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
Networking
An informal evening to meet fellow participants and the faculty before the programme begins.
08:00 – 09:00
08:30 – 09:15
Big Picture
A rapid-fire intelligence briefing grounded in what is actually deployed, not what is promised. We move fast, stay concrete, and start from the room's own answers.
Facilitated by Emmanuel Daniel
09:15 – 10:45
Hands-On Lab
This session brings every participant to the same starting point: able to design and run an agent, a basic skill that will soon be assumed before any discussion begins. Most senior banking audiences have watched AI being demonstrated. Far fewer have configured an agent, watched it fail, and had to decide whether they would put their name to it. By 10:45 every table has a working AI agent doing a real piece of back-office banking work. It will be rough and it will get things wrong, and that is the point: its failures are the material for the rest of the day.
Output: agent v0.1 running one task end to end; your table's working document (completed Agent Opportunity Scorecard) started; the first two slides of your table's deck generated by AI; one agreed real banking use case; selected agent pattern.
Facilitated by Tavonga Muchuchuti
10:45 – 11:00
11:00 – 11:45
Tutorial
A briefing on the governance, liability and architecture questions practitioners are wrestling with now.
Discussion: map the three banking processes in your institution most exposed to agent disruption in the next 12 months — and three where you could deploy agents to gain competitive advantage.
Facilitated by Axel Winter
11:45 – 12:15
Dialogue
Exercise: each table allocates twelve pieces of work between people and agents, then names the human who supervises the agent and the limits on its permissions, spending and data access. Tables compare answers before the briefing begins.
AI agents take on work that used to belong to analysts, operations staff and relationship teams. Banks therefore face a new management task: running a workforce in which some of the workers are not human. This session looks at five questions:
Participants will leave with a practical view of what changes in supervision, performance management and governance when the team includes agents.
What do we tell our board?: each delegate writes one sentence on the hybrid workforce for their own board; three are read aloud and pinned to the Board Wall.
Facilitated by Prof. Juergen Rahmel
12:15 – 13:15
13:15 – 14:15
Tutorial
This session examines the most consequential architectural development in financial AI — the emergence of institution-wide foundational models trained on a bank's own proprietary event data: one shared backbone that replaces dozens of siloed models. We will work through the strategic and data governance implications for every bank in this room. Two cases show two different roads; the second matters most for this room.
Facilitated by Axel Winter and Prof. Juergen Rahmel
14:15 – 15:00
Design
Most discussions of AI in banking focus on use cases: fraud detection here, customer service there. This session addresses the harder question: what does a bank's entire technology architecture look like when it is rebuilt for an agentic, foundation-model world? The answer, emerging from practitioners at the most advanced AI banking programmes, dismantles assumptions that most boards and technology teams still take for granted.
Architecture workshop: apply the three-phase model to your institution. Where are you today? What is the primary constraint holding you in Phase 1? What would Phase 3 look like for your specific product and regulatory environment?
Facilitated by Axel Winter
15:00 – 15:30
Design
A digital bank adds AI to review applications faster. An AI-native bank designs credit, fraud and payments assuming machines will act first and humans will supervise. Banks that treat AI as a programme ask how many use cases they have deployed. AI banks ask how many material decisions are now machine-supported, and how they are governed. This session is about designing that answer before scale.
Exercise: list the decisions your table's agent makes, from the first lookup to the last action. For each one: is it reversible, what is at stake, how often is it made, and can you tell afterwards whether it was right? Those four tests decide everything that follows.
What do we tell our board? he first material decision in your bank that should move to "AI with exceptions", and who signs the mandate.
Facilitated by Emmanuel Daniel and Prof. Juergen Rahmel
15:30 – 15:50
15:50 – 17:00
Hands-On Lab
This second hands-on session is the most valuable for this room, because everyone in it is accountable for something. Business delegates now drive and technical delegates interrogate.
Output: agent v0.5 with an audit trail; slides three to six of your table's deck drafted by AI from the working document.
Facilitated by Tavonga Muchuchuti
17:00 – 17:30
Dialogue
Each table in three sentences: what our agent does, what broke, what we fixed.
Facilitated by Tavonga Muchuchuti and Emmanuel Daniel, with delegates
19:00
07:30 – 08:30
09:00 – 09:30
Dialogue
Exercise: your AI agent declined 400 SME loans overnight. Which executive answers for it? Each table names the person in its own bank, then places its institution on the spectrum from central AI lab to embedded squads to hybrid. The two ends explain their choice.
What do we tell our board?: one sentence each on who should own AI in your bank; three read aloud.
Facilitated by Prof. Juergen Rahmel
09:30 – 10:00
Dialogue
Exercise: bring-your-own-pilot. One delegate per table describes a live AI pilot from their own bank in two minutes, and the table runs a pre-mortem: the three most likely reasons it dies before production.
What do we tell our board?: one sentence each on why our pilots stall; three read aloud.
Facilitated by Prof. Juergen Rahmel
10:00 – 10:30
Dialogue
Exercise: the Bias Hunt. Each table inspects its own agent for one of four entry points for bias (credit scoring, collections prioritisation, fraud flags, marketing eligibility) and writes the fix into its guardrails before the briefing.
What do we tell our board?: one sentence each on the bias your board is not asking about; three read aloud.
Facilitated by Prof. Juergen Rahmel
10:30 – 11:00
11:00 – 11:30
Dialogue
Exercise: Regulator's Question Time. Two delegates answer for their own bank on a live question; the regulator is played by a delegate from a different jurisdiction, and the room scores the answers.
What do we tell our board?: participants write one "plumbing fix" each to take back to their institution; three read aloud.
Facilitated by Prof. Juergen Rahmel
11:30 – 12:30
Tutorial
AI has fundamentally changed the threat landscape. The attack surface is now cognitive as well as technical, and foundation models and agentic architectures create vectors that conventional security frameworks were not designed for.
Facilitated by Axel Winter
12:30 – 13:30
13:30 – 14:30
Board Simulation
Delegates are divided into two or three boards, each governing a different bank in the same fictional developed market: a digital-only bank, a mid-sized bank and, if numbers allow, the largest bank in the country. Each board receives the same paper from management asking it to approve a five-year AI strategy, but each bank starts from a very different position, and all of them compete for the same customers.
Output: one signed board resolution per bank, carried into the Capstone and the Showcase.
What do we tell our board?: one sentence each on the question from this meeting that your own board has not yet answered.
Facilitated by Emmanuel Daniel, with all faculty in role
14:30 – 15:00
15:00 – 16:30
Capstone
This is the session the Bootcamp has been building towards. Building an agent is not the hard part. Getting predictable behaviour under conditions you did not anticipate is. This session proves your agent, turns it into a business case and finishes the deck you will present.
Output: your table's PowerPoint deck and working agent, and your personal board pack, submitted by 16:20.
Facilitated by Tavonga Muchuchuti and all faculty in role
16:30 – 17:00
Showcase
The final session is where the learning becomes accountability. Each table presents its own AI-built PowerPoint deck and runs its agent live. No pre-prepared slides. What you built is what you present, and every member of the table speaks.
Facilitated by all faculty
17:00
Close
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