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Singapore leads in AI but faces client loss challenges

Newsflash Asia

- October 8, 2025

Singapore’s financial institutions are at the forefront of adopting artificial intelligence (AI) for compliance, with a remarkable 92% integration rate, according to the 2025 Fenergo Financial Crime Industry Trends Report. Despite this, Singapore reports the world’s highest client loss rates, with 76% of firms losing clients due to onboarding inefficiencies.

The report, based on a global survey of 600 senior decision-makers in banks, asset managers, and fund administrators, highlights the paradox of Singapore’s financial sector. Whilst leading in AI-powered know your customer (KYC) and anti-money laundering (AML) adoption, the country struggles with onboarding delays, a critical factor in client retention.

Globally, AML fines have surged, reaching $1.23b in the first half of 2025, a 417% increase from the previous year. Singapore’s average AML costs stand at $68.2m per firm, reflecting ongoing regulatory pressures following a significant money-laundering scandal.

Cengiz Kiamil, Managing Director for Asia Pacific at Fenergo, emphasised the importance of embedding AI throughout the client lifecycle to meet the Monetary Authority of Singapore’s (MAS) heightened expectations. “Our survey shows Singaporean firms lead the world in AI adoption at 92%. Whilst more Singaporean FIs lost clients due to inefficient onboarding (76%) compared to other countries, this has changed significantly since 2024 when 87% of firms lost clients due to subpar onboarding,” Kiamil stated.

The report underscores the need for financial institutions to streamline operations and enhance client experiences to reduce abandonment rates whilst maintaining compliance with global standards. As Singapore continues to navigate these challenges, the integration of AI remains a pivotal strategy for improving efficiency and client satisfaction.

This story was selected and published by a human editor, with content adapted from original press material using AI tools. Spot an error? Report it here.

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