10% accuracy boost
Enhanced matching
Broader coverage
Improved efficiency
The challenge
Overcoming data fragmentation for accurate customer matching
A leading credit bureau provided analytics and intelligence to local credit rating agencies, leveraging data from multiple sources such as banks, voter IDs, and tax returns to generate credit scores.
Key challenges
Inconsistent address formats complicated matching
Lack of a common key hindered accurate identification
Different IDs across sources made linking records difficult
Required better precision, fewer false positives, and a unified view
The solution
Optimized data matching for unified customer insights
Smart matching
Standardized names and addresses
Configurable matching algorithm
Integrated search for better results
High accuracy and efficiency
Enhanced match accuracy
Seamlessly linked records
Created a unified customer view
Implementation approach
1
Data standardization
Standardized data
Removed extraneous elements
Segmented addresses
2
Advanced search and matching
Matched via pin codes
Used dCrypt for accuracy
Selected top 100 matches
3
Validation and optimization
Compared match scores
Validated manually
Refined algorithms
The impact
Enhanced data matching and accuracy
Accuracy boost
10% improvement on 11M addresses
Enhanced match precision
Fewer false positives
Advanced matching
Used three algorithms for coverage
Integrated search feature
Enhanced efficiency
Better insights
Strengthened decision-making
Unified customer records
Optimized operations
Looking ahead
AI-driven precision
Explore advanced AI techniques for even more precise matching
Real-time integration
Integrate real-time data sources for dynamic updates
Data expansion
Expand the solution to additional datasets for broader insights
