Banking

The APAC advantage: How the region’s banks are building decision advantage through people and culture - not just technology

Insights from our 2026 Banking Study find APAC banks are not just investing in technology, they are simultaneously investing in the people, culture and organizational change that decide whether technology delivers.
 

Five takeaways

1.Competitive, regulatory and risk pressures are being felt more acutely in APAC
2.On technology, APAC is keeping pace - and in some areas leading
3.APAC's competitive edge is people and culture
4.APAC banks are training broadly, hiring specifically and partnering strategically
5.Delivery of technology is running ahead of peers

The same pressures, felt more acutely

In several dimensions, APAC banks are feeling 2026’s pressures more sharply than their peers in the US and Europe:

  • 55% of APAC banks cite the emergence of new and non-financial risk as a significant challenge - versus 46% in the US and 49% in Europe.

  • 51% cite tightening regulatory requirements on capital - significantly above 38% in the US and 45% in Europe.

  • 51% are experiencing increased competition from new banks and fintechs (US 52%, Europe 61%).

  • 51% identify legacy infrastructure as a constraint, and 50% cite fragmented  data systems - both above US and  European averages.

  • 53% cite rising fraud and sanction enforcement (versus US 44%)
"New entrants have the capability to cannibalize banks. They’re able to do the same thing a bank does, but they’re much nimbler and much cheaper.”
Chief Control Officer, APAC bank

The infrastructure challenge matters most, because it is the foundation on which every  other response - to competition, fraud, regulation, customer expectation - must be built.

49% of APAC banks describe their data as fragmented, unstructured or inconsistent - requiring 'cleansing' before it can be put to meaningful use, compared with 36% in the US and just 39% in Europe.

It suggests APAC banks are responding to comparable challenges from a starting position  that is, in several respects, more constrained.
 

The shared response: technology investment

APAC institutions have assessed these pressures and the investment response is well underway. The headline picture mirrors the global one:

  • 51% of APAC banks are investing in technology and AI for better workflow integration (US 47%, Europe 47%).

  • 49% are investing in AI for data analytics (US 46%, Europe 58%).

  • 46% are investing in new and innovative service delivery to meet rising customer expectations (US 43%, Europe 41%).

On the use of agentic AI specifically, APAC is ahead: 82% of banks report that it is in use across some or most teams – compared with 74% in the US.

“By being able to easily access data used in a decision, it becomes a defensive weapon if you need it. Reporting within a bank, and to regulators, is one of the toughest things in the industry right now.”
Head of Wholesale Lending, APAC bank

But where this commentary usually ends, the more interesting story in APAC is just beginning.

The differentiator: people, culture and organizational change

The defining feature of the APAC response is not its technology spend. It is the recognition that technology delivers efficiency only when the organization around it is ready to use it.

Across the region, three deliberate moves are shaping that response: APAC banks are upskilling the existing workforce, hiring the specialist talent needed to lead the change, and blending in-house capability with strategic external partnerships. Each is happening at materially higher rates in APAC than in the US or Europe.

Train broadly

38% of APAC banks are making cultural changes to move the organization toward a technology-enabled data business - meaningfully ahead of 32% in the US and 30% in Europe.

Open-text comments from the region frame the tension between overcoming ingrained human behavior and investing in people as a precondition to realizing the benefits of AI and technology:

“Everyone is not an IT engineer. Everyone is not an AI guy, so you need to invest in your people, down to the people who are in the branches, they should know what AI is and how it works. Investing in AI is for people and for data."
Senior executive, APAC bank

Hire specifically

42% of APAC banks are hiring senior staff with  data and AI experience to drive strategic change - compared to 31% in the US and 32% in Europe. A further 43% are hiring AI and data specialists (US 37%, Europe 38%). Recruitment that once focused on generalists is now targeted at specific specialist categories - a direct response to AI agendas in areas like digital lending:

“Earlier we were hiring only generalist people. But now we are hiring skilled people, particularly skilled to certain data or tech categories only.”
Senior executive, APAC public-sector bank

New positions are being built around data and specialized technology roles. The framing, however, is that AI augments judgment rather than replaces it:

“More and more roles - data scientists, data analysts are coming over. A human touch is required. The decision-making power should stay with the human.”
enior executive, APAC bank

Use partnerships strategically

Behind both the upskilling and the hiring sits a nuanced approach. APAC banks are deliberately blending in-house build with carefully chosen external partnerships - calibrating where to build, where to partner, and where to internalize what was first developed elsewhere.

