The Middle East’s banking sector is poised for significant transformation through the increased adoption of Artificial Intelligence (AI). The outlook is decidedly positive, with predictions indicating that AI could contribute as much as 13.6% to the region’s GDP by 2030.
Currently, much of the focus in discussions about AI within banks centres on Generative AI (GenAI). This is understandable, given the immediate impact GenAI can have on customer-facing applications like AI-powered customer service chatbots and in assisting human workers with knowledge management. Examples include Bank ABC’s “Fatema Digital” and Mashreq Bank, where chatbots handle a substantial volume of customer interactions. The enthusiasm for GenAI is further fueled by projections suggesting the GCC region could see a substantial return on investment in these technologies.
However, while the importance of GenAI cannot be denied, banks that fail to recognise the broader AI landscape risk missing out on a wave of transformative opportunities.
The power of LQMs and agentic AI in finance
Many of the most pressing challenges in the financial sector are not fundamentally language-based. These challenges require AI models capable of processing and analysing complex data and, crucially, models that can autonomously orchestrate actions based on that analysis. This is where Large Quantitative Models (LQMs) and agentic AI come into play.
LQMs are AI models trained on quantitative data, using mathematical equations to model real-world behaviours. This allows them to provide precise predictions and simulations for complex problems, making them ideally suited for the quantitative nature of finance. LQMs are designed to address the core analytical needs of the global economy.
Building on this, agentic AI takes automation a step further. Agentic AI involves developing AI systems that can:
- Automate intricate, multi-step workflows.
- Dynamically create and coordinate teams of AI agents to achieve specific objectives.
- Autonomously execute tasks in the digital (and potentially physical) world.
These agentic systems move beyond pre-defined workflows, enabling a more dynamic and adaptive approach to automation.
It’s important to emphasise that LQMs and agentic AI are not merely futuristic concepts. They are being adopted to address critical needs within the financial industry. For instance, AI is being used to extract information from text sources and transform it into structured queries compatible with internal data systems, bridging data silos and alleviating friction in data processing workflows. This technology is also being explored for portfolio analysis applications, such as testing portfolio adjustments and comparing different portfolio construction methodologies.
Banks that embrace LQMs and agentic AI will gain a significant competitive advantage.
Key applications in banking
LQMs and agentic AI offer transformative potential across various banking functions:
Risk management and forecasting
New credit risk management standards from the UAE’s central bank came into effect at the end of November. In Saudi Arabia, lending has grown by 13%, with Saudi Arabian banks providing $780 billion in credit to public and private sectors by the end of November 2024—marking the highest level of the year and a year-on-year increase of over 13%.
- LQMs can enhance credit risk management by providing more accurate borrower behaviour and market volatility predictions.
- AI-driven systems can automate complex risk assessment workflows, enabling near real-time scenario planning in response to market shocks and geopolitical events.
- AI agents can analyse diverse data sources to improve macroeconomic forecasting and provide more accurate predictions of economic trends.
Wealth management
Dubai is home to 212 millionaires with over US$100 million or more and 15 billionaires, making it one of the wealthiest cities in the world. Neighbouring Abu Dhabi has been recognised as a city “to watch” for this list, while Qatar boasts an impressively high per capita GDP of over $87,000.
- LQMs can develop more sophisticated portfolio optimisation models and create personalised investment strategies tailored to individual client needs and risk profiles.
- Agentic platforms can provide financial analysts with advanced tools to test portfolio adjustments, compare different portfolio construction methodologies, and gain deeper insights into asset allocation.
Regulatory compliance
The UAE’s removal from the FATF grey list in February 2024 underscores the significance of regulatory efforts, particularly around Anti-Money Laundering (AML).
- AI agents can automate compliance checks, monitor transactions for suspicious activity, and streamline regulatory reporting processes, significantly reducing the compliance burden.
- AI-driven systems can automate compliance verification workflows and ensure adherence to regulations.
LQMs and agentic AI represent a paradigm shift in the application of AI to finance. By enabling sophisticated data analysis, autonomous decision-making, and workflow automation, these technologies empower banks to operate more efficiently, manage risk more effectively, and deliver enhanced value to their customers.
To remain competitive and capitalise on the vast opportunities presented by AI, financial institutions in the Middle East must proactively explore and adopt these cutting-edge solutions.
