Transforming Credit Risk with Alternative Scoring for a Leading Fintech
Client Overview
A fast-growing fintech company serving underbanked populations needed a better way to assess creditworthiness. Traditional credit scores left out a large portion of potential customers — especially those with limited financial histories but strong repayment potential.
To grow responsibly, the company needed a smarter, more inclusive way to evaluate risk.
The Challenge
Many of the client’s target users lacked sufficient traditional credit data, making it difficult to extend loans or credit lines without incurring high default risk. Existing models either rejected too many users or exposed the business to unnecessary losses.
They partnered with us to design a custom credit scoring model that could expand access while keeping default rates low.
Our Solution
We built a machine learning–powered alternative credit score that went beyond conventional data. By combining spending behavior with social network signals, we created a richer, more predictive profile of each user’s ability and willingness to repay.
Key Features of the Model:
Spending Pattern Analysis: We analyzed purchase types, regularity, cash flow consistency, and financial habits to infer stability and reliability.
Social Network Features: Using graph-based techniques, we incorporated insights from the user’s financial and social connections — for example, repayment behaviors of close peers and the trustworthiness of their immediate network.
Real-Time Scoring Engine: The score was integrated into the company’s decision engine, enabling instant credit decisions and adaptive limits.
Results
The model delivered both financial and strategic impact:
Increased approval rates among previously unscorable users, unlocking new revenue streams.
Reduced default rates, even in high-risk segments, by leveraging deeper behavioral signals.
Higher loan portfolio performance, leading to sustainable and scalable growth.
Conclusion
By using machine learning to score users based on behavior and social context, we helped this fintech reimagine credit risk — turning exclusion into opportunity. The result was a more inclusive, more accurate credit scoring system that balanced growth with safety.