Building A Lending Analytics
Building A Lending Analytics Engine For Better Portfolio Control
The Background
The lender used multiple disconnected tools:
- Loan origination
- Bank statement analyzer
- Collections software
- SMS data scraping
- Recovery system
- Excel-based reconciliation
Nothing synced.
Reporting took days.
The Problem
No clear portfolio view
No insight into repayment patterns
Collections team lacked prioritization
Historical borrower behavior was invisible
The Trigger
An internal audit found that many defaulters had early behavioral red flags — but no system caught them.
Leadership realized they were approving the wrong borrowers.
Cor Advance Data Solution
We built a Financial Data Warehouse that cleaned, merged, and structured
Repayment behavior
- Web analytics
- Email engagement
- Trial activity
- Company size & industry fit
- Behavioral patterns of past converters
Bank statement trends
- Usage depth
- Feature adoption
- Login frequency
- Support queries
- Account configuration behavior
NPA flags
Suggests optimal overbooking, sends automated WhatsApp/SMS reminders, and alerts staff of high-risk patients.
Collections success rates
- Web analytics
- Email engagement
- Trial activity
- Company size & industry fit
- Behavioral patterns of past converters
Region-wise portfolio health
- Usage depth
- Feature adoption
- Login frequency
- Support queries
- Account configuration behavior
Borrower segmentation
Suggests optimal overbooking, sends automated WhatsApp/SMS reminders, and alerts staff of high-risk patients.
Implementation Journey
Weeks 1-2
Discovery & Requirement Analysis
Portfolio health visibility improved
Week 4
Blockchain Strategy & Planning
Collections improved by 17%
Week 6
Smart Contract & DApp Development
Faster reporting
Week 8
Smart Contract & DApp Development
Smart prioritization reduced NPA risk
Transformation & Results
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