AI-Driven Multi-Document Correlation for Financial Compliance
summary
Framework combines graph-based entity correlation, adaptive probabilistic risk modeling, and cross-jurisdictional normalization into a deployable system evaluated on approximately three million anonymized records across four jurisdictions. It reports ~91% precision, ~87% recall, F1 ≈ 0.89, a 76% reduction in false positives and ~40% lower manual audit effort. Integration points include ERP payroll procurement and tax systems and the implementation scales to enterprise volumes demonstrating measurable detection and operational improvements.
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chapters (14)
- 0:00Introduction and context
- 2:00The compliance gap
- 3:36Limitations of current tools
- 5:04Framework architecture
- 6:51Entity correlation engine
- 7:45Probabilistic risk modeling
- 8:51Normalization layer
- 9:47Evaluation and results
- 10:36Operational impact
- 12:53Continuous learning cycle
- 13:59Predictive governance
- 15:00Implementation considerations
- 16:56Conclusion and takeaways
- 18:23Closing remarks
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