The opportunity
Getting started with data transformation
Artificial intelligence could bring tremendous value on a global scale. According to our researchIn the Middle East alone, AI could create up to $150 billion in value, equivalent to 9% of the combined GDP of the Middle East’s Gulf Cooperation Council (GCC) countries.
Emirates NBD (ENBD), one of the leading banks in the UAE, saw this opportunity and took a bold step: embarking on a transformation process to become an AI-driven organization.
“ENBD had just completed a multi-year IT transformation that significantly simplified the IT landscape, digitized many core processes, and created a bank-wide data lake. Advanced analytics and AI were the future growth drivers to drive value through IT transformation investments,” says Miguel Rio Tinto, ENBD Group Chief Digital and Information Officer.
With more than 20 million customers and more than 30,000 employees across 13 countries, it is a complex, multidimensional business. Additionally, in the financial sector, large-scale AI transformation often requires significant initial investment and a long lead time before delivering measurable impact.
Many banks view AI as a huge investment that yields very little return up front. They understand that it will eventually be effective, but find it takes too long. If you choose wisely, including leveraging the capabilities of generation AI, you can bootstrap the investment with the impact initially accrued as you scale.
Ashwin NaibMcKinsey, Associate Partner
The approach
Bootstrapping at scale
In 2021, ENBD embarked on the first phase of this transformation by partnering with McKinsey. “It was crucial for us to establish a business-aligned advanced analytics strategy and roadmap from the start so that we could quickly scale use cases and their impact,” says Neeraj Makin, Head of strategy, analysis and venture capital at ENBD. “Sponsorship from business leaders has played a key role in driving use cases and broad adoption by frontline stakeholders.”
The focus was on high-impact flagship use cases that demonstrated substantial value across all business units and had executive support. To minimize front-line change management, the analysis results were integrated into existing business applications.
Like all banks, we had huge volumes of uncatalogued, multi-source data of varying quality and unclear provenance. Discoverability, understandability and data quality were significant challenges.
Ray RichardsonDirector of Data and Analytics at ENBD
To jump-start the journey, a select group of people with a deep understanding of the complex data landscape were brought into the core team. This small team maintained the data assets needed for the Lighthouse’s initial use cases. Scaling this data effort then required a significantly different approach in which a data mesh-inspired strategy was initiated by the small core team. This team has established a fully federated approach to data governance and management across the enterprise.
To develop the analysis in complete security, the bank worked with QuantumBlack, AI by McKinseyon features like an automated CI/CD framework and containerized builds that eliminate reproducibility issues. A feature store was created to provide observability of data and machine learning (ML) pipelines and reduce build time. A model validation framework was developed to standardize, validate and automatically generate model documentation.
ENBD has hired a core team of more than 70 new analytics and strategy professionals.
How a UAE Bank Transformed to Lead with AI and Advanced Analytics
“As business demand for analytics grew, there became a need to move from large-scale, long-cycle use cases to much lighter, short-cycle, iterative delivery versions,” says Ahmed Al Qassim, Head of Wholesale Banking at ENBD Group.
ENBD took a test-and-learn approach to this transition and leveraged the data and analytical resources developed for scaling. Additionally, business impact was measured and tracked using standardized ML-based synthetic control groups, closely supported by the business and finance teams.
Building a strong talent pipeline has been crucial throughout this process. Initially, ENBD focused on building data science capabilities while reskilling existing staff for roles such as delivery managers and data engineers.
“When we talk about AI, the focus is on building technical capabilities. But it’s just as important to focus on change management. Techno-functional roles are key to bridging this gap,” says McKinsey partner Saadi Azeem.
As the transformation progressed, the growing need for techno-functional roles became essential to drive change management and identify future use cases. The bank initially focused on external recruitment to fill these positions. In parallel, ENBD designed an upskilling program with the McKinsey Academy and supported internal events such as generative AI (gen AI) hackathons to build a broader AI community and develop capabilities within the organization.
The impact
Scalability across the organization
ENBD has demonstrated scalable growth in the industry through this strategic AI transformation. Integrating scalable analytics across the enterprise has improved operational efficiencies and institutionalized data and analytics decision-making across the organization.
Example of an AI tool developed with Generation AI querying capability to equip relationship managers with detailed transactional analytics on their current customers and prospects.
How a UAE Bank Transformed to Lead with AI and Advanced Analytics
ENBD can now use predictive AI to create personalized customer experiences. For example, in retail banking, hyper-personalization capability can be used to advise mass segment customers, including new investors, on tailored investment solutions.
“Advanced analytics models can help identify high-potential prospects, drive better engagement with existing customers by matching them to the right products and services, and support frontline teams with insights and insights personalized discussion sessions,” says the head of retail banking and wealth management at the ENBD group. Marwan Hadi.
During the first two years of this effort, the bank produced more than 100 models and assembled an advanced analytics team of more than 70 people. Overall, ENBD aims to generate five to seven times ROI on its AI investment through the business value generated by analytics and data-driven initiatives.
“From board decisions to every customer interaction, we intend to make AI a crucial part of every business moment and event,” says Ray.
Senior Partner and Managing Partner, Karachi
