Improved candidate tracking processes
Refined matching outcomes
Greater process consistency
Insights powered by data
The challenge
Breaking barriers in data matching
One of the enterprises in 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
Better precision and detection range
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 maximum results
3
Optimized output
Scores and ranks matches
Selects best matches
Enhances accuracy
The impact
Improved precision and comprehensiveness
Wider coverage
Identified previously overlooked data
Improved inputs to credit rating processes
Tracked a larger pool of candidates
Higher accuracy
Matching up
Demonstrated upward movement
Minimized likelihood of errors
Improved operational outcomes
Timely and consistent matches
Improved process effectiveness
More informed decision-making
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
