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: Leading US Bank
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Objective
To support a leading US bank in its migration from a legacy risk management system to a new strategic platform by standardizing data, and implementing and monitoring a control framework to assess data quality.
Challenges
Presence of multiple risk systems and inconsistent data feeds
Existence of different naming conventions and formats
Ned to generate multiple reports per regulatory and internal requirement
sNeed to maintain data granularity for reporting purposes
CRISIL's Solution
CRISIL followed a three-step end-to-end process for data standardization and quality enhancement:
1. Establishing Control Framework
Gap Analysis - Analyzed the as-is process and identified pain points
Data Sourcing - Identified sources and determine data that needs to be normalized
2. Data Analysis & Standardization
Cluster Analysis - Identified and grouped data on the basis of underlying issues or patterns
Machine Learning - Approximated string matching using fuzzy logic for data that could not be clustered
Standardization - Standardized data across all products at various levels
3. 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
The CRISIL solution provided automation across three stages with Python, and 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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