World's Fair 2026
AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent
Overview
This talk introduces a framework for AI-driven multi-document correlation designed to enhance enterprise financial compliance and fraud detection. Traditional systems often analyze financial documents independently, missing critical risks that only become apparent when data is connected across multiple systems. The proposed framework integrates graph-based entity correlation, probabilistic risk modeling, and cross-jurisdictional normalization to uncover these hidden relationships, transforming compliance from a reactive process into a proactive intelligence capability.
Who should watch
- Compliance officers struggling with hidden fraud patterns and regulatory risk.
- Financial analysts and auditors dealing with large volumes of disparate data.
- AI engineers and architects looking to build systems for complex data correlation.
- Product managers seeking to improve enterprise governance and risk management.
- Anyone interested in moving from reactive to proactive compliance strategies.
Key takeaways
- Many significant compliance and fraud risks are found by connecting information *between* documents, not just within them.
- Traditional systems fail because they validate individual documents in isolation, missing subtle inconsistencies across multiple systems.
- The presented framework combines three components: a graph-based entity correlation engine to link data across systems, an adaptive probabilistic risk model to assess risk signals, and a cross-jurisdictional normalization layer for consistent interpretation.
- Evaluation using 3 million financial records across four jurisdictions showed approximately 91% precision and 87% recall, with an F1 score of 0.89.
- The framework achieved a 76% reduction in false positives and a 40% reduction in manual audit effort, demonstrating significant operational value.
- Continuous learning from audit outcomes allows the system to adapt and improve accuracy over time, moving towards predictive risk management.
- Successful enterprise deployment requires seamless integration with existing systems, jurisdictional configuration, alignment with audit frameworks, and scalability.
- The approach enables a shift from reactive compliance to predictive intelligence-driven risk management, anticipating potential issues before they arise.
Notable quotes
*Many of today's most significant compliance and fraud risk exist between the documents, not within them.*
*Connecting data across documents produces better detection, fewer false positive, and more actionable, compliance intelligence than analyzing the documents in isolation.*
*The framework enables organizations to move beyond reactive compliance toward predictive intelligence driven risk management.*
Unofficial community note. Prefer the recording for nuance.