UnionBank improves fraud detection rate, turnaround time with AI
UNION Bank of the Philippines (UnionBank) recently conducted a study to test a new method that used artificial intelligence (AI) and graph analytics to detect fraudulent transactions more efficiently, leveraging the bank’s capabilities to better safeguard its customers.
Developed by UnionBank’s Artificial Intelligence and Innovation Center of Excellence team, the new method provided nuanced insights into fraud that could potentially improve systems to effectively mitigate financial risks and enhance decision-making processes.
UnionBank used graph analytics to scrutinize the intricate relationships between transactions. For instance, fraudsters often involved intermediary accounts to secretly facilitate money laundering across a network. By expanding the bank’s analysis to three degrees of connection instead of just the first degree, the institution was able to gain a more comprehensive understanding of the risks associated with different fraudulent activities.
This approach allowed the bank to quantify the number of connections associated with each account, identify those acting as middlemen and evaluate their proximity to other accounts to determine the speed of fund transfers. Subsequently, the team studied how these measures correlated with fraud to determine which indicator was most relevant in those scenarios.
Tailoring fraud indicators to each degree proved effective as results showed that 19 percent more fraudulent transactions were detected by applying fraud indicators to include second and third degrees with 80 percent saved for the turnaround time.
This breakthrough was timely as fraudsters had been continuously becoming more innovative in executing attacks, especially those directed at banking systems and bank customers.