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

 

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

Request for services

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Questions



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