Recent Asian money laundering scandals continue to shake up the financial world, and the ripple effect is still keenly felt across the region. Analytics firm FICO recently surveyed 50 executives from financial institutions across the region at its annual FICO Asia Pacific Fraud Forum.regional banks and found that more than 90 percent of them fear they or their peers, may risk inadvertently facilitating the next money laundering scandal.
Most respondents (62%) said that a lack of resources was the biggest reason APAC banks remained exposed; 25 percent cited a lack of expertise while 13 percent indicated it was political constraints imposed by government.
“Asia’s reputation for financial probity may take a long time to recover fully and it seems that our region’s bankers agree that we are just at the start of a compliance technology and process overhaul that may take many years to complete,” said Dan McConaghy,President of FICO in Asia Pacific.
Respondents had a difference of opinion on the most effective way to increase money laundering compliance. While around one in five banks (19%) felt that increasing fines and penalties was the most feasible way to improve financial probity, a further 40 percent thought it was necessary to better resource the regulators. However, the majority (42%) of APAC banks believe the best way to tackle money laundering is through introducing anti-money laundering (AML) solutions that use machine learning.
“We have seen fines of up to USD$100 million imposed on banks in Asia for money laundering compliance failures, as well as the departures of numerous CEOs and even the shuttering of some lenders,” said Dan McConaghy. “So the risk for banks can be very high, which is why so many lenders are realizing the need to dramatically improve the efficacy of compliance operations, using machine learning for AML.”
The survey revealed that 40 percent of APAC banks felt that their AML capabilities were average, while a further 20 percent do not know how they might compare to their industry peers. Only two percent said that their AML -readiness made them a recognized top performer in the industry. However, most are optimistic about their AML capabilities in the future – 61 percent of the respondents were confident that their AML approach will be better in a year’s time.
“High false positives and inefficient processes are one reason that the vast majority of money laundering is going unstopped, saidMcConaghy. “That is why computing power and machine learning is required to meet the transactional complexity of AML. It is a large problem to solve. It is currently estimated that the annual amount of money laundered sits at about 2-5 percent of global domestic gross product (GDP).”
To add to the concern, new types of transactions outside of banking are emerging as money laundering risks in the region. APAC banks surveyed rated cryptocurrency (33%), shadow banking (22%) and property transactions (20%) as posing the biggest risks. Chinese banks, in particular, are concerned with cryptocurrency (46%) and shadow banking risks (40%).
“To achieve better AML detection, financial institutions have to help their compliance employees sift through enormous piles of data, more efficiently report suspicious activity to regulators and upgrade their AML tools. Incorporating machine learning and AI-driven analytics to prioritize alerts and accelerate decisions ensures banks can go beyond what rules-based systems can do to catch new types of money laundering.” McConaghy added.