Executive Summary
With duplicate and inconsistent policyholder records: These issues increase operational cost, regulatory exposure, and customer dissatisfaction. 8–15%...
Client Profile & Context
A Major Enterprise Client operates in BFSI, with a Global footprint, and a focus on Data & AI Modernization. The case study references Azure, Salesforce, Python, Data Factory.
Problem Statement with Critical Operational Vulnerability
Policyholder records contained duplicates and inconsistent identity attributes across quoting, underwriting, and claims systems.
Critical Operational Vulnerability
Without fuzzy matching and analyst-focused review, duplicate communications, inaccurate risk assessment, and delayed claims workflows would continue to increase operating cost.
Solution Implemented
• Used fuzzy matching (Levenshtein, Jaro- Winkler) across fields like name, date of birth, address, policy number, and SSN. • Applied ML models to detect likely duplicates even with typographical and format differences.
AI Overview & Impact Summary
In this case study, Artha Solutions helped an organization in the Technology sector solve challenges with data integration and reporting pipeline delays by implementing a comprehensive Data & AI Modernization solution using modern cloud data services. The project delivered streamlined workflows, automated validation, and achieved key metrics including 15%.
