AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent
Varsha Shah's research introduces an AI-driven framework for enterprise financial compliance and fraud detection, overcoming the limitations of traditional systems that analyze documents in isolation. The framework combines graph-based entity correlation, adaptive probabilistic risk modeling, and cross-jurisdictional normalization to uncover hidden fraud patterns across payroll, tax, procurement, and financial records. Evaluated on 3 million anonymized records across four jurisdictions, it demonstrates significant improvements in detection accuracy (91% precision, 87% recall), reduces false positives by 76%, and lowers manual audit effort by 40%, ultimately transforming compliance from a reactive process into a proactive, intelligence-driven capability through continuous learning.
AI-Driven Multi-Document Correlation for Enterprise Financial Compliance and Fraud Detection
Varsha Shah’s research presents an innovative AI-driven framework designed to overcome the limitations of traditional compliance and fraud detection systems, which struggle to identify sophisticated fraud patterns hidden across disparate enterprise systems.
The Compliance Gap in Modern Enterprises
Organizations today generate vast amounts of financial data across payroll, tax, procurement, and transaction systems. Despite this data abundance, compliance teams face significant challenges due to:
- Increased Complexity: Global operations, diverse regulatory frameworks, and multiple financial systems make compliance intricate.
- Data Volume: Exponential growth in financial records (payroll, tax, procurement) renders manual reviews impractical.
- Evolving Fraud Patterns: Modern fraud rarely appears as an obvious error within a single document. Instead, it exploits subtle inconsistencies across multiple systems, remaining invisible to isolated document analysis.
- Limitations of Traditional Systems: Rule-based and document-level NLP systems are designed for individual record validation, not for understanding relationships across documents.
Traditional approaches fail because they evaluate each document independently (e.g., a payroll register validated against payroll rules, vendor invoices against procurement policies, tax filings under tax regulations). While each document may appear compliant individually, sophisticated fraud emerges only when these interconnected records are analyzed together. The core problem is not a lack of information, but the inability to understand the relationships between documents, which this research aims to address by shifting focus from document-level validation to cross-document intelligence.
The Proposed AI-Driven Framework
To address this