Data and Business Analytics to Increase Productivity, Lower Risk Costs, Accelerate Growth – A Large Financial Group
The client is a worldwide financial group with offices in more than 11 countries including 26 domestic offices in the USA and $ 15 B in average assets. The client had challenges with responding to regulatory requests in a timely manner, reconciliation of data from more than 25 source systems to their General Ledger, understanding customer profiles, and finding opportunities to upsells and cross-sells. There were minimal risk mitigation processes in place because of lack of availability of trustworthy and meaningful data in a timely manner.
The client had nearly 25 source systems with no data standardization. Integrating inconsistent and incomplete data from all these heterogeneous sources poses the primary challenge, followed by reconciling the integrated data with the bank’s ledger data. Other challenges included back tracing the information value chain to identify and rectify anomalies in the reconciliation output and implementing the complex business logic during transformation without impacting performance.
- Improved risk control. A bank could lower its risk costs through advanced early-warning systems, and credit-collection analytics
- Deeper and more detailed profiles of customers, together with transactional and trading analytics, improved the acquisition and retention of clients, as well as cross- and upselling, hence increased revenue
- Product and Customer level performance reporting now allows the business to concentrate on the critical segments as well as to take corrective actions if needed
- Increased productivity as bank staff did not have to spend a lot of time in responding to regulatory requests
- A streamlined automated process and system that replaces the multiple manual processes of generating spreadsheets based on rate calculations for very limited accounts
- Data from nearly 25 source systems were integrated and transformed as per business logic and fed into downstream applications which process the data and provide business users access to performance reports
- At every stage before feeding the downstream application, data quality and data reconciliation are done with the ledger data. Any data quality issue is tracked properly through a mechanism that will allow the team to track and correct the cause
- Through proper data quality checks implemented for the project, some of the data mapping issues that were unnoticed before were identified, which helped other business users who were not part of this implementation
- An enterprise business intelligence and analytics system was developed quickly without any compromise in quality of the deliverables