We are living through the fastest technology transition in the history of finance. Reasoning models now outperform expert humans on medical and legal licensing exams. Multimodal AI reads documents, interprets charts and listens to calls in real time. Agentic AI systems execute multi-step tasks autonomously — booking, transacting, reconciling — without a human in the loop. And the cost of intelligence has fallen by more than 99% in three years.
For banks, this is not a background trend. It is a structural disruption arriving simultaneously on every front: customer experience, credit decisioning, fraud detection, compliance, treasury, wealth management and the operating model itself. The question is no longer whether AI will transform your institution — it is whether your leadership team will shape that transformation or be shaped by it.
This two-day Bootcamp is built around three convictions: that understanding must be current (the AI landscape of six months ago is already obsolete); that strategy must be hands-on (knowing about AI is not the same as knowing how to use it); and that decisions must be made. By the end of Day Two, every participant will have built a draft AI Bank concept — stress-tested by peers and ready to take to their board.
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Shape executive strategies for future banking
Lead technology-driven transformation
Strengthen decision-making and governance
Design customer-first product innovations
Manage data, ethics and trust
Drive organisational change
Expand peer networks across the region
Banking in an intelligent economy
Evolving technology architecture
Data governance and cybersecurity
Customer engagement and new products
Leadership and talent strategies
Fairness, ethics and trust
Regulatory models and public policy
The five developments below define the strategic context for this Bootcamp. They are not predictions — they are live deployments reshaping competitive advantage in banking right now.
The new competitive moat
Banks are moving from fragmented AI use cases to unified models trained on proprietary data. Competitive advantage is no longer the algorithm. It is the depth and structure of your data.
Banks are moving from fragmented AI use cases to unified models trained on proprietary data. Competitive advantage is no longer the algorithm. It is the depth and structure of your data.
Read MoreMoving beyond applications
AI agents are beginning to orchestrate banking processes across systems. Over time, business logic shifts out of applications and into an agent layer, changing how banks are built.
AI agents are beginning to orchestrate banking processes across systems. Over time, business logic shifts out of applications and into an agent layer, changing how banks are built.
Read MoreAI becomes the front end
AI is replacing traditional interfaces across enterprise systems. For banks, this changes how customers interact, how workflows operate, and how vendors integrate.
AI is replacing traditional interfaces across enterprise systems. For banks, this changes how customers interact, how workflows operate, and how vendors integrate.
Read MoreA near-term reality
Transactions will increasingly be negotiated and executed by AI agents. Customer journeys, pricing models and authentication frameworks will need to be redesigned.
Transactions will increasingly be negotiated and executed by AI agents. Customer journeys, pricing models and authentication frameworks will need to be redesigned.
Read MoreFrom principles to enforcement
AI governance is now being enforced through regulation. Boards must address model risk, explainability and accountability across all AI-driven functions.
AI governance is now being enforced through regulation. Boards must address model risk, explainability and accountability across all AI-driven functions.
Read MoreThe most consequential development in banking AI is not a chatbot or a copilot — it is the emergence of domain-specific foundational models trained on proprietary banking event sequences. Revolut's PRAGMA (April 2026) is the most advanced public example: a single pre-trained backbone that simultaneously outperforms task-specific models across credit scoring, fraud detection, lifetime value prediction, product recommendation and more. Stripe, Mastercard and Visa have all announced comparable models. The moat is no longer the algorithm — it is proprietary data at scale. Any bank sitting on comparable transaction volumes could build its own version. Most are not.
Read MoreToday's bank technology estate is built around vertical application silos — lending, treasury, payments, CRM — each a walled garden connected by brittle integration layers. A rigorous new practitioner framework describes the three-phase migration path out of this architecture: Phase 1, agents orchestrate across existing systems via API; Phase 2, new products are built as pure agentic processes and business logic begins migrating out of applications; Phase 3, applications flatten to data access layers and the concept of a 'banking application' becomes obsolete. The critical implication: data governance investment must precede architectural migration, not follow it.
Read MoreAnthropic's Model Context Protocol — now backed by OpenAI, Google, Microsoft and AWS under the Linux Foundation — has fundamentally changed how enterprise software is built. Major platforms including Slack, Figma and Asana now embed their interfaces directly inside AI chat windows. The app becomes a backend; the interface is owned by the AI. For banks, every customer touchpoint, every workflow tool and every vendor relationship must be re-evaluated against an agentic architecture.
