Machine learning and AI-driven analytics critical to tackle money laundering: FICO

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Machine learning and AI-driven analytics critical to tackle money laundering: FICO

42 percent of respondents in a recent survey conducted by Silicon Valley analyst firm FICO believe that an increased uptake of anti-money laundering (AML) solutions that use machine learning is the best way to improve financial probity.

The survey highlighted that the recent Asian money laundering scandals continue to shake up the financial world, and the ripple effect is still keenly felt across the region.

90 percent of them fear they or their peers, may risk inadvertently facilitating the next money laundering scandal.

“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.

  • 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.
  • 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.

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.

“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.

Images are for reference only.Images gathered automatic from google.All rights on the images are with their original owners.

2019-02-28 07:32:34

Images are for reference only.Images gathered automatic from google.All rights on the images are with their original owners.

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