AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent

Traditional compliance and fraud detection systems analyze financial documents in isolation, making it difficult to identify sophisticated fraud patterns that emerge across multiple enterprise systems. This session presents an AI-driven framework that combines graph-based entity correlation, adaptive probabilistic risk modeling, and cross-jurisdictional normalization to detect hidden compliance risks across payroll, tax, procurement, and financial records. Drawing on an evaluation of approximately three million anonymized records across four jurisdictions, the talk demonstrates how cross-document intelligence can improve fraud detection accuracy, reduce false positives, and lower manual audit effort. Attendees will gain practical insights into building scalable AI solutions that transform enterprise compliance from a reactive validation process into a predictive, intelligence-driven capability.

Speakers:
- Varsha Shah (Independent Researcher): Varsha Shah is a Technical Architect, researcher focused on enterprise AI, agentic systems, intelligent document processing, and AI-powered financial governance.
  LinkedIn: linkedin.com/in/varsha-shah-7b5111247
  GitHub: https://github.com/VarshaShahTech