The Middle East's payment landscape is entering a decisive new phase. Across the GCC, digital and real-time payments are becoming core financial infrastructure, not just consumer convenience.
Saudi Arabia has already crossed 79% non-cash retail transactions, ahead of its Vision 2030 milestone, reflecting how quickly the region is moving towards faster, more connected payment ecosystems.
For banks, payment processors and financial institutions, this shift creates both opportunity and responsibility. The same rails that are enabling instant commerce, financial inclusion and smoother cross-border value movement are also expanding the surface area for fraud. The risk is becoming more sophisticated as artificial intelligence gives fraudsters the ability to automate attacks, mimic legitimate behaviour and scale scams faster than traditional controls can respond, writes Sandeep Crasta, Business Head, FSS (Middle East).
Sumsub’s Identity Fraud Report 2025–2026 found that fraud rates in the Middle East rose 19.8% in 2025, even as sophisticated, multi-step fraud attacks grew 180% year-on-year globally. The signal is clear: the challenge is no longer only about stopping more fraud but stopping more complex fraud. Fraud today moves faster, runs constantly and adapts as it goes. At the core of this transformation is artificial intelligence.
Fraud is now an engineered operation
For years, fraud prevention was largely built around identifying known patterns. A suspicious transaction, an unusual login, a mismatched device, or a sudden change in behavior could trigger a review. AI, however, has changed the economics of fraud. What once required time, technical skill, and manual effort can now be automated. Fraudsters can generate synthetic identities by combining real and fabricated data, making it harder for onboarding and KYC systems to separate genuine customers from manufactured profiles. This is particularly relevant as banks, wallets, and fintech platforms across the GCC expand remote account opening and digital identity-led services.
Deepfake and voice-cloning attacks are adding another layer of complexity. Fraudsters can now imitate voices, faces and communication styles with growing accuracy. In financial services, that creates risk not only for retail customers but also for corporates, senior executives, and high-value transactions where social engineering can be used to trigger approvals.
Phishing is also becoming more sophisticated. Messages are no longer generic or easy to identify. AI can help create more personalised communication in local languages, tailored to specific markets, corridors, or customer behaviours. A scam targeting a remittance user, an e-commerce buyer, or a corporate finance team can now look far more credible than before.
The larger issue is that AI allows fraud to scale. It can mimic normal transaction behaviour, test systems repeatedly, adjust tactics, and run multiple attacks at the same time. In a real-time payments environment, this makes traditional, rule-based fraud controls increasingly insufficient.
Payment systems are countering with AI defences
As fraud becomes faster, the response also has to become faster. Banks and payment processors cannot rely only on controls that sit outside the transaction flow or operate after the event. In instant payments, the decision window is extremely short. Once a transaction is approved and settled, recovery becomes far more difficult.
This is where AI-led fraud defence becomes critical. Modern payment systems need to assess risk while the transaction is still moving. That means looking beyond the transaction amount or location and reading a wider set of signals: customer behavior, device patterns, transaction history, account relationships, velocity, timing, and contextual anomalies.
For the GCC, payments are becoming more interconnected. Domestic instant payment rails are scaling, cross-border flows remain significant, and financial institutions are modernizing infrastructure to support higher transaction volumes. Fraud defence cannot be treated as a separate compliance layer. It has to be embedded into the payment architecture itself.
The real challenge: speed versus accuracy
The hard part is not only detecting fraud. It is doing so without damaging the customer experience.
Consumers and businesses now expect payments to be instant, seamless and always available. Overly strict fraud controls can block legitimate transactions, frustrate customers and reduce confidence in digital payments. Weak controls, however, allow fraud to pass through and can damage trust at an ecosystem level.
This is the balance financial institutions must solve. Payment systems need to be fast enough to support real-time commerce, but intelligent enough to stop suspicious activity before it becomes a loss. That requires infrastructure that can process large volumes, detect anomalies instantly and make more accurate decisions without slowing down legitimate payments.
In practical terms, fraud management must operate closer to the transaction layer. It should support switch-level intelligence, real-time monitoring across channels and a unified view of customer and transaction behaviour. The future of payment security will depend less on adding more checks at the edge and more on building intelligence into the core.
From detection to prediction
Fraud management is now moving beyond detection. The next phase is prevention, and the phase after that is prediction.
Detection asks whether fraud has happened. Prevention asks how it can be stopped before loss occurs. Prediction goes further by identifying weak signals, emerging patterns, and likely risk before the fraudulent transaction is completed. This is where AI can change the role of payment security from reactive monitoring to proactive ecosystem protection.
For the Middle East, the region is simultaneously leading digital payment adoption and facing a more sophisticated fraud environment. Vision 2030, the UAE’s National Payment Systems Strategy and the broader ambition to build world-class digital economies all depend on trust. If consumers and businesses do not trust the rails, adoption slows. If institutions cannot protect the rails, scale becomes a liability.
Static defences no longer work in a dynamic threat landscape. As AI becomes more accessible, fraudsters will continue to evolve. But institutions that invest in adaptive, intelligent, and scalable systems will be better positioned to stay ahead.
The lesson is clear. Real-time payments need real-time protection. In today’s payments ecosystem, the fight against fraud is no longer simply human versus machine. It is increasingly machine versus machine. And only the smarter system wins.
