Transforming AI strategy into advantage in APAC lending

APAC banks are keeping up as competition intensifies by improving lending decisions through shifting AI experimentation to execution.

Artificial intelligence (AI) is rapidly reshaping the lending landscape in Asia Pacific. However, the gap between ambition and implementation remains significant. Whilst many banks are investing in AI capabilities, only a few of them have successfully embedded these technologies into their day-to-day operations.

According to a recent report by Moody's, in collaboration with Asian Banking & Finance, 18% of Asia Pacific bank executives say their organisations are using generative AI in production. This finding underscores how the race is about those who can execute AI effectively, and no longer about who is experimenting with it.

Competitive differentiation

AI in banking was once viewed only as a tool for automation and cost efficiency. But now, it has been repositioned as a driver of growth and competitive advantage.

As Omar Akkor, Senior Director of Product Strategy at Moody’s, explained in an interview with Asian Banking & Finance, banks are no longer looking at AI as a back-office efficiency tool.

“Initially, they explored AI primarily to enhance operational efficiency and reduce cost, but they are now increasingly leveraging it as a strategic competitive tool to drive growth, improve customer experience and develop innovative financial products,” he said.

This shift is reinforced by the Moody’s survey, which shows that competitive positioning has overtaken cost efficiency as the driver of AI adoption. In SME and commercial lending, banks are increasingly turning to AI to refine credit decision-making.

“Banks are now using AI to sharpen credit decisions, differentiate crowded SME and commercial and industrial segments, and defend their market share,” Akkor added, noting that this technology no longer serves as a support function but a central factor that equips banks to compete and grow.

Bridging the gap between strategy and execution

Despite strong interest, many banks face challenges in implementing AI at scale, and Moody’s report highlights that the key obstacle is the complexity of execution.

Banks must contend with high upfront costs associated with infrastructure and talent, as well as the difficulty of integrating AI into legacy systems that were not designed for modern data-driven technologies. At the same time, they must operate within strict regulatory frameworks that demand transparency and data privacy.

To overcome these challenges, the strength of a bank’s data and governance framework is crucial. AI systems cannot deliver reliable outcomes without high-quality and connected data.

“Data is the foundation of AI-enabled lending. AI is only as effective as the data behind it,” Akkor said.

Also equally important is governance, which supports the ability to scale AI initiatives across the organisation.

“Without clear roles and review processes, banks move slower and face greater internal frictions,” he added.

Despite this, according to the survey, only 57% of banks have formal AI governance structures in place.

Turning AI into real-world lending impact

As banks work to overcome these barriers, the focus is shifting from pilot projects to full-scale deployment. The Moody’s report points to growing momentum in areas such as credit origination, credit assessment, and portfolio monitoring.

Moody’s is supporting this transition by helping financial institutions embed AI into their core lending processes using its “build, connect, and embed” approach.

“We provide trusted data, analytical tools, and decision-grade intelligence that help them make more consistent and informed lending decisions across the whole customer lifecycle,” Akkor stated.

Through this, banks can deploy practical AI applications, including automated credit memos, early warning systems, and AI-driven scorecards — tools that help credit professionals scale operations.

At the same time, the Moody’s report shows that leading banks distinguish themselves by aligning AI initiatives with business outcomes; investing in strong and connected data ecosystems; and adopting hybrid strategies that combine internal capabilities with trusted external partners. Just as importantly, they implement robust governance frameworks that support innovation and control.

As AI continues to evolve, success in APAC lending increasingly depends on how effectively banks can turn strategy into execution. Those poised to be key leaders are the institutions that can translate strategy into action by strengthening data foundations, embedding governance, and focusing on measurable outcomes.

To explore these insights in greater depth and learn how leading banks are turning AI ambition into measurable results, access Moody’s full survey report in collaboration with Asian Banking & Finance here.