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

AI Engineer · 19 min · 145 sentences · from YouTube's caption track

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1. 00:04Hello everyone.
2. 00:05Thank you to the AI engineering world fair team for providing this wonderful opportunity to share my research.
3. 00:13It is truly an honor to be speaking alongside so many talented researchers and practitioners.
4. 00:18My name is Varsha Shah.
5. 00:20I am an enterprise technical architect working at Tata Consultancy Services working for Microsoft.
6. 00:26I'm focused on artificial intelligence enterprise compliance, finance governance, and intelligent automation.
7. 00:34Today, I would like to share my research on AI-driven multi-document correlation for enterprise financial compliance and fraud detection.
8. 00:43As organizations continue to digitalize their operations today, they generate a numerous amount of data for the financial system across payroll, tax, procurement, transaction system.
9. 00:56Ironically, while we have more data than ever before, compliance teams continue to struggle with hidden fraud patterns, regulatory risk.
10. 01:05The reason is the most existing solution analyze the documents independently.
11. 01:10While many of the most critical risk only become visible when the information is connected across the multiple systems.
12. 01:18In this presentation, I'll introduce you to a framework that combines the graph-based entity correlation, probabilistic risk modeling, and cross-jurisdictional normalization to uncover these hidden relationships and transform enterprise compliance from reactive process into proactive intelligence capability.
13. 01:38The framework was evaluated using approximately 3 million financial records across four jurisdictional demonstrating both the strong detection performance and meaningful operations improvement.
14. 01:51With that context, let's begin by looking at the compliance gap that organizations continue to face today.
15. 02:01Let's begin by understanding the compliance gap that many organizations continue to face.
16. 02:05Enterprise compliance has become significantly more complex over the last decade.
17. 02:10Organizations now operate across multiple countries, regulatory frameworks, and financial system, each with own reporting standards and compliance requirements.
18. 02:20At the same time, the volume of the enterprise has grown exponentially.
19. 02:25Payroll records, tax filings, procurement transactions, and financial documents are generated every day, making manual reviews both time-consuming and increasingly impractical.
20. 02:38Adding to this challenge, fraud has been evolved.
21. 02:42Modern fraud rarely appears as an obvious error within a single document.
22. 02:48Instead, it exploits subtle inconsistency across multiple system.
23. 02:54Patterns that often remain invisible when the records are reviewed independently.
24. 02:59This creates a fundamental limitation.
25. 03:02Traditional rule-based and document-level NLP system are designed to validate individual records, but they are not built to understand relationship across the documents.
26. 03:14And that's the gap this research aims to address.
27. 03:17Moving beyond this isolated document analysis to uncover the hidden risk through the cross-document correlation.
28. 03:24To better understand this limitation, let's look at why traditional document level analysis often fail to detect the most sophisticated fraud patterns.
29. 03:36To understand why traditional approaches struggle, let's consider how most compliance system operates nowadays.
30. 03:44They evaluate each document independently.
31. 03:47A payroll register is validated against the payroll rules.
32. 03:51The vendor invoices are checked against the procurement policies.
33. 03:55A tax filing is reviewed under the tax regulations.
34. 03:59If each document passes its individual validation, the transaction is generally considered compliant.
35. 04:05The challenge is that many sophisticated fraud patterns doesn't really appear within a single document.
36. 04:12They emerges only when the multiple documents are analyzed together.
37. 04:16For example, a payroll record may appear accurate to us.
38. 04:19A vendor invoice may seem legitimate to us.
39. 04:23A tax filing may be correctly submitted.
40. 04:27But, when these records are connected, they're revealing the inconsistencies and indicate the fraud and compliance risk.
41. 04:34The information already exists.
42. 04:37What missing is the ability to understand the relationship between these documents.
43. 04:41That is why the research shifts the focus from document level validation to cross document intelligence, enabling the organizations to detect the risk that would otherwise remain hidden.
44. 04:53So, if the problem is understanding relationships rather than individual documents, what kind of architecture can solve this?
45. 05:01Let me introduce you to the framework.
46. 05:05Now that we have established the problem, let's look at the proposed framework.
47. 05:09Rather than relying on a single model or algorithm, the solution is built on three complementary components that work together to transform the raw enterprise data into the actionable compliance intelligence.
48. 05:22The first component is the entity correlation engine.
49. 05:26It the purpose of this is to connect the related information across the payroll, tax, procurement, financial systems, creating a unified view of enterprise activity rather than isolated records.
50. 05:39Once these relationships are established, the second component that is adaptive probabilistic risk model which is which evaluates the connected data to determine which pattern represent meaningful compliance.
51. 05:53Instead of generating alerts based on single rule, it it prioritizes the cases using the multiple risk signals here.
