Union Bank of the Philippines (UnionBank) tested a new approach that leverages artificial intelligence (AI) and graph analytics to boost the detection of fraudulent transactions.
Developed by UnionBank’s Artificial Intelligence and Innovation Center of Excellence team, this method delves deep into identifying nuanced patterns of fraudulent behavior. The bank is hoping to further strengthen its systems against financial risks while expediting and refining decision-making processes to achieve heightened accuracy and efficiency.
Employing graph analytics, UnionBank explored transactional relationships, particularly focusing on the involvement of intermediary accounts employed by fraudsters to clandestinely engage in money laundering across networks. Distinguishing itself by extending analysis beyond the immediate connections, the bank examined relationships spanning up to three degrees, thereby garnering a comprehensive understanding of the diverse risks associated with fraudulent activities.
UnionBank releases recommendations on responsible AI implementation
Data science, AI beneficial to open finance — UnionBank exec
“Examining the influence of an account within a network is one pattern we seek to identify. The technique we employed to assess this is by measuring its centrality,” said Abigail Antenor, data scientist at UnionBank.
Fraud indicators
This innovative approach empowered the bank to quantify connection volumes linked with each account, pinpoint intermediary entities, and evaluate their proximity to other accounts, thereby gauging the velocity of fund transfers. UnionBank also looked into metrics across various degrees of connection to determine the most important indicators correlating with fraud.
Tailoring fraud indicators to specific degrees yielded significant efficacy, with findings showcasing a 19% increase in the detection of fraudulent transactions by extending fraud indicators to encompass second and third-degree connections, thereby achieving an 80% reduction in turnaround time.
This breakthrough arrives at a crucial time, with fraudsters persistently devising sophisticated attacks, particularly targeting banking systems and their clientele.
“With this, we have further proven that AI can significantly augment our ability to spot patterns in transaction flows, detect malicious activities, and prioritize suspicious accounts for further investigation,” said Dr. Adrienne Heinreich, head of AI Center of Excellence, Data and AI Group, UnionBank.