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.
Request for services
Questions
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