AICB Nexus as a Strategic Platform
It is a great pleasure to join you this morning at the inaugural AICB Nexus Conference. Let me begin by congratulating the Asian Institute of Chartered Bankers (AICB) and its partners for bringing together the Malaysian Banking Conference and the Bank Audit Conference under one platform.
Bringing these communities together reflects an important reality: the future of finance cannot be shaped through separate conversations. Innovation cannot succeed without governance. Governance cannot be effective without assurance. And neither can earn public trust without the other. It is timely that we bridge these conversations, recognising that innovation and assurance must increasingly advance together.
Innovation without trust is not progress
Today, AI is reshaping financial services. It is helping institutions detect fraud, assess credit and insurance risks, strengthen compliance, manage risks more effectively, and serve customers better. When optimised, AI can improve productivity, decision-making and access to financial services.
Understandably, much of today's discussion will focus on how quickly we can adopt AI. But perhaps the more important question is this: can we ensure that AI strengthens trust, preserves accountability and reinforces public confidence in the financial system?
After all, finance is built on trust. Trust that savings will be safeguarded. Trust that capital will be allocated efficiently. And trust that the institutions, governance and rules underpinning the system remain credible and resilient.
AI may transform finance. But trust will determine whether that transformation endures. That is why I would like to emphasise one simple yet powerful principle, “Innovation without trust is not progress”. This is not merely a statement about adoption of technology; it is about how we exercise leadership, how we uphold governance and ensure that the financial system we build remains trusted and firmly anchored in the needs of society.
If trust is to remain the foundation of finance in the age of AI, there are three priorities that deserve our attention. Allow me to frame these three principles today.
First priority: Purposeful innovation
First, innovation must be pursued with purpose. This is especially important as AI becomes more deeply embedded in financial services. Today, more than 70% of Malaysian financial service providers have implemented at least one AI application. Industry responses to our Discussion Paper on Artificial Intelligence in the Malaysian Financial Sector show that AI adoption is accelerating. Adoption is concentrating where there are opportunities for efficiency gains, productivity gains, and better risk management.
Thus far, financial institutions have focused on use cases that deliver internal value. The opportunity before us now is to move beyond this institution-centric lens. The next frontier is to use AI to solve problems that no institution can solve on its own.
Challenges such as scams, fraud and cyber threats require collective action. In the case of Malaysia, BNM, PDRM, financial institutions, PayNet and other partners have worked together to strengthen safeguards, share intelligence and protect customers. Trust is not built by one institution, but by an ecosystem working as one. This is innovation with purpose. Institutions should compete where markets demand it and collaborate where public interest demands it.
Second priority: Responsible and risk-aware innovation
Second, progress must be pursued responsibly and anchored in trust. We often speak about trust in AI as a technical challenge. One of model accuracy, data quality or system reliability. But in reality, it is a governance challenge.
As AI systems become more capable, they also become more complex. In many cases, AI models that offer the greatest analytical power are also among the most difficult to explain, validate or challenge. This presents new questions for every institution represented here today.
- First, how do we preserve accountability when decisions become harder to explain?
- Second, how do we ensure fairness when models continue to learn and evolve?
- Third, how do we govern technologies whose capabilities are advancing faster than our traditional control frameworks?
These are not technology questions alone. They are leadership questions.
As AI becomes more deeply embedded in financial services, the role of professional judgement becomes even more important. While machines may generate insights and support decisions, accountability must remain with those entrusted to govern and lead. Responsibility cannot be delegated to an algorithm.
For boards and senior management, this has profound implications: AI should not sit at the margins as a technology project. It belongs firmly on the board agenda. It is a matter of capital allocation and governance, anchored in clear business outcomes, measurable value and a defined risk appetite. The responsibility of leadership is to ensure that innovation strengthens, rather than weakens, trust.
To our colleagues in risk, compliance and internal audit: your role has never been more important. As systems become more complex and autonomous, assurance becomes harder – to be constructive and to be fair. It is no longer sufficient to confirm that controls exist. The question is whether institutions can explain outcomes, challenge decisions, and retain accountability in increasingly opaque systems.
Third priority: Innovation that brings progress for all
Last but not least, innovation must translate into progress for all. This begins with investing in people. The future of AI will be shaped less by technology than by talent. It is not only about technical skills, but governance capability, critical judgment and the ability to operate effectively where humans and machines interact. Without these capabilities, even the most advanced technologies will struggle to earn trust.
At the same time, AI is reshaping the nature of work. Roles are evolving, workflows are being redesigned, and job composition is changing. Preparing the workforce for this transition is not optional; it is a strategic imperative. Institutions must invest in upskilling and reskilling, while supporting their workforce through transition to ensure no segment is left behind.
AICB has an important role in building a future-ready workforce. Through the Future Skills Framework, it is helping professionals develop the AI literacy, ethical judgment and governance capabilities that will become increasingly important for the industry. We strongly support this effort and its continued evolution to meet the future needs of the industry.
Our approach
For BNM, our approach is clear: encourage innovation, uphold trust and ensure that progress benefits society. We neither constrain innovation prematurely nor leave it to unfold unchecked. Our stance is anchored in proportionality, parity and technology neutrality, ensuring innovation progresses responsibly and with confidence.