32% of APAC banks are forming partnerships with  organizations in adjacent sectors - almost a third more than the 24% (US) and 22% (Europe). The mix of internal build and external partnership is deliberate, not default. The result is a coherent philosophy: train broadly, hire selectively, partner where it accelerates, and keep critical capability inside the perimeter.

Three reasons sit behind this approach:

1. To maintain strict control over sensitive data and ensure regulatory compliance

“We never want to go outside and collaborate with Google, Gemini or any other player. We try to build that in-house... we don’t want to share our data with any outside player. There is a regulatory aspect as well - that’s the reason the bank decided to have an in-house AI capability.”
Partnership Lead, Payments & Platforms, APAC

2. To gain and sustain competitive advantage through highly customized solutions that off-the-shelf products cannot provide

“For large international banks, most of it is built on third-party and internal systems combined. That’s the way it’s done, because otherwise the competitive advantage is gone. It needs to be quite customized to what the bank can do.”
Senior executive, APAC bank

3. To internalize technology capabilities first developed through fintech collaborations, while retaining the customer trust

“Banks are closing collaborations with fintechs because they have now understood the technology and deployed it on their own platforms. At then end of the day, the customer trusts a bank over a fintech - that is the advantage banks always have.”
Product Sales Lead, APAC bank

The payoff is clear

APAC banking leaders think their approach is working:

  • 47% strongly agree their organization places a high value on effective collaboration between internal teams (US 42%, Europe 43%).

  • 32% strongly agree they are succeeding in implementing technological and data change directives operationally - ahead of 25% in the US and 29% in Europe.


The benefits of this approach are surfacing in different functions. In compliance and financial-crime work, the gain is in reducing investigation timelines:

“The people sitting in Hong Kong might not have the same line of sight over the customer’s data as London - but payments need to be fast. If you suspect something, it can take hours to figure out. Overlaying AI means it can all be decoded rapidly.”
Chief Control Officer, APAC bank

In commercial lending, the benefit is in connecting functions that were previously working from different data sets. One APAC product sales lead put the link between data, teams, and time-to-decision plainly:

“We’ve cut our time to decision in half - not because the model has changed, but because we have finally connected data from origination, credit, and risk in one place. That visibility gives us the confidence to move faster.”
Product Sales Lead, APAC bank

In credit risk, the payoff is in the rhythm and breadth of daily decision-making. AI assembles the data picture across portfolios, geographies and markets; human committees translate that picture into decisions:

“For the credit committee package, we hold daily meetings to discuss regionally and globally all data issues - geopolitical risk, country risk, and market risk across the equity, bond and commercial paper markets.”
Senior executive, APAC bank

The technology has made the saving possible. It is the region's cross-functional and people-orientated approach - across data, teams, and functions - that has made it real.

To find out how we can support your institution in its data and transformation journey, get in touch today. 

Methodology

This whitepaper draws on data from an online survey of 348 senior banking decision-makers spanning the United States (45%), Europe (33%), and Asia-Pacific (19%), supplemented by 20 in-depth executive interviews across the same regions and functions. Participants comprised finance  (41%), compliance (22%), risk (19%), and lending (19%) functions, from regional banks to Tier 1 global institutions. 

Nearly half of respondents came from Tier 1 banks (large global or systemically important institutions, with assets typically exceeding US$500bn), 40% from Tier 2 (mid-sized regional and national banks), and 12% from Tier 3 (smaller community banks and specialist institutions). Traditional banks accounted for 70% of respondents, with fintechs and neo-banks comprising the remainder.

The research was conducted in February and March 2026 by independent research consultancy We Live Context on behalf of Moody’s, and is published in full in The intelligence edge: Banking’s new decision advantage (Moody’s, May 2026).  

All comparative statistics cited in this whitepaper are drawn from that dataset.


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