2026 Agenda

1100 - 1105

Opening Remarks

1105 - 1125

Can Your Core Keep Up? Infrastructure for the Agentic Era

Embedded finance needs real-time responses. AI agents will transact autonomously. Fraud defences must run at machine speed. None of that works on a core built for batch processing. Big-bang replacement has failed enough that the industry is moving on. The question is no longer replace or wrap — it's whether progressive modernisation can close the gap between market demand and core capability before it becomes permanent.

Big-bang replacement is giving way to sidecar and strangler-fig models. Which MENA banks are running dual cores and what are they learning?
AI agents and embedded platforms need real-time event-driven responses. Can a wrapped legacy core deliver that, or is it a ceiling?
Progressive modernisation sounds safer but takes years. How do banks maintain competitive pace while migrating piece by piece?
The market is moving toward composable, cloud-native architecture. What should a CIO prioritise when evaluating a core partner?

1125 - 1135

How We Solved...

A practical case study on a real industry challenge, the approach taken, and results achieved.

1135 - 1155

The AI Gap: Why Pilots Aren't Becoming Production

MENA banks are investing heavily in AI — from credit scoring and chatbots to document processing and fraud detection. Pilot projects fill boardroom slides and innovation reports. But the number of banks running AI in production, at scale, with measurable P&L impact is still small. The question every CIO faces: is the gap technical, organisational, or both — and what would it take to close it?

Most AI pilots never reach production. Where do banks most often underestimate the effort required to move from pilot to production?
AI in credit decisioning means algorithms saying no to customers. What governance frameworks can stand up to regulator scrutiny?
Where is AI delivering measurable ROI in production today — and what conditions made those deployments possible?
MENA faces an acute shortage of data engineers and ML specialists. Build internally, outsource, or partner with fintechs?

1155 - 1205

How We Solved...

A practical case study on a real industry challenge, the approach taken, and results achieved.

1205 - 1225

The Invisible Bank: When Your Product Lives on Someone Else's Platform

Banking is disappearing into other people's platforms. A customer gets a loan at checkout, insurance at point of booking, a savings product inside a ride-hailing app — and never opens a banking app. Open finance frameworks across the MENA region made this possible. The question is no longer whether to build the APIs. It's whether banks are becoming invisible infrastructure — powering products they no longer own, for customers they no longer see.

When a retailer offers lending at checkout using your balance sheet, who owns the customer — the platform or the bank?
Super apps are embedding payments, credit and insurance into daily transactions. What revenue still belongs to the bank?
Some banks are choosing to become infrastructure providers. Is Banking-as-a-Service a growth strategy or a race to the bottom?
If banking becomes invisible, brand disappears with it. How does a bank build loyalty when the customer never sees them?

1225 - 1235

How We Solved...

A practical case study on a real industry challenge, the approach taken, and results achieved.

1235 - 1255

When the Customer Has the AI: Agentic Banking and the End of Inertia

Banking has always relied on one invisible advantage: customers don't move. Deposits stay because switching is hard. Products renew because comparison is effort. But AI agents are starting to act on behalf of customers — optimising rates, reallocating funds and choosing providers based on logic, not loyalty. When the customer's AI doesn't care about your brand, only your price and speed, what happens to the business model built on inertia?

AI agents can already move deposits between banks based on yield. What happens to funding costs when customer inertia disappears?
If an AI agent selects products on price and speed alone, what levers does a bank have left to defend its customer relationships?
Banks are building their own automated agents while customers deploy competing ones. Who controls the interaction — and who sets the rules?
Regulators haven't caught up with autonomous financial decisions. Who is liable when an AI agent moves money the customer didn't expect?

1255 - 1305

How We Solved...

A practical case study on a real industry challenge, the approach taken, and results achieved.

1305 - 1325

When the Attacker Has AI Too: Fraud, Deepfakes and the New Arms Race

The threat has changed. Attackers now deploy AI agents that open mule accounts autonomously, deepfake video calls that impersonate executives, and fraud-as-a-service platforms sold with onboarding guides and performance guarantees. At the same time, banks must learn to authenticate not just customers but the AI agents acting on their behalf. KYC was built for humans. The question now is what security looks like when both sides are machines.

Fraud has been industrialised. Deepfake toolkits are now sold with customer support. How do banks defend against factory-scale attacks?
A deepfake video call cost one company $25 million. When you can no longer trust what you see or hear, what verification still works?
Customers are sending AI agents to transact on their behalf. How does a bank tell a legitimate agent from a hostile one?
Attackers use AI to probe defences at machine speed. Are banks structured to respond at the same pace — or still operating in human time?

1325 - 1330

Closing Remarks

THE INDUSTRY'S TOP VOICES