Hong Kong’s fintech story is a resounding success: 95% of banks are already using some form of it. However, the recent HKMA Fintech Promotion Blueprint reveals a sobering reality: high fintech adoption in Hong Kong does not equal deep transformation.

With the release of the Blueprint, the regulator is now addressing this effective adoption gap head-on. The Blueprint provides a diagnosis of the industry’s current bottlenecks and a practical guide, with initiatives, for turning tech usage into measurable business value.

The Shift Toward Deep Fintech Integration

When the HKMA published its Tech Maturity Stock-take findings in 2025, it indicated that 95% of surveyed banks in the city, including all retail banks, had adopted some form of fintech.

However, there is a more compelling finding, which seems to be the premise of the HKMA’s newly released Fintech Promotion Blueprint. The Blueprint informs,

“Most banks implement fintech solutions as standalone tools that operate in isolation and do not fundamentally transform existing processes or fully integrate into core operating models. This is a particularly important finding as widespread adoption of fintech depends on these technologies delivering tangible, ROI-driven business value.”

In simple terms, high fintech adoption may not produce deep fintech adoption, the kind that transforms how a bank operates, generates measurable returns, and reaches customers.

This is what the HKMA is now moving towards.

The Hong Kong Fintech Blueprint, released in February 2026 with KPMG and Quinlan & Associates, lays out a detailed strategy for accelerating advanced fintech adoption across the city’s financial services industry.

It identifies five tech priorities: AI, distributed ledger technology, high-performance computing, data excellence, and cyber resilience and structures its response around 13 specific initiatives grouped into three strategic dimensions.

What emerges from a close reading of the Blueprint and its supporting data is a diagnosis of the challenges that stand between where Hong Kong’s banking sector is today and where the city wants it to be.

The Fintech Adoption Gap

The Tech Maturity Stock-take, conducted in 2025, provides some of the most granular public picture to date. AI adoption, for example, has grown from 59% of surveyed banks in 2022 to 75% in 2025, a meaningful progress. Meanwhile, adoption of distributed ledger technology (DLT) grew from 30% in 2022 to 45% in 2025.

High-performance computing, which the Blueprint defines to include supercomputing and quantum computing infrastructure, sits at 23% today, with HKMA projections suggesting it could reach 61% by 2028.

Quantum computing’s current adoption rate, meanwhile, is at 7% and is mostly focused on exploration and testing phases.

Why Has Adoption Not Translated Into Fintech Transformation?

The Tech Maturity Stock-take asked banks to identify the biggest obstacles that came their way in expanding fintech adoption.

The most commonly cited barrier was high implementation costs, which tend to accompany larger-scale fintech adoption, cited by 75% of participating institutions.

This challenge was acute for smaller institutions with constrained financial and human capital. One bank noted in the Blueprint,

“Banks have limited resources and may not be able to afford the introduction of AI or big data models.”

Close behind, at 73%, were those concerned about the risks associated with new tech: security vulnerabilities, operational disruptions, and regulatory compliance uncertainty.

Next, the Blueprint sheds light on the fact that many Banks in Hong Kong still operate on legacy core banking systems, with some dating to mainframe architectures designed for batch data-output processing.

Systems like these are not built for real-time as they process data in scheduled batches rather than continually. This, in turn, creates a mismatch with the current demands of modern fintech applications, like a fraud detection system powered by AI that needs the latest data flows.

Migrations away from these systems are expensive, time-consuming and risky. The Blueprint estimates that the costs of DLT integration and migration ‘may often exceed the cost of maintaining existing legacy systems’. This calculation, rationally, leads many institutions towards delays.

For the HKMA’s ambitions around advanced technology adoption to be realised, this infrastructure problem has to be confronted. The Blueprint indicates that its ambitions extend beyond the short term, describing itself as a guide for efforts over the next few years under HKMA’s broader Fintech 2030 strategy.

Bridging the AI Talent Gap With A Practical Roadmap

A survey by the Hong Kong Institute for Monetary and Financial Research (HKIMR) cited in the Blueprint found that approximately 76% of institutions report technical skill gaps in generative AI development and use. This is a large percentage, but it is the quality of the shortage that the HKMA identifies as the deeper challenge. It shares,

“More critically, the survey identifies a severe shortage of professionals possessing both technical expertise and business acumen, particularly for risk management roles requiring the rare combination of GenAI proficiency, financial regulation knowledge, and local market operations experience, a skillset seldom cultivated in current training programmes as highlighted in the report.”

Closing the AI talent gap requires deliberate capability building across two fronts: technical expertise and business adoption.  On the technical side, organisations need structured up-skilling programs that cover model development, best practices, secure deployment and explainability.

Next, on the business side, training should focus on practical application. Teams must learn how to translate AI-driven insights into better strategic decisions, stronger product innovation, and responsible, secure usage.

In response, the HKMA’s Competency Development Support initiative is designed to equip industry practitioners with the skills they need while clearly defining the core competencies required too. By setting these benchmarks, organisations can develop focused talent programs that build these skills effectively.

The HKMA also aims to establish a knowledge repository within the hub to enable “sharing of specialised technical know-how.” The Blueprint indicates,

The repository will provide financial institutions and fintech solution providers with practical, ready-to-use resources and real-world examples to accelerate the adoption of advanced technologies.

Ultimately, by formalising these competencies and providing a centralised knowledge hub, the HKMA is providing a practical roadmap.

AI’s Catch-22 Is The Biggest Barrier to ROI

Another interesting facet from the Blueprint is the revenue circularity problem in AI adoption. According to the HKIMR survey, most generative AI deployments in Hong Kong remain internal and non-customer-facing.

The Blueprint attributes this to a set of concerns,

“This cautious approach stems from fundamental reasons including banks’ requirement for greater trust in accuracy before deploying GenAI in revenue-sensitive areas, the inability of current models to replace front-office expertise, and widespread scepticism regarding ROI from complex models, given these limitations.”

This creates a Catch-22 situation. Banks hesitate to deploy AI in customer-facing roles because the return on investment (ROI) is uncertain. The ROI remains uncertain as deployments are confined to internal and limited use cases. Deployments stay internal as banks possibly lack the confidence to push further.

Demonstrating meaningful returns from advanced AI requires a level of risk-taking that institutions are reluctant to embrace, precisely because the returns are unproven. GenAI models may also produce hallucinations, plausibly yet factually incorrect outputs, that can mislead users and erode trust.

Breaking this cycle is one of the objectives behind the Industry Showcase Workshops and Publications initiatives. The aim is to present concrete case studies of successful AI deployments, both within Hong Kong and internationally, to illustrate what measurable returns look like in practice.

Peer evidence can carry more weight with risk committees than regulatory encouragement. However, the relevance of those case studies may be the ultimate determiner of whether the initiative shifts behaviour.

Turning Fintech Momentum Into Institutional Change

The Fintech Promotion Blueprint describes real problems with reasonable specificity, proposes targeted interventions, and acknowledges that implementation will require thorough planning, dedicated resources, and collaborative efforts across multiple sectors.

The limitations talked about are mostly situational. What the Blueprint does establish is a structured framework for moving a sector incrementally in the right direction. There are specific enough initiatives to be measurable and flexible enough to adapt as conditions emerge.

While Hong Kong has built a fintech ecosystem that is broad and well-developed, its next test will be embedding technology into the core of how banks operate, which could be the harder challenge.

The Blueprint is possibly the HKMA’s most detailed attempt yet to map what breaking through will take.

Featured image edited by Fintech News Hong Kong based on images by mohammadhridoy_11 and obrako0 on Freepik