Data quality ranks as the top concern for financial institutions weighing AI adoption in compliance functions, according to a new survey by Risk.net and Fenergo. The finding comes as banks and asset managers across Asia-Pacific evaluate a widening set of AI tools, from generative AI to agentic systems.
The survey gave data quality an aggregate concern score of 275, well ahead of integration with existing systems at 236. Regulatory and compliance concerns followed at 209, with implementation costs close behind at 207. Trust and reliability scored 172, while data privacy reached 162. A lack of expertise scored 132 and technical issues 100.
Change management and C-suite or board-level support ranked lowest, with scores of 42 and 35 respectively. The survey gathered insights from 110 practitioners at banks and asset managers in Singapore, Malaysia and Australia.
Adoption plans span multiple AI technologies
Despite the concerns, most financial institutions are actively exploring AI. AI for Finance adoption is broad: generative AI was the most commonly cited technology, with 77% of respondents saying their organisation was considering it. Machine learning followed at 68%.
Robotic process automation was cited by 47%, natural language processing by 46% and agentic AI by 44%. Another 40% said their organisations were considering predictive analytics. Just 2% said they were considering no forms of AI.
Where agentic AI fits in compliance
Among respondents considering agentic AI, transaction monitoring was the leading priority use case at 66%. Fraud detection followed at 55%, with sanctions screening cited by 46%. KYC maintenance and customer onboarding were identified by 42% and 41%, respectively.
The report said legacy systems, fragmented data and manual processes were making AI implementation more difficult. Governance, data management and shortages of staff with the required technical skills were also flagged as challenges for compliance teams.
Why this matters for finance professionals
The survey makes clear that AI investment is moving forward across compliance functions, but the bottleneck is not model capability. It is data. For finance leaders, the priority is fixing data infrastructure and governance before layering AI on top. Without that, the same fragmented systems and manual processes that slow compliance today will limit what any new technology can deliver. Those evaluating vendor tools or internal builds should ask hard questions about data quality requirements first. For CFOs and senior finance executives, the AI Learning Path for CFOs offers a structured way to build the technical literacy needed to assess these investments.
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