g., GDPR Article 22). System evaluation shows sub-25 ms inference latency and economically viable deployment on Layer-2 networks at under $0.01 per transaction (validated against PolygonScan data), supporting enterprise-scale workloads of 10,000+ monthly payments.
Comments: Accepted to IEEE COMPSAC 2026 (Paper ID 9376, SEPT Symposium). This is the de-anonymized camera-ready version. Code is available at: this https URL
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Software Engineering (cs.SE)
Cite as: arXiv:2604.22096 [cs.CR]
(or arXiv:2604.22096v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2604.22096
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From: Zhaohui Wang [view email]
[v1] Thu, 23 Apr 2026 22:00:45 UTC (884 KB)
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