In today’s competitive environment, Banks need to personalize their products & services offerings to retain and expand their client base. They leverage independent credit reference agencies in a major way to ensure their credit decisions are accurate, transparent and fair. Under the “Principles of Reciprocity”, financial Institutions also have an obligation to share their customers’ credit related information through seed-files with credit reference agencies too. Our client, a large retail and commercial bank in the UK, was using a mainframe system to feed their retail customers data to the credit reference agencies.
To comply with additional obligations, the bank identified a requirement to amend their existing process of sharing its Corporate and Commercial customers credit related information with the credit reference agencies. Legacy system constraints and low transaction processing capabilities were causing delays in the credit scoring process, leading to customer dissatisfaction and disrupted services.
After doing an internal ROI analysis, the Bank concluded that the cost of building additional processes on the existing legacy tech-stack and considering future requirements were far more than upgrading to a new technology stack. Coforge was selected as the transformation partner to drive and implement this change.
Coforge set up a cross-functional team to conduct a discovery exercise and figured that all customer information was available in the Bank’s Data Lake. Based on the findings, Coforge proposed to build the new processes on this tech-stack and produce the seed-files of Retail, Corporate and Commercial customers for the credit reference agencies.
Coforge developed, tested & deployed the new processes / modules for seed-file generation using Skala/Spark framework on the Hadoop Data Lake. Legacy RDBMS was decommissioned after migrating the data to SQL Server. A micro-services and API based architecture was also created to dynamically interact with multiple credit reference agencies.
Coforge developed, tested & deployed the new processes / modules for seed-file generation using Skala/Spark framework on the Hadoop Data Lake.
Legacy RDBMS was decommissioned after migrating the data to SQL Server. A micro-services and API based architecture was also created to dynamically interact with multiple credit reference agencies.
1. Decommissioning of mainframe processing aligned with the technological strategy of the Bank and thereby helped reduce cost by 30%.
2. The system is now easy to monitor, and associated maintenance also decreased by 25%.
3. The database upgrade increased the transaction processing capability, throughput and system stability. Processing errors and infrastructure issues reduced by 45%.
4. The Bank’s new ability to interact with multiple credit reference agencies simultaneously lead to quicker credit decisions from days to hours leading to an all-around improved customer experience.