TigerGraph > Case Studies > Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph

Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph

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 Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
  • Cybersecurity & Privacy - Identity & Authentication Management
Applicable Industries
  • Finance & Insurance
Use Cases
  • Fraud Detection
  • System Integration
The Customer
About The Customer
The customer is a major financial institution that provides banking, investment, mortgage, trust, and payment services products to individuals, businesses, governmental entities, and other financial institutions. It is one of the largest banking institutions in the United States, with over 3,000 branches, primarily in the Western and Midwestern United States. The company has subsidiaries that include a processor of credit card transactions for merchants and a credit card issuer that issues credit card products to financial institutions.
The Challenge
The financial institution, one of the largest in the United States, was seeking to enhance its networking and link analysis capabilities for anti-money laundering. The institution wanted to identify connections between open work items in situations of interest, such as previous SAR filings and other open work items, and make this information available to analysts and investigators. They also wanted to conduct thorough ad hoc reviews of an entity, displaying the connections from an ecosystem surrounding a specific starting point of an investigation. Furthermore, the system should enable analysts to identify which connections and situations of interest lead to productive investigations and inform the creation, hibernation, or escalation of work items. The company was in search of a solution that offered a client-focused approach, state-of-art technology, and a next-generation database management solution that integrated seamlessly with its existing workflow.
The Solution
The financial institution chose TigerGraph to improve its anti-money laundering capabilities. TigerGraph provided the institution with the ability to perform in-depth analysis that was impossible with its prior legacy system based on a relational database. TigerGraph offered the capability for the business to see its connected data in context. Moreover, TigerGraph delivered the scalability to analyze ever-increasing amounts of data and an extensibility derived from its support for machine learning that keeps the bank’s anti-money laundering program ahead of financial criminals. The institution deployed TigerGraph on AWS, resulting in a scalable, high-performance system that allowed them to quickly deliver real-time insights into complex relationship-based workflows common in tasks such as credit scoring, fraud detection, recommendation engines, and risk analysis.
Operational Impact
  • The financial institution has seen significant improvements in its analytical and investigative teams' ability to identify and trace connections between work items indicative of money laundering and react in real-time. This has enhanced the effectiveness and efficiency of these teams. Furthermore, substantial gains in productivity have resulted from their ability to determine which situations of interest to prioritize. Although the system has only been in production for a few months, the improvements are already evident.

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