AI Adoption Lag Leaves Asian Financial Institutions Vulnerable Amid Rising Financial Crime

AI-based transaction monitoring, sanctions screening, and fraud prevention deliver proven benefits as adoption gains traction.
New research from SymphonyAI and Regulation Asia reveals legacy systems, data quality, model explainability, data privacy, and regulatory uncertainty hinder AI adoption in financial crime compliance.
Only 15% of Asian FIs report "advanced" AI integration in their compliance functions, leaving significant untapped potential.
Financial crime, particularly money laundering, represents an escalating threat, accounting for up to 6.7% of global GDP.

SymphonyAI, a leader in predictive and generative artificial intelligence (GenAI) SaaS products for the enterprise, recently launched a new report with Regulation Asia, revealing a concerning gap in artificial intelligence (AI) adoption among Asian financial institutions, leaving them vulnerable to escalating financial crime. The report also details the lack of AI adoption by Asian financial institutions (FIs) despite clear benefits and cost advantages in effective financial crime prevention and detection.

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 financial crime compliance, operational, and technology practitioners from FIs across the Asia Pacific (APAC) region. The results reveal a stark reality: despite recognising the early proof of effectiveness of AI in financial crime compliance, over 50% of APAC FIs are not currently using AI for anti-money laundering (AML).

This hesitancy to embrace new technology comes at a time when financial crime is surging in the region. In Southeast Asia, money laundering risk events climbed 64% in 2023 from 2018, with Thailand, Singapore, Malaysia, Indonesia and the Philippines forming the top five countries, according to Moody's.

Key findings from the report that highlight this trend include:

Lower level of AI sophistication: While interest in AI is high, only 15% of FIs in Asia say they are actively applying the technology for AML processes.
Many firms are limited by AI integration and data quality: 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.
Regulatory standards differ across different markets: From Singapore's balanced approach to Australia's mandatory guardrails, Asian countries are forging diverse regulatory paths for AI. 37.9% of respondents mentioned ensuring regulatory compliance as a key challenge.
Leaders are bullish but proof of value is critical: 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, managing director of APAC, Financial Services, SymphonyAI. "The rapid growth and varying levels of regulation and market maturity in APAC financial services present a unique challenge and an opportunity for organisations. 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. 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 Regulation Asia Co-Founder and Head of Research Bradley Maclean. "The cost of inaction is rising rapidly. Financial institutions that delay AI adoption risk not only financial losses but also reputational damage and increased regulatory scrutiny."

The SymphonyAI-Regulation Asia 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.

"In the fight against financial crime, especially in APAC, AI is helping financial institutions move from defense to offense," said Craig Robertson, financial crime subject matter expert, APAC, Financial Services, SymphonyAI. "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. The good news is, effective AI implementation can be incremental, delivering immediate value while paving the way for profound long-term transformation."

The report also provides a clear roadmap for APAC FIs to accelerate AI adoption, such as:

FIs can safely explore the transformative power of AI by starting small, learning iteratively, and scaling strategically to unlock its full potential.
Open collaboration between FIs, technology providers, and regulators is crucial for building trust and shaping a responsible and innovative future for AI in finance.
Boosting operational efficiency with AI is just the first step; FIs must reinvest those gains to strengthen risk management and combat financial crime.
Leveraging AI for data quality and governance can empower FIs to streamline operations, optimize resource allocation, and strengthen their digital transformation journey.
Strong governance, clear metrics, and leadership buy-in are essential for FIs to successfully secure regulatory support and enhance compliance efforts.