AI Credit Scoring
for New-to-Credit Users
AI Credit Scoring
Success Data
Increase
in approved loan applications.
Reduction
in default rates using predictive modeling.
The client required a solution that could
Decide whether borrowers are eligible for loans even if they have no credit history.
Automatically evaluate loan applications instead of relying on manual reviews.
Improve risk segmentation using alternative data.
Analyzes spending behavior, bank flows, repayment habits, and digital activity.
Provides automated loan approvals based on AI risk scores.
Uses mobile usage, cash flow, and transaction data to predict the chance of repayment.
Continuously improves with real borrower outcomes.
Many applicants were rejected because they had no traditional credit history.
Manual checks made loan approvals take too long.
It was hard to clearly tell low-risk borrowers from high-risk borrowers with limited data.
Hiteshi addressed the client's concerns through the following solutions.
01
Enabled more inclusive lending by accurately predicting borrower risk.
02
Reduced underwriting time by 90% through automation.
03
Improved loan quality by lowering default rates.
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