Uncover Hidden Patterns in Financial Crime Activities using Graph Analytics
Financial crimes in fraud and money laundering spaces are common problems faced by financial institutions. With the use of data analytics and machine learning in recent years, the effectiveness of advanced technologies in financial crime detection has been proven. However, the most commonly used techniques have limited capabilities in discovering underline crime patterns. This presentation focuses on how to leverage graph databases and link analysis to detect and prevent financial crime activities by uncovering hidden patterns in data, using graph algorithms in real-time.
Amir Sepasi is the Lead Data Scientist for Group Advanced Analytics at Manulife Financial. He is currently leading the Global Fraud Analytics Center of Expertise (CoE), working on innovative ways to track the ever-evolving capabilities of fraudsters. His team has recently developed an application entitled Advanced AML Alert (AAA) which uncovers hidden, very sophisticated money laundering patterns using graph databases and link analysis. Amir received a 2018 Star of Excellence, the most prestigious employee recognition program in Manulife.