52. 06:02The cross-jurisdictional normalization layer provides the regulatory context by standardizing the currency, tax structure, reporting standards, and compliance rules across the different jurisdictions.
53. 06:16This ensures that the risk are evaluated consistently regardless of where the transaction originated.
54. 06:24So, individually, each component provides a value.
55. 06:27Together, they enable the framework that move beyond the document validation and towards the enterprise-wide compliance intelligence.
56. 06:35In the next few slides, I'll briefly explain you how each of these components contribute to the process.
57. 06:42So, let's begin with the foundation of the framework, the entity correlation engine, which makes the cross-document intelligence possible.
58. 06:52So, the first component is graph-based entity correlation engine.
59. 06:56The role is to connect the entities across the payroll, tax, procurement, financial system into unified network.
60. 07:03Rather than analyzing the documents independently, it it identifies the relationship between the employee, vendors, account, transaction, regulatory files.
61. 07:14And these connections reveal the hidden pattern and structural anomalies that are often invisible to the traditional document level analysis we were doing.
62. 07:24In simple terms, this component answers one fundamental question, what is connected?
63. 07:30It provides the relation relational fundamental for the rest of the framework.
64. 07:36Once those relationships are established, the next step is determining which one actually represents the meaningful risk.
65. 07:48Once relationships have been established, the next step is to determine which one actually represents meaningful risk.
66. 07:55That's the role of the adaptive probabilistic risk model.
67. 07:58Instead of relying on static rules, it combines the multiple risk indicators such as anomaly anomaly strength or the source reliability historic patterns has happened to calculate confidence best risk score.
68. 08:15This helps prioritize the cases that require immediate attention while reducing the unnecessary investigations.
69. 08:22The important advantage is its ability to learn from the audit outcomes, allowing the models to continuously improve its accuracy over the time.
70. 08:32Simply put, this component answers the question that what is most likely to be genuine compliance risk.
71. 08:40Once we have identified the high-risk cases, the final step is to ensure those risk are interpreted consistently across the different regulatory environments.
72. 08:52The final component is cross-jurisdictional normalization layer.
73. 08:56Global organizations operate across different countries, currencies, tax structure, and reporting standards.
74. 09:04Without normalization, the same transaction can be interpreted differently depending on the jurisdiction.
75. 09:10This layer harmonizes the financial data by standardizing the currencies, tax rules, the reporting periods, and the classification schemes as well.
76. 09:22As a result, the risk are evaluated consistently regardless of where the where it has originated.
77. 09:29In short, this component answers to the question, "How should the risk be interpreted within the appropriate regulatory context?"
78. 09:38With all these components working together, the next step was the evaluated how the framework performs under real-world enterprise conditions.
79. 09:48So, before discussing the result, let's um uh it's it's very important to uh briefly understand the evaluation of the environment.
80. 09:56The framework was evaluated using the approximately 3 million of the financial records collected over 5 years of period over the four different regulatory jurisdictions.
81. 10:07The objective was to assess how the framework perform under the real realistic uh enterprise condition and where uh large volume of interconnected financial data and varying regulatory uh requirement introduce the significant complexity here.
82. 10:24Uh with that foundation established, let's look at how the framework perform.
83. 10:28So, how effective was the framework in detecting hidden compliance risk?
84. 10:34Let's look at the results.
85. 10:37So, here now uh look at the detection performances.
86. 10:41The framework achieved approximately 91% of precision, meaning the vast majority of the flat cases were confirmed as a genuine anomalies here.
87. 10:51At um it also achieved 87% um recalls, demonstrating its ability to identify most true fraud cases while minimal uh minimizing the misdetections here.
88. 11:07So, together, these results produce a F1 score that is 0.89, indicating a strong balance between the precision and recall here.
89. 11:17More importantly, these results are achieved across four jurisdictions and large enterprise scale data, demonstrating that the framework performs consistently in the complex real-world uh environments.
90. 11:29The next question is um equally important.
91. 11:32How do these improvements translate into operational value for the compliance teams?
92. 11:38Detection accuracy is one part of the story.
93. 11:41The real value um comes from improving the efficiency of the compliance operations.
94. 11:50Beyond detection accuracy, the framework delivers measurable operational benefits.
95. 11:54One of the most significant outcome was a 76% uh reduction in false positive, meaning um investigators spend uh far less than reviewing the cases that they uh they're ultimately
96. 12:08legitimate.
97. 12:10So, in addition, the framework reduces the manual audit efforts by approximately 40% allowing compliance teams to be focused on the only high-risk cases instead of uh routing the document reviews.
98. 12:22Ultimately, uh this translates into faster investigations, improved uh resource utilization, and uh greater confidence in compliance decisions.
