CreditLens
How It Works
Step 1 - Data Analysis
Extracted data is fed into our proprietary analytics engine, enabling deep analysis of both structured and alternative financial data.
Step 2 - Feature Engineering
Data is normalized and enhanced through feature engineering to generate meaningful features and attributes for credit scoring.
Step 3 - Insights and Decisioning
Real-time insights are delivered to the underwriters, improving the risk assessment accuracy and increased loan approvals with lower default rates.
Why Choose CreditLens?
CreditLens
- Uses real-time and historical data for increased accuracy and timeliness.
- Expands credit coverage by including individuals with thin or no credit history.
- Delivers a full analysis of cash flow and expenses for a holistic financial assessment.
- Promotes financial inclusion by enabling access to credit for underserved and underbanked customers.
Traditional Credit Scoring
- Relies on outdated data with up to a one-month lag, reducing accuracy and timeliness.
- Offers limited coverage, often excluding individuals with thin or no credit history.
- Focuses solely on loan repayment history, missing a holistic view of financial behaviour.
- Excludes thin-file customers, limiting financial inclusion and access to credit.