Enhanced candidate tracking
Higher matching accuracy
Improved efficiency
Data-driven advantage
The challenge
Breaking barriers in data matching
One of the world’s largest providers of credit information and information management services aimed to develop a sophisticated algorithm to accurately link names and addresses across multiple datasets. The goal was to enhance match accuracy while minimizing misclassification rates, ultimately driving measurable performance improvements.
Key challenges
Data mismatches across sources
Sought to reduce false matches
Needed standardization for a unified view
Required efficient handling of large datasets
The solution
Data standardization and structuring
Advanced matching
Fuzzy matching with Levenshtein and tokens
Improved accuracy and recall
Heuristics-based scoring
Data processing
Standardizes data
Segments addresses
Ensures consistency
Implementation approach
1
Standardization
Formats data
Prepares for fuzzy matching
Removes inconsistencies
2
Address matching
Verifies names
Filters by postcode
Matches top 100 results
3
Optimized output
Scores and ranks matches
Selects best matches
Enhances accuracy
The impact
Enhanced accuracy and coverage
Wider coverage
Captured missed data
Strengthened credit ratings
Tracked 80M more candidates
Higher accuracy
Matching up by 7%
Increased to 81.2
Reduced errors
Better performance
Faster, reliable matches
Optimized efficiency
Better decisions
Looking ahead
Further accuracy gains
Continuously refining matching algorithms
Scalability and expansion
Enhancing systems to handle larger datasets
Advanced AI integration
Leveraging AI for smarter data processing