In practice, this means engaging industry early, providing clearer regulatory expectations, supporting responsible experimentation, and investing in shared infrastructure that enables innovation at scale.
Alongside AI, other developments will shape the sector. Our Open Finance framework establishes a foundation for secure, consent-based data sharing, with supporting infrastructure being developed with PayNet and the industry, and phased implementation from 2027. At the same time, our asset tokenisation roadmap is progressing into pilots through the Digital Assets Innovation Hub.
But we are clear that these are tools, not destinations. Their true value lies in whether they improve lives, strengthen resilience and deepen trust.
Looking ahead, we are shaping the next chapter of financial sector development through the Financial Sector Blueprint 2027 to 2030, developed in close collaboration with the industry, government and stakeholders to ensure it is grounded in real challenges and aligned with the evolving needs of the economy.
The future of finance will not be defined by how fast and sophisticated our technology is. It will be defined by whether that technology strengthens trust, broadens opportunity and serves society well.
That is the challenge before us. It is also our shared responsibility. And I believe that together we can deliver this outcome.
Facts Only
* The event was the inaugural AICB Nexus Conference.
* The conference brought together the Malaysian Banking Conference and the Bank Audit Conference.
* AI is reshaping financial services by helping institutions detect fraud, assess risks, strengthen compliance, manage risks, and serve customers better.
* Optimized AI can improve productivity, decision-making, and access to financial services.
* The central principle presented is "Innovation without trust is not progress."
* Priority one is purposeful innovation, suggesting moving beyond institution-centric use cases to collective action on challenges like scams and fraud across the ecosystem.
* Priority two is responsible and risk-aware innovation, focusing on governance challenges related to explaining decisions, ensuring fairness, and governing advanced systems.
* Priority three is innovation that brings progress for all, emphasizing investment in people through upskilling and reskilling to develop necessary capabilities.
* BNM's approach involves encouraging innovation while upholding trust, using proportionality, parity, and technology neutrality.
* Other initiatives include the Open Finance framework and an asset tokenisation roadmap.
Executive Summary
The conference brought together the Malaysian Banking Conference and the Bank Audit Conference under one platform, emphasizing the need to connect innovation and governance for the future of finance. The core argument is that progress in finance cannot occur without trust, and AI adoption must be managed with this principle in mind. The speaker proposes three priorities for ensuring innovation is trustworthy: purposeful innovation, responsible and risk-aware innovation, and innovation that brings progress for all.
Purposeful innovation requires moving beyond institution-centric value to collective action where public interest demands it, such as in combating scams and fraud through ecosystem collaboration. Responsible innovation addresses the governance challenge inherent in complex AI systems by focusing on accountability, fairness, and governing technology faster than existing frameworks. Finally, innovation must benefit everyone by investing in people through upskilling and reskilling to build the necessary human capabilities for navigating this technological change.
The speaker advocates for a regulatory approach that encourages innovation while ensuring trust is maintained through engagement, clear expectations, and shared infrastructure. This approach incorporates existing initiatives like the Open Finance framework and asset tokenisation roadmaps, positioning technology as a tool to strengthen resilience and trust rather than an end in itself.
Full Take
The narrative constructs a powerful ethical framework positioning trust as the necessary precondition for technological advancement in finance, reframing the AI discussion from a purely technical capability debate to a governance and societal responsibility challenge. The transition from viewing AI adoption as solely a question of speed or efficiency to framing it around accountability (innovation without trust is not progress) functions as a crucial rhetorical pivot.
The progression through the three priorities—purposeful innovation, responsible innovation, and inclusive innovation—follows a logical trajectory: defining *what* we innovate for (purpose), establishing *how* we govern it (responsibility), and determining *who* benefits (progress). This structure is designed to preempt technological determinism by anchoring AI development in established principles of fiduciary duty and social contract.
The implication lies in shifting the locus of control from purely technical experts to governance structures and human capability. The emphasis on leadership questions for boards regarding AI demonstrates an understanding that technology deployment is fundamentally a political and organizational decision, not just an engineering one. The pattern observed is a systemic resistance to viewing complex risks as purely quantifiable metrics; instead, the narrative forces the recognition of epistemic responsibility—the obligation to explain and justify outcomes in opaque systems.
The central tension rests between the imperative for rapid innovation and the necessity for deliberate, trust-anchored governance. The implied risk, which must be consciously managed by leaders, is that a focus solely on achieving efficiency or adoption may inadvertently erode the very foundation of financial stability if accountability is sidelined. This requires an ongoing commitment to embedding human judgment within algorithmic systems, moving beyond mere compliance to cultivating genuine systemic resilience.
Bridge Questions: If trust is the foundation, what specific, measurable metrics should boards use to quantify the 'trust dividend' generated by AI implementations? How can regulatory bodies effectively audit the 'collective action' necessary in an ecosystem without stifling competitive innovation? What structural changes are needed within traditional assurance roles to adequately govern autonomous, complex decision-making systems?
Sentinel — Human
The text reads like a carefully constructed, high-level speech designed to set a strategic direction based on established industry concerns regarding AI, trust, and governance.