Lag in adoption of artificial intelligence (AI) leaves Asian financial institutions vulnerable to rising financial crime, new study finds SymphonyAI And Asian regulations revealed Tuesday.
According to the study, existing systems, data quality, explanatory power of models, data privacy and regulatory uncertainty hinder the adoption of AI in financial crime compliance.
Only 15% of Asian financial institutions report “advanced” integration of AI into their compliance functions, leaving significant untapped potential.
It is worth noting that financial crime, particularly money laundering, poses a growing threat, accounting for up to 6.7 percent of global gross domestic product (GDP).
The report, titled “Untapped Potential: AI-enabled Financial Crime Compliance Transformation in Asia – Maturity, Applications and Trends,” is based on surveys and interviews with 126 compliance, operations and compliance practitioners. and financial crime technology from financial institutions in the Asia-Pacific region. (APAC).
The findings reveal a stark reality: despite early evidence of AI’s effectiveness in fighting financial crime, more than 50% of financial institutions in the Asia-Pacific region are not currently using AI to fight against money laundering (AML).
This hesitancy to embrace new technologies comes at a time when financial crime is soaring in the region.
Quoted by Moody’s, it notes that in Southeast Asia, money laundering risk events increased by 64% in 2023 compared to 2018, Thailand, Singapore, Malaysia, Indonesia and the Philippines constituting the five main countries.
Although interest in AI is high, the study highlighted that only 15% of financial institutions in Asia report actively applying the technology to anti-money laundering processes.
It indicates that many companies are limited by AI integration and data quality: integrating AI into existing systems (58.6%), data quality and availability (58.6%) %), the explanatory capacity of the model (46.6%) and confidentiality and data protection (43.1%). among the main challenges cited by respondents.
He also noted that regulatory standards differ in different markets: from Singapore’s balanced approach to Australia’s mandatory safeguards, Asian countries are forging diverse regulatory pathways for AI.
According to the study, 37.9% of respondents cited regulatory compliance as a major challenge.
Executives are optimistic, but proof of value is key: Boards and C-suite executives play a critical role in AI adoption, with 40% of respondents saying their top executives are key. defenders.
However, the demonstrable value of AI through reducing false positives, improving accuracy and efficiency, and controlling costs is crucial to board buy-in for investments in AI. AI.
“Financial institutions around the world that have adopted AI-driven predictive and generative AML have seen transformational results in productivity, accuracy and speed, but Asian financial institutions are lagging behind their peers. peers elsewhere in the adoption of these critical technologies,” said Gerard O’Reilly, Managing Director of APAC, Financial Services, SymphonyAI.
“The rapid growth and varying levels of regulation and maturity of the Asia-Pacific financial services market present a unique challenge and opportunity for organizations,
“Keeping pace with compliance requires strategic adoption of AI with full board buy-in to drive meaningful change,” he added.
The study also found that nearly 58.6% of respondents cited challenges with legacy systems and data quality as major barriers to AI adoption.
Many financial institutions still view AI as a long-term project, particularly due to the perceived complexity of integrating or layering AI into existing systems.
This struggle to effectively implement AI is particularly concerning given the rapid evolution of financial crime.
As criminal activity becomes increasingly sophisticated and transcends borders, traditional methods of compliance are proving woefully inadequate, the study notes.
“Asian financial institutions recognize the potential of AI in fighting financial crime, but our research shows a significant gap between ambition and action.
“The cost of inaction is growing rapidly. Financial institutions that delay AI adoption risk not only financial losses, but also reputational damage and increased regulatory scrutiny,” said Bradley Maclean, co-founder and research director of Regulatory Asia.
The SymphonyAI-Regulation Asia study highlighted that financial institutions view AI as a critical solution for effective transaction monitoring, with 78% of respondents saying it is a priority deployment area.
This is largely due to AI’s ability to efficiently process large 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 improvement, PEP/sanctions screening, case management, transaction retrospectives and combating trade-based money laundering.
“In the fight against financial crime, particularly in the APAC region, AI is helping financial institutions move from defense to offense,” said Craig Robertson, financial crime expert, APAC, financial services, SymphonyAI.
“AI brings both efficiency and effectiveness. Financial institutions are using AI to more effectively detect new crimes, reduce costly false positives, and control growing operational expenses.
“This proactive approach allows us to prevent crime instead of just reacting to it. The good news is that effective AI implementation can be incremental, delivering immediate value while paving the way for deep, long-term transformation,” he added.
The report also provides a clear roadmap for financial institutions in APAC to accelerate the adoption of AI, for example, financial institutions can safely explore the transformative power of AI by starting small, by learning iteratively and evolving strategically to unlock its full potential.
At the same time, open collaboration between financial institutions, technology providers and regulators is crucial to building trust and shaping a responsible and innovative future for AI in finance.
The research also highlighted that improving operational efficiency through AI is only the first step; financial institutions must reinvest these earnings to strengthen risk management and fight financial crime.
He noted that leveraging AI for data quality and governance can enable financial institutions to streamline their operations, optimize resource allocation and strengthen their digital transformation journey.
Strong governance, clear metrics and executive buy-in are also essential for financial institutions to gain regulatory support and strengthen their compliance efforts, he adds.
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