Fenergo: 66 per cent of APAC compliance teams remain heavily manual
30 January 2026 Ireland
Image: David/stock.adobe.com
Manual workflows continue to dominate despite growing interest in deploying artificial intelligence for compliance, according to new research published by Fenergo in partnership with Risk.net.
The report, ‘AI and the next era of APAC compliance’, which surveyed 110 risk, financial crime and compliance professionals at banks and asset managers in Singapore, Malaysia, and Australia, highlights a widening gap between the scale of compliance workloads, institutional intent to adopt AI and readiness to execute automation at scale.
The firm says financial institutions across Asia Pacific face growing pressure to modernise compliance operations, yet most teams remain heavily reliant on manual processes.
Two-thirds of respondents (66 per cent) report heavy manual workload, with more than half (54 per cent) facing periodic Know Your Customer (KYC) review backlogs, while 45 per cent cite high false-positive rates across KYC, screening, and transaction-monitoring processes.
These pressures persist despite 54 per cent saying they are actively exploring AI use cases, while only one third (34 per cent) have begun implementation, and 13 per cent say they are not using AI at all.
Bryan Keasberry, APAC head of strategy, Fenergo, says the findings reflect the operational complexity facing compliance teams in the region: “Compliance in APAC continues to feel unusually manual, largely due to the region’s linguistic and regulatory complexity.
Institutions are operating across fragmented regulatory regimes, diverse languages and complex data environments.
“That makes data consistency and quality difficult to achieve, and without those foundations, AI adoption inevitably slows.”
While interest in AI is rising, execution remains constrained by structural barriers.
Operational efficiency emerged as the primary driver of AI investment, ranking ahead of cost reduction and task automation.
At the same time, data quality was identified as the single biggest challenge to AI adoption, followed by integration with legacy systems and regulatory compliance concerns.
Keasberry, says: “The challenges faced by firms today reflect the realities of legacy operating models and the need to balance innovation with regulatory accountability.
“For organisations at earlier stages of AI adoption, keeping human oversight in the loop remains essential. Regulators expect AI systems to be explainable and well governed.
"The findings suggest institutions want to demonstrate control over how AI models operate and how decisions are reached, particularly in high-risk areas such as KYC, AML and fraud.”
The report, ‘AI and the next era of APAC compliance’, which surveyed 110 risk, financial crime and compliance professionals at banks and asset managers in Singapore, Malaysia, and Australia, highlights a widening gap between the scale of compliance workloads, institutional intent to adopt AI and readiness to execute automation at scale.
The firm says financial institutions across Asia Pacific face growing pressure to modernise compliance operations, yet most teams remain heavily reliant on manual processes.
Two-thirds of respondents (66 per cent) report heavy manual workload, with more than half (54 per cent) facing periodic Know Your Customer (KYC) review backlogs, while 45 per cent cite high false-positive rates across KYC, screening, and transaction-monitoring processes.
These pressures persist despite 54 per cent saying they are actively exploring AI use cases, while only one third (34 per cent) have begun implementation, and 13 per cent say they are not using AI at all.
Bryan Keasberry, APAC head of strategy, Fenergo, says the findings reflect the operational complexity facing compliance teams in the region: “Compliance in APAC continues to feel unusually manual, largely due to the region’s linguistic and regulatory complexity.
Institutions are operating across fragmented regulatory regimes, diverse languages and complex data environments.
“That makes data consistency and quality difficult to achieve, and without those foundations, AI adoption inevitably slows.”
While interest in AI is rising, execution remains constrained by structural barriers.
Operational efficiency emerged as the primary driver of AI investment, ranking ahead of cost reduction and task automation.
At the same time, data quality was identified as the single biggest challenge to AI adoption, followed by integration with legacy systems and regulatory compliance concerns.
Keasberry, says: “The challenges faced by firms today reflect the realities of legacy operating models and the need to balance innovation with regulatory accountability.
“For organisations at earlier stages of AI adoption, keeping human oversight in the loop remains essential. Regulators expect AI systems to be explainable and well governed.
"The findings suggest institutions want to demonstrate control over how AI models operate and how decisions are reached, particularly in high-risk areas such as KYC, AML and fraud.”
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