--- title: "Slides: AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent" category: "slides" video_id: "Iwe_RY-fYgI" sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"] --- # Slides: AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent ## Source Video [AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent](https://www.youtube.com/watch?v=Iwe_RY-fYgI) ## Relationship To World's Fair 2026 These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material. ## Related Scheduled Sessions - No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck. ## Extracted Slides ![[assets/slides/Iwe_RY-fYgI/slide-001.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-001.html) - AI slide classifier: `title_card` confidence `0.99` - Text source: agent_vision. Slide text: > AI-Driven Multi-Document Correlation for Enterprise Financial Compliance and Fraud Detection > A framework for cross-document fraud detection through relational intelligence, evaluated across 3 million anonymized records and four jurisdictions. > By Varsha Shah, Enterprise Technical Architect, USA ![[assets/slides/Iwe_RY-fYgI/slide-002.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-002.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > The Compliance Gap No One Is Closing > Multi-Jurisdictional Complexity > Growing Data Volumes > Sophisticated Fraud Patterns > Rule-based and NLP-augmented systems operating at the document level are structurally incapable of detecting these cross-document anomalies. ![[assets/slides/Iwe_RY-fYgI/slide-003.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-003.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: agent_vision. Slide text: > Why Document-Level Analysis Falls Short > Traditional compliance tools evaluate records in isolation. Fraud that arises from discrepancies between payroll registers, vendor invoices, and tax filings remains invisible when each document passes its own internal validation. > The most costly fraud patterns are not found within a single document. They emerge in the space between documents. ![[assets/slides/Iwe_RY-fYgI/slide-004.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-004.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: agent_vision. Slide text: > Framework Architecture Overview > Entity Correlation > Risk Modeling > Normalization Layer > The three components operate in concert: entities are linked relationally, risk signals are aggregated and calibrated, and jurisdictional variance is normalized before scoring. ![[assets/slides/Iwe_RY-fYgI/slide-005.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-005.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: agent_vision. - OCR decision: ready — Dense text slide with title, paragraph, and bullets; OCR is appropriate. Slide text: > Graph-Based Entity Correlation Engine ![[assets/slides/Iwe_RY-fYgI/slide-006.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-006.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: agent_vision. - OCR decision: ready — Dense text slide with title, paragraph, and bullets; OCR is appropriate. Slide text: > Adaptive Probabilistic Risk Model ![[assets/slides/Iwe_RY-fYgI/slide-007.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-007.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Evaluation Conditions > Dataset Scale > Approximately 3 million anonymized financial records > Jurisdictions > Four distinct regulatory environments evaluated in parallel > Time Horizon > Five years of historical data reflecting real-world enterprise conditions ![[assets/slides/Iwe_RY-fYgI/slide-008.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-008.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Detection Performance Results > ~91% Precision > ~87% Recall > ~0.89 F1 Score > Performance was measured against a labeled ground truth derived from confirmed audit findings across all four jurisdictions and the full five-year evaluation window. ![[assets/slides/Iwe_RY-fYgI/slide-009.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-009.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: none. - OCR decision: ready — Dense slide with charts, captions, and small body text. ![[assets/slides/Iwe_RY-fYgI/slide-010.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-010.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: none. - OCR decision: ready — Dense multi-column comparison slide with small text. ![[assets/slides/Iwe_RY-fYgI/slide-011.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-011.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: none. - OCR decision: ready — Diagram slide with multiple labels and a paragraph of small text. ![[assets/slides/Iwe_RY-fYgI/slide-012.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-012.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: none. - OCR decision: ready — Four dense text panels with small body copy. ![[assets/slides/Iwe_RY-fYgI/slide-013.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/Iwe_RY-fYgI/slide-013.html) - AI slide classifier: `title_card` confidence `0.99` - Text source: agent_vision. Slide text: > Thank you. > Varsha Shah — Enterprise Technical Architect > linkedin.com/in/varsha-shah-7b5111247 > varsha.shah.tech@gamil.com Classification audit: `raw/sources/slide-ai-classification/slides/Iwe_RY-fYgI/audit.json` ## Slide-Derived Subjects To Review Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.