Migration of legacy risk management systems
Objective
A large US-based bank wanted to standardize data and implement monitoring control check points to measure data quality for large scale migration of legacy risk management systems.
Challenges
- Inconsistencies in data feeds across multiple risk systems.
- Different naming conventions and formats of data.
- Maintaining the granularity for reporting purposes.
Approach
- Performed a gap analysis and understand data sources.
- Established a control framework.
- Incorporated automated machine learning matching algorithms, cluster analysis and approximate string matching using Python and Qlik to standardize data across platforms.
Impact
- Created end-to-end control framework for data quality management.
- Established a scalable and standardized reporting framework for both internal use and regulatory requirement including IMM, Volcker and BCBS 239.
Questions