Data Standardization and Measuring Data Quality Assessment for large scale migration from legacy risk management systems to a strategic system for one of the leading US Bank
Client : One of the leading US banks
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Objective
Standardize data, implement and monitor a control framework to assess data quality for large scale migration from legacy risk management systems to a strategic system
Challenges
Presence of multiple risk systems and inconsistent data feeds
Existence of different naming conventions and formats of the data
Generation of multiple reports as per regulatory and internal requirements
Need to maintain data granularity for reporting purposes
CRISIL’s solution
CRISIL followed a 3-step end-to-end process for data standardization and quality enhancement
Establishing Control Framework
Gap Analysis - Analyze the as is process and identify pain points
Data Sourcing - Understand the sources and agree on the data that needs to be normalized
Data Analysis & Standardization
Cluster Analysis – Identify and group the data on the basis of underlying issues or patterns
Machine Learning - Approximate string matching using fuzzy logic for data which could not be clustered
Standardization- Standardized data across all products at various levels
Resolution & Reporting
Reporting- Data quality issue summary reported to senior management
Reports generated using BI tools to ensure data consistency
Resolution - Identified and remediated causes of data quality issues by coordinating with technology partners
Value addition
Automation across 3 stages with Python
Incorporated machine learning matching algorithms (fuzzy logic) using distance and ratio methodology to automate data quality checks
Client impact
Created an end-to-end control framework for data quality management
Established a scalable and standardized reporting framework for both internal use and regulatory requirements
Overall automation resulted in throughput reduction of ~85% in terms of TAT
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