The Client
One of the largest retail banks in the Europe providing current account, Credit cards, Loans, Insurance, Mortgage & Saving and Investment services to customers
Business Challenge
In today’s competitive environment, banks need to personalize their products and service 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.
Our client was using a mainframe system to feed their retail customers’ data to the Credit Reference Agencies. To comply with additional obligations, bank needed to amend their existing process of sharing customers’ credit information. The limitations of the legacy system, along with its low transaction processing speeds, caused delays in the credit scoring procedure, resulting in customer dissatisfaction and service disruptions.
After doing an internal ROI analysis, the Bank concluded that the costs of building additional processes on the existing legacy tech-stack as well as future requirements, were far more than upgrading to a new technology stack
Our Solution
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. After migrating the data to SQL Server, Legacy RDBMS was decommissioned. A micro-services and API based architecture was also created to dynamically interact with multiple Credit Reference Agencies.
The Result
Decommissioning of mainframe processing aligned with the technological strategy of the bank led to reduction in costs by 30%.
The system was now easy to monitor, and associated maintenance costs went down by 25%. The bank upgraded its database, resulting in improved capability for transaction processing, higher throughput, and greater system stability. This led to a 45% decrease in processing errors and infrastructure problems. Furthermore, the bank was now capable of communicating with Credit Reference Agencies simultaneously, allowing for faster credit decisions to be made - going from taking days to just hours - and enhancing the customer experience overall.
