Despite recognising the early proof of effectiveness of AI in financial crime compliance, over 50% of financial institutions in the Asia-Pacific region are not currently using AI for anti-money laundering (AML), according to a report from SymphonyAI and Regulation Asia.
The report said that this hesitancy to embrace new technology comes at a time when financial crime is surging in the region.
The findings are based on data collected from surveys and interviews conducted with 126 financial crime compliance, operational and technology practitioners across APAC between July and September 2024.
Respondents were based in markets including Singapore, Australia, New Zealand, Malaysia, Philippines, Thailand, Vietnam, Indonesia, Hong Kong, and Japan, among others.
Findings show that while interest in AI is high, only 15% of FIs in Asia say they are actively applying the technology for AML processes.
Integrating AI with existing systems (58.6%), data quality and availability (58.6%), model explainability (46.6%), and data privacy and protection (43.1%) were among the top challenges cited by respondents.
From Singapore’s balanced approach to Australia’s mandatory guardrails, Asian countries are forging diverse regulatory paths for AI. Among respondents, 37.9% mentioned ensuring regulatory compliance as a key challenge.
Boards and senior managers are playing a critical role in driving AI adoption with 40% of respondents saying their top leaders are primary advocates.
However, demonstrable value of AI through reducing false positives, improving accuracy and efficiency, and controlling costs is crucial for board-level AI investment buy-in.
“Financial institutions worldwide who have adopted predictive and generative AI-powered AML have seen transformational results in productivity, accuracy, and speed, yet Asian financial institutions lag their counterparts elsewhere in embracing these critical technologies,” said Gerard O’Reilly, SymphonyAI managing director of APAC Financial Services.
“The rapid growth and varying levels of regulation and market maturity in APAC financial services present a unique challenge and an opportunity for organisations,” said O-Reilly. “Keeping pace with compliance demands a strategic embrace of AI with full board-level buy-in to drive meaningful change.”
The research found that nearly 58.6% of respondents cited challenges with legacy systems and data quality as major roadblocks to AI adoption. Many FIs still see AI as a long-term project, especially the perceived complexity of integrating or overlaying AI into legacy systems.
The report finds this struggle to effectively implement AI is particularly concerning given the rapidly evolving nature of financial crime. As criminal activity becomes increasingly sophisticated and transcends borders, traditional compliance methods are proving woefully inadequate.
“Asian financial institutions recognize the potential of AI for fighting financial crime, but our research shows a significant gap between ambition and action,” said Bradley Maclean, Regulation Asia co-founder and head of research.
“The cost of inaction is rising rapidly,” said Maclean. “Financial institutions that delay AI adoption risk not only financial losses but also reputational damage and increased regulatory scrutiny.”
The study highlighted that FIs see AI as an essential solution for effective transaction monitoring, as 78% of respondents stated it is a top priority area for deployment. This is largely due to AI’s ability to efficiently process vast amounts of data to detect suspicious patterns that traditional methods might miss.
Other critical areas where AI is being implemented include KYC/digital verification, data integrity enhancement, PEPs/sanctions screening, case management, transaction lookbacks, and combating trade-based money laundering.
Craig Robertson, financial crime subject matter expert, APAC, Financial Services, SymphonyAI, said in the fight against financial crime, especially in the region, AI is helping financial institutions move from defense to offense.
“AI is delivering both efficiency and effectiveness. Financial institutions are using AI to detect new crime more effectively, reduce costly false positives, and control spiralling operational expenses. This proactive approach allows us to prevent crime instead of just reacting to it,” said Robertson.