Read MoreWhen a customer's AI agent negotiates with a bank's AI agent to arrange a mortgage, the entire customer journey, pricing logic, authentication framework and relationship model must be redesigned. Early agent-to-agent payment rails are already live on Stripe's Agent Commerce Protocol. Banks that have not begun designing for this world are already behind — and foundational models make the gap wider still.
Read MoreThe EU AI Act is now in force for high-risk applications including credit scoring, KYC and fraud systems. Bank Negara Malaysia and MAS Singapore have both issued updated guidance on model risk, algorithmic fairness and AI governance. Boards must understand what this means for model documentation, explainability requirements and third-party vendor oversight — especially when a single foundational model underlies multiple regulated functions simultaneously.
Read More
Chairmen and board members of traditional and digital banks
CEOs and senior management
Heads of strategic planning, innovation and business development
Heads of AI, data, analytics, cybersecurity, channels and distribution
Senior managers driving AI adoption across banking functions
Regulators and policymakers shaping AI governance in financial services
A structured intelligence briefing on live deployments and current developments, followed by facilitated discussion.
Participants work directly with AI tools on real banking scenarios. Laptops required. Output produced during the session.
Small groups interrogate a live architectural question — applying the three-phase core banking migration model or the PRAGMA paradigm to their own institution.
SmallFacilitated peer discussion using real-world scenarios and challenge cards.
Participants use AI to construct a real deliverable in real time — a workflow, agent design, policy draft or strategy document.
Groups design a functioning AI Bank concept, using AI to produce their presentation deck or prototype live — presented to peers at the close of Day Two.
18:00 – 20:00
07:30 – 08:30
08:30 – 09:30
Expert Briefing
A rapid-fire intelligence briefing grounded in what is actually deployed, not what is promised. We move fast, stay concrete, and challenge assumptions from the first minute.
09:45 – 11:00
Hands-On Lab | Laptops required
We skip basic prompting and go straight to the strategic question that matters most: what happens to your bank when your customers have AI agents, your competitors have foundational models, and your core banking architecture is still organised around application silos?
11:00 – 11:20
11:20 – 12:30
Tutorial
This session examines the live governance, liability and architecture questions practitioners are wrestling with now — not introductory framing.
12:30 – 14:00
14:00 – 15:15
Tutorial
This session examines the most consequential architectural development in financial AI — institution-wide foundational models trained on proprietary banking event sequences — and works through the strategic and data governance implications.
15:15 – 15:35
15:35 – 16:40
Design Workshop
Most AI-in-banking discussions focus on use cases. This session addresses the harder question: what does a bank's entire technology architecture look like when rebuilt for an agentic, foundational-model world?
16:40 – 17:30
Structured Dialogue
AI transformation fails most often not because of technology but because of organisational design. This session draws on case studies from institutions that have succeeded and those that have stalled.
17:30
07:30 – 08:30
08:30 – 09:45
Case Studies
A deep dive into the specific AI programmes underway at leading global institutions — not press releases, but operational realities, architectural choices, lessons learned and competitive implications.
09:45 – 10:00
10:00 – 11:00
Structured Dialogue
Ethics is not a governance layer you add after the system is built — it is in the architecture, the data and the objective function from day one.
11:00 – 11:20
11:20 – 12:20
Tutorial
AI has fundamentally changed the threat landscape. The attack surface is now cognitive as well as technical — and agentic architectures create new vectors that conventional security frameworks were not designed to address.
12:20 – 13:45
13:45 – 15:30
Capstone Project | Laptops required
Working in groups of 3–4, participants design a concept for an AI Bank — built from scratch on agentic infrastructure, or a transformation roadmap for their existing institution. The constraint: you must use AI to build your own presentation.
15:30 – 15:45
15:45 – 17:00
Hands-On Lab
The final session is where learning becomes accountability. Each group presents their AI Bank concept using the AI-built deck or application created in the Capstone Lab. No pre-prepared slides.
17:00
The programme is anchored and delivered by some of the most experienced professionals in the industry.
US$ 5,800
*Excludes airfare, visa fees, airport transfers and dinners
US$ 5,000
*Excludes airfare, visa fees, airport transfers and dinners