finance credit operations

37% improvement in forecast accuracy

Background

Traditional credit scoring models often overlook millions of creditworthy individuals—especially in emerging markets or among younger, underbanked populations. Our client, a digital lending platform, aimed to solve this gap by adopting a more inclusive and data-driven approach.

The Challenge

The platform struggled to accurately assess risk for users with limited or no traditional credit history. Existing models were too rigid, resulting in high rejection rates and missed lending opportunities—especially in markets where financial behavior is underrepresented in bureau data.

The Solution

Azranta implemented a machine learning–driven credit scoring system that delivers deeper, more contextual insights. The model analyzes both traditional and alternative data sources to generate a holistic credit profile, including:
Bank transaction patterns
Mobile payment and utility history
Behavioral signals and digital footprints
Social verification and device metadata
This enabled the client to assess risk more accurately—especially for first-time borrowers or applicants with thin credit files.

What the Retail Chain Said

Azranta’s machine learning models helped us unlock entirely new customer segments.

This transformation didn’t just improve the lender’s bottom line—it expanded financial access to underserved populations, aligning with broader goals of inclusion and economic empowerment. Azranta’s AI-driven approach enabled smarter credit, for more people, with less risk.

30% increase in loan approval rates among previously rejected applicants
20% reduction in default rates due to improved risk modeling
Significant growth in underbanked customer segments, especially in rural and youth demographics

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