Building Credit Scoring for the Invisible Economy

How we're using alternative data to bring credit access to 450 million underbanked Indians.

The Problem

450 million Indians are "credit invisible." They have no credit history, no bank statements, no traditional markers that credit scoring systems understand. Yet many of these individuals are creditworthy — they run businesses, pay bills, maintain consistent economic activity.

The traditional credit infrastructure simply can't see them.

Alternative Data: A New Lens

What if we looked at creditworthiness differently? Instead of bank statements, what about:

• Mobile recharge patterns: Consistent, responsible spending
• Utility payments: History of meeting obligations
• Business transaction flows: Revenue and cash flow visibility
• Social graph signals: Community trust indicators

This is the foundation of OpenCredit.

Why Open Source?

Credit scoring has historically been a black box. Algorithms that determine who gets loans — and at what rates — are closely guarded secrets. This creates two problems:

1. Trust Deficit When you can't see the algorithm, you can't trust it. This is especially problematic in financial inclusion, where historical biases have excluded entire communities.

2. Innovation Barrier Proprietary systems are slow to evolve. Open source enables rapid iteration, peer review, and community-driven improvement.

OpenCredit is Apache 2.0 licensed. The algorithm is visible. The methodology is documented. Anyone can audit, critique, and contribute.

The Technical Approach

Feature Engineering

We work with alternative data sources to extract meaningful credit signals. This isn't about more data — it's about better features.

Model Architecture

Ensemble methods combining traditional scoring approaches with modern ML. Explainability is built in — every score can be traced to specific factors.

Bias Detection

Continuous monitoring for disparate impact. If the model is treating groups unfairly, we catch it and correct it.

Privacy by Design

We don't need raw data. Aggregated, anonymized features are sufficient for scoring. Your transaction history stays yours.

Real World Impact

Working with partner NBFCs and fintechs, OpenCredit has enabled credit access for individuals who would otherwise be rejected outright. Early results show:

• 40% higher approval rates for thin-file applicants
• Default rates comparable to traditional scoring
• Faster decisions due to automated processing

Getting Involved

OpenCredit is a community project. We welcome:

• Financial institutions looking to expand inclusion
• Data scientists interested in alternative credit modeling
• Policy advocates working on financial access
• Developers who want to contribute

Check out the project on GitHub and join our community.

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Learn more at /products/opencredit or visit our GitHub repository.