Holding enterprises in the United States have government-mandated responsibilities with regard to reporting, including quarterly risk reporting. To fulfill these obligations, and to effectively manage exposure to individual clients, organizations need to be able to tabulate risk for their clients in a consolidated way.
Here is how Mindtree helped a bank holding enterprise develop a holistic picture of its dealings with clients and associated credit risks across geographies and business segments.
The customer was obliged to report risks through asset quality reviews, quarterly risk reports and other filings to the government due to its status as a bank holding enterprise.
However, the organization's IT infrastructure posed a challenge. Due to historical reasons, the customer had multiple databases that were unable to provide a consolidated view of the customer base and the credit exposure to those customers. In addition, lack of information on inter-relationships across systems limited up-selling and cross-selling opportunities. And finally, due to the complexity of financial instruments and underlying data, the process for producing reports was highly effort intensive and error prone.
The customer asked Mindtree to lead a data management program to cater to the needs of its business and also ensure future extensibility of the platform to support broader business objectives.
Mindtree built a comprehensive and scalable enterprise warehouse using a data model based on the industry-standard UDM (Universal Data Model) financial model. We also assessed limitations of some of the existing data marts and information platforms and defined the target architecture for the program along with the bank.
We developed a data model to harmonize the varied financial instruments the holding enterprise's banks used and ensured scalability so that new financial instruments could be added in the future.
We also developed a process to reduce the number of steps required to integrate data elements from 30 source systems for business reporting. Given the usage of the solution for regulatory reporting, data accuracy had to be of the highest standards. Therefore, 100% data validation process was included as part of the data load processes. The team also ensured there were specialized components to monitor data quality in the warehouse. Given the scale of the process involved in extracting data, cleansing it and loading it into the new warehouse, an orchestration framework was developed to help manage the workflow. Mindtree made sure that detailed audit and traceability information was captured during the data migration for tracking data lineage.
The team also developed additional components as part of solution to enhance its usability and effectiveness.