用区块链锁定审计记录，防止内部人员篡改欺诈检测日志。
Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML
- 将机器学习预测与审批流程写入不可篡改的区块链，全程由智能合约控制。
- 检测准确率高（F1=0.895），决策过程可加密验证，满足GDPR等合规要求。
- 系统延迟低于25毫秒，单次操作成本不足0.01美元，适合大规模企业使用。
在企业欺诈检测中，仅靠模型精度不足，因内部人员可篡改审计日志或绕过审批流程。现实案例显示，欺诈持续存在并非算法失效，而是审计链被特权操作者掌控，形成根本性信任缺口：谁来审计审计者？本文提出一种防篡改欺诈检测系统，将机器学习预测与工作流执行锚定至不可篡改的区块链账本。不将区块链作为被动存储，而是通过智能合约强制执行完整审批流程，确保每笔交易、预测和解释均原子化记录且无法事后修改。检测模块实现竞争性准确率（F1 = 0.895，PR-AUC = 0.974），并提供密码学可验证的决策路径，支持监管审计需求（如GDPR第22条）。系统评估显示推理延迟低于25毫秒，基于Layer-2网络部署成本低于0.01美元/交易（经PolygonScan验证），可支撑每月超10,000笔支付的企业级负载。
原文摘要 · Abstract (English)
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.
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