Algorithmic Credit Scoring
Project Information
- Category: Algorithmic Lending
- Client: Consumer Fintech
- Stack: ML Scorecard, Alternative Data, Bank Statement Analysis
- Delivery: Credit Model + Monitoring Pipeline
A bureau score tells part of the story. We built the model that tells the rest.
A consumer fintech was relying exclusively on bureau scores, declining thin-file applicants who were genuinely creditworthy. We built an ML credit scoring model trained on their own loan book and enriched with alternative data signals—giving them better accuracy across every tier, and a way to serve borrowers they previously couldn't.
The Challenge
The fintech's credit model was a single bureau score cutoff. Applicants above the threshold got approved; those below were declined—with no ability to differentiate between borrowers within a tier, and no way to evaluate thin-file applicants who had no bureau history. A meaningful portion of their application volume was being declined even though these borrowers had consistent bank transaction patterns that suggested reliable repayment behaviour. The bureau-only approach was capping approval rates and leaving addressable market untouched.
What We Built
We built an ML-driven credit scoring model trained on the client's own historical loan performance data and enriched with alternative data features: bank transaction patterns, income and expenditure signals derived from bank statement analysis, utility and telco payment behaviour, and application-level metadata. The model runs a staging and validation pipeline before any version is promoted to production, and ongoing monitoring detects score drift and triggers scheduled retraining on the client's evolving loan book.
Key Capabilities Delivered
- ML scorecard trained on the client's own historical loan performance
- Alternative data features: bank transaction patterns, utility/telco behaviour, application metadata
- Thin-file and new-to-credit segment coverage through alternative data signals
- Risk-based pricing engine mapping predicted PD to dynamic interest rate bands
- Ongoing model monitoring with drift detection and alerting
- Staging and validation pipeline before any model is promoted to production
The Outcome
The ML model outperformed bureau-only scoring across all credit tiers. The fintech gained the ability to approve thin-file borrowers they previously had to decline—expanding their addressable market without materially increasing credit risk. The risk-based pricing engine allowed them to offer more competitive rates to lower-risk applicants, improving conversion at the top end of the credit spectrum while better pricing risk at the lower end.