The banking sector in Asia is experiencing a profound shift driven by artificial intelligence, with agentic AI—systems capable of autonomous planning, reasoning, and action—emerging as a key transformer.
Coming into 2026, analyses from McKinsey, Capco, and others highlight rapid investments in AI, enabling hyper-personalisation, operational efficiency, and new revenue opportunities amid supportive regulatory environments in markets like Singapore and Hong Kong, and to varying degrees across the rest of Asia.
As artificial intelligence moves from experimental novelty to strategic necessity, banking leaders across Asia face mounting pressure to embed AI responsibly—not just in algorithms, but in organisational culture.
For CIOs, chief data officers (CDOs), and heads of digital transformation, the challenge is twofold: accelerating value through AI while ensuring its safe, ethical, and compliant deployment across front-, middle-, and back-office functions.
Targeting measurable outcomes and building ecosystems
Banks are pursuing tangible benefits such as enhanced fraud detection, accelerated loan approvals, and increased customer lifetime value.
Mohammed Rahim, group chief data officer at Standard Chartered, observes: "Across the industry, we are seeing a focus on ROI in terms of productivity and operational efficiency." He quickly points out, however, that there is yet no collective consensus on how this should be measured.
He emphasises the importance of driving AI literacy and adoption to help achieve business impact.
"Our approach is to build a strong ecosystem around AI – this also includes having targeted client-centric use cases, an operating model for responsible AI delivery, the right data architecture, research capabilities and partnerships, as well as robust AI safety governance." Mohammed Rahim
The strategic shift: From automation to reimagination
AI in banking is no longer about automating routine tasks. According to IBM, institutions are now leveraging generative AI and agentic systems to "reimagine customer journeys" and unlock new revenue streams—such as hyper-personalised wealth advice or predictive cash-flow services.
Yet, this ambition is tempered by regulatory complexity, model bias risks, and workforce readiness gaps.
Rahim captures this duality: "We look beyond the short-term benefits of process automation, prioritising transversal and highly applicable solutions across a vast range of functions."
This signals a maturing approach—one where AI is architected as a reusable, scalable capability rather than a siloed pilot.
AI literacy as a foundational capability
A recurring theme in 2025–2026 banking trends is that AI literacy must span the entire enterprise. IBM's Global Outlook for Banking 2025 stresses that banks "can't afford to delay reskilling workers who are often stuck in routine tasks".
Bridging capability gaps through tailored initiatives is crucial, as exemplified by DBS Bank's extensive reskilling programmes, which are projected to have a significant economic impact.
Without widespread fluency in AI concepts—from prompt engineering to ethics-by-design—organisations risk deploying powerful tools without the judgment to wield them wisely.
Rahim reinforces this: "AI literacy does not just mean understanding how AI works, but also how to work with AI… That is genuine AI literacy."
He advocates for tailored upskilling—from executive workshops to cross-functional "AI co-pilot" certifications—so employees at all levels can "identify use cases with the biggest business impact" and contribute to responsible governance.
For CIOs, this means AI literacy is not an HR initiative; it's a strategic enabler of innovation and risk mitigation.
Governance as an innovation catalyst
In Asia's fragmented regulatory landscape—from Singapore's Model AI Governance Framework to Hong Kong's SFC guidelines on algorithmic trading to India's RBI FREE-AI guidelines emphasising fairness and explainability, clear accountability for ethical AI is essential. Banking across one or more markets highlights the potential for compliance to become a bottleneck.
Yet Rahim flips this narrative: "We believe in governance as an enabler of innovation. AI safety and explainability are not an afterthought but are always embedded at the forefront of our product development process."
Standard Chartered's structured AI operating model assigns clear accountability: technical product owners, dedicated squads for adoption roadmaps, and an AI Safety Council that assesses risks per use case and reports to data governance teams familiar with local regulations.
This prevents siloed decision-making and ensures consistency across APAC jurisdictions.
"Having these holistic frameworks keeps us aligned and allows us to confidently harness the transformative potential of AI tools for sustainable growth," said Rahim.
IBM echoes this, noting that banks must adopt "a comprehensive strategy for approaching AI" with transparent governance to "maximise business value while mitigating risk".
Generative AI in customer-facing applications
Banks are increasingly embedding GenAI in customer interactions—virtual assistants that handle complex queries, budgeting tools that anticipate needs, or loan agents that guide applications end-to-end. But with this comes heightened scrutiny.
As IBM warns, "generative AI models do not reason or 'understand' their outputs," making explainability critical in regulated settings. Moreover, cybercriminals now use AI to mimic voices or craft convincing phishing scams—raising the stakes for real-time AI-powered fraud detection.
Rahim's response is pragmatic: Standard Chartered's GenAI platform, SC GPT, serves both internal productivity (used by ~80,000 colleagues) and customer-facing functions—"but can also be incorporated in the tech stack for a wide range of business functions such as information search or knowledge management."
Crucially, every deployment undergoes impact assessments, risk scoring, and review forums under its "AI safety workflow."
The human-centred imperative
Technology alone won't secure a sustainable advantage. Rahim stresses that "it requires strategic thinking at every level, and demands a robust ecosystem of technical architecture, workflows, and knowledge sharing."
This human-centred ethos aligns with IBM's finding that customers "want seamless digital banking experiences" that still "feel personalised and uniquely human."
For banking leaders, the lesson is clear: AI transformation succeeds not by replacing humans, but by augmenting them—with the right tools, literacy, and ethical guardrails.
Looking ahead: From compliance to competitive edge
In 2026, CIOs and CDOs who treat AI governance as a constraint will fall behind. Those who, like Standard Chartered, embed safety, transparency, and literacy into their AI DNA will turn regulatory complexity into a differentiator.
As Rahim concludes: "Moving collectively towards this nuanced definition of AI literacy is precisely what enables financial institutions to stay competitive."
