Some financial institutions are pressing ahead and applying Gen AI tools to assessing and adapting both risk control frameworks and processes, as well as client onboarding and service journeys. They are beginning to see early gains in operational cost reductions, significantly improved client onboarding and servicing journeys, as well as dynamic financial crime controls.
Forward-thinking technology teams in large financial institutions are also applying Gen AI solutions to harmonize legacy enterprise technologies, thereby reducing technical debt and freeing up operating costs for innovation. Large language model-powered (LLM- powered) code generation and debugging tools speed up such technology modernization efforts by identifying the underlying business logic constructs. They also help in delivering modern and scalable microservices-based designs. Low-code or no-code Gen AI platforms available in the market automatically generate documentation for the modernized codebase, as well as the API code for integration with the rest of the technology landscape. Some of the more innovative emerging Gen AI solutions also use Retrieval and Augmented Generation (RAG) to iteratively learn and adopt coding standards specific to each financial institution, based on architecture design and standards documentation.
Similarly, COOs are exploring opportunities to convert traditional cost centers such as the procurement function into revenue-enabling entities by repurposing the enhanced insights, decision-making capabilities, and automated AI agents gained from Gen AI solutions, to offer “procurement-as-a-service” to smaller supplier partners, as well as affiliates. The most innovative banks and FinTechs are exploring opportunities to dynamically reconfigure products, or to create new products on the fly, in response to evolving client needs and market conditions, with one major APAC bank recognizing that Gen AI can produce 40% of its product manufacturing requirements.
MENA banks ready to harness Gen AI opportunities
Banks across the region are uniquely well positioned to leapfrog their peers in other parts of the world in leveraging Gen AI to drive growth with adaptive new product management capabilities, while also increasing the share of risk-weighted assets (RWA) and driving down operating costs.
Sovereign funding enables these banks to focus on long-term investments and growth opportunities and many have invested heavily over the past five to seven years in upgrading their technology infrastructure. As a result, more banks in the region have adopted flexible, scalable cloud-native technologies and modular API-enabled product platforms, as well as platform-centric operating models. These banks are largely free of legacy technologies such as mainframes. They do not have mission-critical systems with a large overhang of technology debt and key man risks from a dwindling pool of resources conversant in legacy programming languages such as Common Business Oriented Language (COBOL).
Both banks and national regulators across MENA are innovation-focused, with mature regulations on cloud and blockchain technologies, thriving FinTech ecosystems, well-established national identity management infrastructure, and large pools of data available for training Gen AI models. Banks in the region have long embraced FinTech and are well positioned to rapidly incorporate innovation generated through the FinTech hubs in Dubai, Abu Dhabi, Doha, Riyadh and Cairo.
Senior executives at financial institutions across MENA want to invest but they have concerns as to where to start, unclear returns on investment (ROI), feasibility of integration with existing enterprise systems, execution capabilities in-house, LLM risks such as data privacy and security along with other concerns such as model biases, hallucinations and inability to explain with clarity. In some cases, Gen AI technology is useful in identifying the most relevant use cases to pursue. These use cases would be optimized for ROI, ensure integration feasibility, reduce compliance risks or cater to some other set of prioritization criteria.
CFOs at financial institutions also worry about the nontrivial costs of resources required to operate the better-known generalized LLM platforms. Banks are increasingly turning to smaller, more specialized domain models that can be finely tuned on proprietary data, creating a competitive edge while also being more cost-effective. These domain-specific models require fewer tokens to perform tasks, thus reducing operational costs. Additionally, most established financial institutions as well as FinTech institutions rapidly progress from initial exploration with a single LLM to a portfolio of domain-specific models tuned for specific use case categories. These categories would be based on a common substrate that both protects sensitive data and allows results to be compared on a like-for-like basis across multiple LLMs.
Data, however, is a core capability gap for most MENA banks, despite years of spend on data lakes; challenges range from incomplete and inconsistent data on customers, products and transactions, as well as disparate data sources and technologies. Focused effort is required to produce robust, augmented and synthetic data sets for customer needs profiling, product profitability analyses, risk and regulatory compliance model training. Finally, access to data remains a challenge for over 65% of financial institutions, with fragmented data ownership and governance limiting the ability to rapidly adopt GenAI and machine learning (ML) technologies at scale.
New capabilities required to deliver adaptive banking
To capitalize on the most promising opportunities from adaptive banking, banks will need several key building blocks to leverage the natural language orchestration and product manufacturing capabilities of Gen AI.
