Header Banner Header Banner
Topics In Demand
Notification
New

No notification found.

AI-Powered Credit Scoring Using Alternative Data: Redefining Financial Inclusion
AI-Powered Credit Scoring Using Alternative Data: Redefining Financial Inclusion

July 30, 2025

27

0

Traditional credit scoring has long excluded millions from the formal credit ecosystem—particularly first-time borrowers, gig workers, or informal sector employees. But what if we could understand a person's creditworthiness not from old banking data but from how they interact digitally?

This is where AI-powered credit scoring using alternative data is reshaping financial inclusion, offering fairer, real-time, and explainable credit decisions.

The Problem with Traditional Scores

Traditional systems like CIBIL or Experian rely on:

  • Loan repayment history

  • Credit card usage

  • Existing loan accounts

But:

  • ~350 million people in India lack a credit history.

  • New-to-credit (NTC) borrowers are automatically flagged as high-risk.

We need something smarter. Enter AI + alternative data.

What Is Alternative Data?

Alternative data refers to non-traditional but digitally available behavioral signals such as:

  • UPI transaction history

  • Utility bill payments (electricity, water)

  • Mobile data top-ups & SMS patterns

  • Employer PF/EPFO contribution history

  • eCommerce purchase behavior

This data is real-time, highly contextual, and often more reflective of ability and intent to pay.

AI Model Design for Alternative Credit Scoring

We use a layered architecture for our scoring engine:

1. Data Ingestion Layer

  • Sources: UPI apps, telecom providers, PF APIs, Surepass, ASPAPI

  • Format normalization: JSON schemas

  • Privacy: Tokenization and masking

2. Middleware Connect Platform (MCP)

  • Serves as an API orchestration and abstraction layer

  • Routes traffic to providers, handles auth, rate limits, retries

  • Normalizes and enriches raw data before scoring

  • Adds enterprise readiness and makes this pluggable to CRMs

3. Feature Engineering Layer

  • Sample features:

    • Bill payment consistency

    • UPI inflow/outflow ratio

    • Employment stability (from EPFO records)

    • Recharge frequency

4. Modeling Layer

  • Model: XGBoost (Gradient Boosting Trees)

  • Explainability: SHAP (SHapley Additive exPlanations) for each feature’s impact

  • Training: 500K+ labeled records with known loan repayment outcomes

5. Inference & API Layer

  • Real-time scoring: Response in < 2 seconds

  • Score breakdown: Risk bands (low/medium/high)

  • Output: JSON with score + explanation + reason codes

Risk Mitigation

Even with AI, bias and data leakage risks exist. Here’s how we handle them:

  • Bias Auditing: Regular fairness tests on model outcomes across gender, location, employment

  • Model Governance: Version control, rollback, test logs

  • User Consent: Data is fetched only with explicit permission

  • Security: All transmissions via HTTPS, PII encrypted at rest

Continuous Learning & Feedback Loop

We integrate loan repayment outcomes to:

  • Retrain the model every 3 months

  • Fine-tune thresholds per region or product type

  • Adjust feature weights dynamically (e.g., UPI vs utility weight)

Business Impact

  • 30% increase in approved first-time borrowers

  • 50% reduction in manual underwriting time

  • 25% lower default rate for AI-based scoring group

Final Thoughts

AI is not just replacing old methods—it’s enabling better, faster, and more inclusive credit access. By leveraging alternative data and operationalizing it through a Middleware Connect Platform (MCP), fintechs and lenders can offer real-time credit products to underserved segments without increasing risk.

We’re not building just a model—we’re building a movement for democratized access to credit.

 


That the contents of third-party articles/blogs published here on the website, and the interpretation of all information in the article/blogs such as data, maps, numbers, opinions etc. displayed in the article/blogs and views or the opinions expressed within the content are solely of the author's; and do not reflect the opinions and beliefs of NASSCOM or its affiliates in any manner. NASSCOM does not take any liability w.r.t. content in any manner and will not be liable in any manner whatsoever for any kind of liability arising out of any act, error or omission. The contents of third-party article/blogs published, are provided solely as convenience; and the presence of these articles/blogs should not, under any circumstances, be considered as an endorsement of the contents by NASSCOM in any manner; and if you chose to access these articles/blogs , you do so at your own risk.


images
Giri Venkataramanan
Co-Founder & CTO - MPloyChek

© Copyright nasscom. All Rights Reserved.