In an age where digital transformation is moving faster than ever, banks around the Middle East and North Africa (MENA) are forced to confront a growing and increasingly evolving threat: cybercrime and fraud. It’s not just about an increase in the number of incidents; it’s about smarter threats. Nefarious agents are utilizing more complex methods such as leveraging artificial intelligence (AI) to outsmart traditional IT security systems, using everything from deepfake-powered scams to AI-generated phishing campaigns along with social engineering strategies.

In the UAE alone, about 21% of cybersecurity incidents in recent years targeted banks and financial institutions, second only to government entities (Lemos, 2025). With costly breaches on the rise cybersecurity has become a top board-level concern. However globally, 71% of leaders report that small organizations can no longer adequately secure themselves against the growing complexity of cyber risks (WEF, 2025). It’s a high-stakes game and I have personally seen how AI and cybersecurity has taken the spotlight in board meetings and discussion with clients from across the GCC and Levant regions.

This urgency has forced MENA banks to explore AI-driven security solutions that can match the speed and complexity of modern threats, protecting both their customers and their bottom line. The conversation is no longer “if” we need AI-driven defenses—it’s how quickly we can deploy them, and how can we optimize them to adapt to the ever-changing tactics of nefarious agents

Artificial intelligence isn’t just helping plug holes in defenses, it’s defining the rules for how security is built into every layer of operations. Integrating AI into banking operations gives banks a real edge in regions where speed really matters. Having worked with several banks across the region, I’ve seen firsthand how traditional security models are starting to break under the weight of elaborate AI based threats.

For banks in the MENA region, where rapid digitalization coincides with heightened cyber threats, adopting AI-driven systems enhances operational resilience, reduces financial losses due to fraud, and boosts customer trust. AI not only fortifies security frameworks, it also fosters innovation, empowering banks to confidently pursue new digital business models and expansion opportunities.

AI defenses monitor account activity 24/7 and can react in seconds to anomalies, reducing the window of time attackers can exploit. AI-based user behavior analytics can spot an account takeover attempt at the moment it diverges from normal patterns and automatically disable the account, preventing fraud before it escalates. Early-adopting banks in the UAE report that AI systems have sharply reduced successful fraud incidents and enabled rapid intervention in potential cyber attacks​.

AI isn’t just a nice to have security upgrade, it’s a question of survival.

A simple example of successful AI usage in a cybersecurity context is during a next-gen digital onboarding process. With many regulators now strong encouraging or mandating digital onboarding, banks have been able to benefit from using AI-powered systems to prevent fraud before it has a chance to run rampant. Next gen AI-powered onboarding and eKYC minimizes friction for customers looking to open accounts, while providing a secure backend environment to recude the risks for attacks. Such solutions utilize a variety of AI enabled features such as next-gen biometrics, deep ID document validation, Arabic language detection, glare reduction in ID photos, all ensuring a secure authentication and verification of a new customer. An example of this application can be the digital onboarding process implemented by UAE-based Ajman Bank, which has registered a significant reduction in fraud attempts after implementing an AI-based digital onboarding system as part of its digital transformation.

Another strategy for catching instances of fraud is by using AI for anomaly detection. A machine learning model can study what “normal” looks like, in terms of user behavior, transaction patterns, system activity; and flag anything that stands out. This allows banks to see unusual patterns – e.g. a late-night login or peculiar fund transfers, which would evade static rule-based systems. Unsupervised algorithms (like isolation forests or one-class SVMs) and neural network autoencoders sift through vast streams of events to pinpoint such outliers​. Such strategies, can be deployed to facilitate analysis over large numbers of accounts, which can then be flagged to a human for additional intervention and review.

This tactic can work hand in hand with automating routine security tasks with AI, making cybersecurity operations more efficient. This not only addresses the talent shortage by doing more with less, but also lowers costs associated with manual monitoring and investigation. AI-based security solutions have been shown to improve incident response times and cut costs by reducing trivial alerts and speeding up analysis​. Banks in MENA benefit by reallocating human experts to higher-value activities like threat hunting and fortifying security architecture, while letting AI handle the heavy lifting of round-the-clock surveillance.

Neural networks can analyze huge volumes of transactional data, cross-referencing dozens of variables to catch fraud in ways that traditional systems simply can’t. Banks train neural networks on historical transactions to recognize subtle indicators of fraud that humans might miss. An ensemble of decision trees (random forests) or a deep neural network can analyze dozens of features (transaction size, timing, location, device, user profile) to instantly assess whether a transaction is suspicious​. These models adapt as fraud tactics evolve, improving over time. Similarly, neural networks in intrusion detection systems learn to spot network traffic behaviors that resemble known cyberattacks. This leads to faster, more accurate threat detection and frees up human analysts for higher-level decision-making.

Phishing remains a prime concern for many banks as targeting customers can be a much simpler way to compromise a system than to go after the bank itself. In fact, in 2024 there was a sharp increase in phishing and social engineering attacks, with 42% of organizations reporting incidents (WEF, 2025). To mitigate such threats, many cyber security experts are turning to Natural Language Processing or NLP, which has become a dynamic way in recent years that helps banks detect malicious intent in emails, texts, and even chat messages. NLP enables AI to “read” and analyze text for signs of fraud or attack. An NLP-driven system can scan incoming emails to employees and flag phishing attempts based on language patterns and malicious links. Banks use NLP to monitor chat messages and transaction memos for red flags, like someone soliciting account details. By understanding context in language, AI adds an extra layer of defense to catch social engineering and scam attempts that purely numeric data monitoring might overlook.

By deploying these AI-powered strategies in tandem, banks can create a multi-pronged defense system, akin to a digital immune system, ready to tackle a multitude of afflictions. An anomaly detection system might catch unusual account behavior, while an NLP filter flags a related phishing email – together giving a fuller picture of an attack in progress. This intelligent automation amplifies human analysts’ effectiveness, allowing them to focus on verified threats and complex investigations rather than sifting through noise.

We’re entering a new era in banking security. One where artificial intelligence and generative-AI doesn’t just assist, but actively drives how banks detect, prevent, and respond to threats. The emerging champions won’t be those with the biggest budgets, but those with the clearest strategy, and those who understand that AI is both a weapon and a shield in the modern cybersecurity landscape. One that must be deployed correctly to protect institutions and customers.