In financial services, the battle for artificial intelligence (AI) advantage will not be won by those who experiment first, but those who build on strong foundations.
Singapore’s banks and large financial institutions are moving quickly to embed AI into everything from customer engagement and risk analytics to compliance and operations, with firms like DBS already running thousands of AI models across hundreds of applications. As targeted incentives under Budget 2026 and regulatory expectations around AI risk management sharpen, the performance gap is likely to widen between institutions that have strong systems connecting their data and those that do not.
For years, integration was framed as an IT efficiency issue: how to connect systems more cheaply, or move data faster between back‑office applications. That is no longer sufficient. As the Monetary Authority of Singapore (MAS) advances its proposed guidelines on AI risk management, integration has become risk, compliance and growth infrastructure all at once. Without a clear, governed integration layer, it is difficult to know which models are using which data, or to prove that AI‑driven decisions meet supervisory expectations.
Banks that treat integration as a core function can consolidate data from core banking, payments, trade, treasury, regulatory tech (regtech) and ESG systems into well‑defined, policy‑controlled interfaces. That enables them to deploy AI models faster, with consistent data pipelines and audit trails. It also lets them scale successful use cases across products and markets, instead of rebuilding connectivity each time they launch a new service or enter a new Asean market.
Institutions with strong integration backbones can bring AI‑enabled products to market more quickly, personalise offerings based on holistic customer data, and respond faster when conditions change – whether that is a new fraud pattern, an emerging risk, or a regulatory update. Those stuck in siloed data and manual workarounds will find their time‑to‑market lagging, even if they are running similar models on paper.
Budget 2026’s AI and digital transformation measures, together with MAS’ evolving AI risk‑management guidelines – including 400% tax deductions on qualifying AI expenditure under the expanded Enterprise Innovation Scheme and tailored support through programmes such as Champions of AI – combined with evolving AI risk guidelines, could amplify these differences.
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Support for AI adoption and talent will be most valuable to financial institutions that have already invested in the integration and data platforms needed to operationalise new capabilities safely. Conversely, those without that foundation may struggle to absorb incentives, facing higher marginal costs and more complex risk discussions for each incremental AI project.
David Irecki is the CTO for APJ at Boomi
