(MENAFN- Mid-East Info) Banks in the Middle East region have shifted their focus from just going digital. Now, they aim to go intelligent, turning data into decisions. This is where AI and ML development service come into play. Emirates NBD, a government-owned bank in Dubai, showcases this tendency just perfectly, having created one of the biggest data lakes in the region to run AI models across all its systems seamlessly.

The government strongly supports the integration of AI in financial services in the Middle East region. For example, Saudi Arabia has announced massive investments in the AI sector as part of its Saudi Vision 2030 strategy, totaling over $14.9 billion.

In this article, we will go through the key use cases of AI, mainly in banking, and discuss how top AI companies in MENA use this technology. How Banks in MENA Apply AI: Key Use Cases

Experiments with AI in banking are a thing of the past. Banks in the Middle East are now advanced users of AI, aiming to scale this technology to increase customer satisfaction, boost operational efficiency, and, consequently, grow profits. Let's see how they do that. Personalization and Customer Behavior Prediction

By analyzing transaction history, lifestyle, place of living, and more, banks can provide clients with individualized offers instead of generic products. For example, Emirates NBD uses predictive analytics to offer personalized credit or deposit products at the right moment.

Personalization increases conversion because the clients buy relevant products that they really like. Predictive analytics enable banks to identify when users may require a new financial solution, such as insurance, business credit, or other services. Qatar National Bank has an AI solution that predicts events in clients' lives, such as workplace changes, weddings, and childbirth. This helps them offer personalized solutions in advance.

Another benefit lies in the precise estimation of risks and creditworthiness. AI detects hundreds of factors that a human analyst might overlook. First Abu Dhabi Bank, for instance, has integrated ML models for the dynamic update of credit scoring in real-time.

In the region, as banking competitiveness grows rapidly, clients' data analytics bring an advantage: AI transforms banks from service providers to financial advisors that understand clients' needs even before they are announced.

Thus, AI for personalization and behavior prediction includes:

• Big Data analysis;

• Predictive analytics;

• Client segmentation;

• Recommendation systems.

Effective Risk Management

Ernst & Young (EY) analytical material includes information that credit risk and fraud detection are key areas of AI usage. Indeed, AI is bringing about significant changes to the way banking institutions manage risks and protect their clients.

Let's start with processing massive amounts of data. AI algorithms analyse thousands or millions of events, including transactions, behavior patterns, and non-traditional data. This helps understand risks better.

Thanks to machine learning, banks can detect suspicious or atypical models, such as fraud activity and deviations from norms, much faster than humans can. Riyad Bank is a perfect example. It has launched its“Center of Intelligence” to integrate ML solutions for risk, operations, and innovation analysis.

Another crucial benefit lies in risk prediction. AI models are taught to predict the probabilities of defaults (credit risk), financial instability, or market risk based on historical and actual data. Moreover, AI helps optimize the risk management process, including credit scoring, transaction monitoring, and event management, enabling banks to enhance their effectiveness and minimize human errors.

Here are the most popular ways of using AI for risk management:

• Predictive modelling;

• Anomaly detection;

• Natural Language Processing (NLP);

• Machine Learning Scoring Models;

• Behavioral Analytics;

• Stress Testing with AI Simulation.

AI Chatbots and Assistants

Banks in the Middle East region integrate chatbots to deliver 24/7 client service, manage requests, streamline operations, offer expert advice, and more. Chatbots are very popular in the banking sector, and here is why:

• Banks receive an enormous amount of requests from clients, and chatbots help process such massive requests fast and at any time of the day.

• Cultural adaptation and multilingualism: for example, chatbots can support both Arabic and English languages, taking into account dialects. This increases the level of trust and overall client satisfaction.

• Efficiency growth: automating typical routine requests reduces the load on contact and support centers.

• Client experience improvement: banks strive to be more personalized, accessible, and technologically robust. Chatbots and assistants are key elements in achieving this.

For example, Mashreq Bank has integrated an AI chatbot that can serve clients with any question, including authentication requests, operational inquiries, and more. Basic Aspects of Building AI Infrastructure in the MENA Banking Sector

Building AI infrastructure is based on the combination of some key components that shape the background for effective integration of AI into processes.

Firstly, cloud solutions and data centers provide the necessary computing power, data storage, and scalability of models. They enable banks to launch complex ML algorithms, work with massive transaction data, and support seamless operation of AI services 24/7. Owing to this, financial institutions can quickly react to market changes and update models without significant cost on their infrastructure.

At the same time, API ecosystems and integrations have become the backbone of system interactions. Banks connect their core platforms, mobile applications, and external services through standardized interfaces to ensure non-stop data exchange with AI models. This creates a unified digital environment where information flows seamlessly, and the client enjoys a personalized experience in real-time.

Low-code and no-code platforms are also helpful. They enable banks to build and integrate AI solutions without significant engineering resources. Such platforms accelerate the digital transformation of banks, making innovations more accessible even to business users, not just tech experts.

The key success factor for AI implementation is a partnership with technological giants such as Microsoft, IBM, and Google Cloud. Additionally, local AI startups make a significant contribution to helping banks create, deploy, and maintain a modern and secure infrastructure. Such collaborations provide access to advanced technologies and expertise, and accelerate the process from idea to a full-fledged, client-oriented product.

Finally, a digital-native approach provides banks with a significant advantage: institutions that have initially built their processes based on digital principles with flexible architecture can now scale AI solutions more effectively. Such infrastructure not only increases effectiveness but also prepares the bank to meet the next wave of innovations, where AI will become a central part of the banking experience. From Data to Decisions: Summarizing The Rise of Truly Intelligent Banking in MENA

AI has become a strategic advantage, enabling banks to make real-time decisions, personalize services, minimize risks, and build trust through transparency and enhanced service speed. The examples of Emirates NBD, Mashreq Bank, and Riyadh Bank demonstrate that the region is actively investing in its AI ecosystem and partnerships with leading tech companies.

In the future, AI in banking in the MENA region will cross the automation boundaries. We will observe the transition to self-driving banking, the systems that autonomously analyze, predict, and act based on client data. Generative AI, intelligent assistants, autonomous financial management systems, and decentralized analytical models will become a new standard in the field. Banks that are building AI infrastructure today will become technological ecosystems tomorrow.

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