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

Edit This Case Study Record

	Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph TigerGraph

Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph

TigerGraph

Technology Category	Analytics & Modeling - Machine Learning Cybersecurity & Privacy - Identity & Authentication Management
Applicable Industries	Finance & Insurance
Use Cases	Fraud Detection
Services	System Integration
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. Read More
About 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. Read More
Customer Name	Major Bank Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Cybersecurity & Privacy - Identity & Authentication Management

Applicable Industries

Finance & Insurance

Use Cases

Fraud Detection

Services

System Integration

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.

About 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.

Customer Name

Major Bank

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.

Impact #1	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.

Impact #1

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.

Download PDF Version

Overview

Major Financial Institution Enhances Anti-Money Laundering Capabilities with TigerGraph

Operational Impact