Expectations of an imminent interest rate cut by the Federal Reserve have dampened the profitability outlook for banks across Asia Pacific. As global rates moderate, regional lenders are already seeing pressure on interest income — a core driver of bank earnings.

Compounding this are other structural challenges. According to Fitch Ratings, banks in Singapore are navigating thinner net interest margins as the U.S. Federal Reserve cuts filter through to local benchmarks. Loan growth remains subdued, and profitability is expected to stay under pressure. However, the pace of margin compression is likely to ease in the second half of 2025, with the impact of further Fed cuts expected to be less pronounced.

With these headwinds, banks need to double down on operational efficiency — starting with a resource they already have in abundance: data. With the right architecture, well-managed data becomes a lever for automation, fraud prevention, and AI-powered insight generation, unlocking long-term resilience.

Transforming Customer Relationship Management, delivering empathy at scale

Delivering hyper-personalized customer experiences used to be a costly endeavor, requiring manual collation of archetypes and customer profiles, alongside tedious forecasting of customer trends that are subject to human error. However, today, banks have the resources to turn data into a powerful source of insights, delivering customer empathy at scale with AI.

Modern data and AI capabilities allow banks to consolidate previously-siloed customer information scattered across legacy systems, branches, digitaltouchpoints, and other locations. AI models trained on this unified dataset can generate a holistic view of customers at top speed, giving customer servicing teams valuable insight into customer needs, behaviors, and risk profiles. This, in turn, drives more accurate delivery of customized services, in turn boosting customer retention, loyalty, and ultimately, revenue growth.

Across all this, security is a paramount layer that needs to underpin all of these operations. Governments across the Asia Pacific are tightening their laws regulating how banks handle personal data. It is increasingly critical to deploy data platforms that are embedded with governance and privacy controls.

OCBC exemplifies this use case. Together with Cloudera, the bank built a data platform in a private cloud environment to support real-time analytics and AI-driven decision-making, enabling personalized banking services, and faster and more efficient transactions. At the same time, the bank could maintain strict compliance with data protection regulations in Singapore and the markets that they operate in. OCBC’s move to revolutionize the way data is used demonstrates how well-architected infrastructure can drive measurable business outcomes.

UOB is taking a similar path. The bank recently entered into a strategic partnership with Cloudera to strengthen the quality and governance of its enterprise data. By tackling foundational challenges like metadata consistency and data quality remediation, UOB is laying the groundwork for trusted, GenAI-ready data across its operations. This initiative not only accelerates project delivery and improves data accessibility but also ensures that AI adoption aligns tightly with regulatory and business requirements, turning data discipline into a driver of long-term value.

Fighting fraud with unified intelligence

As digital transactions rise, so does the risk of fraud, a challenge that’s hard to address and made more difficult to combat due to fragmented systems. Traditional fraud monitoring systems, which rely on rule-based logic and batch data, are no match for today’s sophisticated threats.

A data platform enables banks to unify streaming data, behavior logs, and historical risk models in one environment. This consolidated view allows fraud teams to detect anomalies in real-time and act before losses occur. It also reduces false positives, cutting investigation costs and minimizing customer disruption.

Enabling governance in the age of GenAI

As generative AI becomes embedded across banking, from credit scoring to virtual assistants, the risk landscape grows more complex. To scale GenAI responsibly, banks must move from siloed governance tools to an integrated approach. Embedding control, compliance, and risk signals directly into the data platform allows banks to monitor usage in real-time, audit model behavior, and respond swiftly to emerging risks.

Bank Negara Indonesia (BNI) is setting a strong precedent. By deploying Cloudera AI Inference to run large language models on-premises, the bank retains full control over sensitive data while ensuring compliance with Indonesia’s regulatory framework while supporting BNI’s data sovereignty goals. Consolidating data ingestion, analytics, machine learning, and governance into a unified platform, BNI is establishing an AI foundation that is both scalable and secure.

As margins tighten, profitability will rely more on operational discipline and the ability to leverage data for banking transformation. The right data and AI platform that unifies intelligence across the enterprise and embeds governance at every layer empowers banks to move fast, stay compliant, and scale AI with confidence. In today’s AI era, well-governed data is a strategic advantage.

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