Data modernisation: How we enabled a global bank to transform its 20-year-old legacy architecture to become process-efficient and customer-centric
Introduction
For a leading global banking and financial services institution, it’s 20-year-old legacy architecture was a major hurdle as they struggled with processing speed, operational efficiency, and data management. The absence of global-standard centralised data marts and data variables across the organisation resulted in inconsistent risk management practices, affecting the bank’s ability to accurately monitor risks. It also made compliance with evolving global regulatory standards, extremely difficult.
The inefficient legacy architecture was not only a risk for compliance failures but also constrained the bank's overall operational agility. Without a centralised data system, different regions operated in silos, which slowed down risk reporting and decision-making. This fragmented approach also increased costs, due to the need for additional labor and time to reconcile data across the bank's regions.
Failure to modernise could have resulted in substantial regulatory fines, loss of reputation, and missed business opportunities. Additionally, the bank was unable to offer personalised financial products, such as tailored loan interest rates, because its data infrastructure could not support the necessary level of granular analysis. Modernising this infrastructure was crucial for both operational efficiency and improving customer service offerings.
The situation
We helped the client implement a centralised risk data management system to comply with global regulatory standards and to support risk analytics and advanced forecasting solutions.
So, what did we do?
The Deloitte way
The approach involved a developing a comprehensive data modernisation initiative to set up centralised data marts, data lakes, and data layers. By centralising risk data and standardising data variables, the bank could finally create a global-standard risk management system.
Creating centralised data infrastructure: The solution involved consolidating the bank's disparate data systems into one centralised architecture. This allowed for the seamless integration of risk data across regions, ensuring real-time risk monitoring and identification of variables that elevated risks.
Business intelligence (BI) and forecasting: Advanced BI tools and forecasting methodologies were implemented, allowing the bank to leverage its centralised data for informed decision-making. The solution supported both market and operational strategies by providing accurate, real-time insights.
The impact
By modernising and centralising data ecosystem the client was able to realise below business impacts:
Improvement in process efficiency: 3X improvement in process efficiency, enabling the bank to process and analyse risk data three times faster than before.
Increased data reliability: 80% increase in data reliability as the new infrastructure ensured that risk data across regions was consistent and accurate, significantly improved regulatory compliance and risk monitoring.
Enhanced risk management: With a centralised, real-time risk data infrastructure, the bank could now monitor and respond to emerging risks more effectively and efficiently.
The client was able to realise immediate improvements in operational efficiency and cost savings as it significantly reduced manual intervention. It also resulted in better compliance with the regulatory requirements without manual reconciliation of fragmented data sources. In the long run, the new infrastructure has positioned the bank to carry out future innovations in risk management, enabling it to handle increasingly complex financial instruments and risks. The bank is also able to offer enhanced customer offerings, such as tailored loan interest rates, allowing the bank to maintain a competitive edge and improve customer satisfaction.
Drilling in success
Christine Ahn