99. 12:32In other words, the uh the framework doesn't just detect fraud more effectively, it helps compliance teams work more effectively.
100. 12:42So, these operational improvements become even more meaningful when we compare uh the uh the framework with the traditional rule-based approaches.
101. 12:58This comparison highlights the advantage of moving beyond the traditional rule-based system.
102. 13:03Uh conventional approaches are effective at validating the individual records, but they often struggle to detect the complex fraud patterns uh that spans multiple uh documents and systems.
103. 13:15So, by incorporating entity uh correlation, probabilistic risk modeling, and regulatory normalization, the uh proposed framework consistently um performs the baseline across the key performance metrics.
104. 13:29The key takeaway is simple.
105. 13:31Connecting data across documents produces better detection, fewer false positive, and more actionable, uh, compliance intelligence than analyzing the documents is, um, in in isolations.
106. 13:44One of the reason the framework continues to improve over the time is that, uh, doesn't rely on the static rules anymore.
107. 13:52Instead, it continuously learn from the completed audit and investigate, uh, and the investigator feedbacks here.
108. 14:00So, this framework is designed to continuously improve through the feedback.
109. 14:05Every, uh, completed audit provides the valuable information.
110. 14:10Uh, confirm the fraud case, strengthen the future detection pattern.
111. 14:14While false positive helps refining the risk scoring and reduce the unnecessary alerts.
112. 14:19This create a continuous learning cycle where the system becomes more accurate with each audit and investigation.
113. 14:27As fraud patterns evolve and business, uh, environments changes, the framework adopts rather than relying on manual rule updates.
114. 14:36In simple terms, every completed audit helps make the next audit more efficient.
115. 14:41This continue, uh, continue learning, uh, capability enables a broader shift here.
116. 14:48From reactive to compliance issue after they occur to identifying the risk before they become audit, uh, findings here.
117. 14:57It's more of a proactive.
118. 15:02Beyond improving detection accuracy, this framework changes how organization approach compliances.
119. 15:08Traditionally, the compliance has been reactive.
120. 15:11Issue were identified after audits, investigation, or regulatory reviews have happened.
121. 15:17This framework enables more proactive approach through continuous, uh, monitoring, cross document correlation, adaptive learning.
122. 15:25Instead of asking what went wrong, organizations can begin asking what is likely to go wrong next.
123. 15:32That shifts from reactive validation to predictive governance, allowing the compliance teams to identify the risk earlier, prioritize the investigation more effectively, and make better informed decisions.
124. 15:45Ultimately, the compliance becomes an ongoing intelligence functions rather than a periodic review process.
125. 15:53While the framework is uh research driven, it has been designed with the enterprise deployment in mind.
126. 16:00Successful implementation depends on uh four key consideration.
127. 16:04First one is seamless integration with existing enterprise systems such as ERP, payroll, procurement, tax uh tax platforms.
128. 16:13Second, the jurisdictional specific uh configuration to ensure the compliance with local regulations and reporting standards.
129. 16:21Third one is um alignment with the audit framework enabling the investigator to focus on prioritized risk code cases instead of manual document reviews.
130. 16:33And finally, scalability.
131. 16:35The evaluation demonstrate that the framework can effectively process millions of financial records um making it suitable for the large enterprise environments.
132. 16:45Uh so, together these uh consi- these considerations help bridge the gap between the research and the practical enterprise adoption.
133. 16:57As I conclude, I would like to leave you with four key takeaways.
134. 17:02First is many of today's most significant compliance and fraud risk exist between the documents, not within them.
135. 17:10Detecting these risk require more beyond document level analysis and cross document intelligence.
136. 17:19Second is combining the graph based entity correlation, probabilistic risk modeling, and cross jurisdictional normalization creates a more comprehensive and scalable approach to enterprise compliance.
137. 17:31Third is the result demonstrated that this particular framework is not only improves the detection accuracy, but also reduces the false positive and significantly lowers the manual audit efforts, delivering the measurable operational value.
138. 17:49Finally, by continuous learning from the audit outcomes, and the framework enables the organizations to move beyond reactive compliance toward predictive intelligence driven risk management here.
139. 18:03I believe this represents an important step toward the future of enterprise financial govern- governance, where AI is not only helping the organizations to detect risk, but also anticipates and
140. 18:19prevent this risk going forward.
141. 18:25So, thank you once again to the AI Engineering World Fest team for this wonderful opportunity, and thank you all for your time and attention.
142. 18:34Please feel free to reach out to me with your questions and your enthusiasm on this email below, or connect me on the LinkedIn.
143. 18:43I'm happy to help you.
144. 18:45If you're building with such kind of amazing systems for compliances using the AI wanted to transform them, and thank you all again.
145. 18:57See you next time.