Case Study: Credit Risk ML Platform for an NBFC

Durga Analytics | Data & AI Solutions for Financial Services

Client Overview

Client: Leading Non-Banking Financial Company (NBFC) in India
Sector: Retail and MSME Lending
Challenge: Reduce NPAs and automate credit risk decisions
Duration: 4 months (end-to-end delivery)

Business Problem

The client relied on rule-based underwriting, resulting in high delinquency and suboptimal credit decisions. The NBFC needed a scalable ML platform leveraging historical and bureau data to improve credit risk prediction and reduce NPAs.

Our Solution

Impact Delivered

Impact Area Outcome
Approval Accuracy 26% improvement in bad loan detection
Turnaround Time Reduced from 36 hrs to < 6 hrs
NPA Reduction Projected 18–24% drop in new NPAs
Scalability Supports over 1 million annual loan applications
Compliance SHAP explainability met audit & regulator standards

Technology Stack

Data Engineering: PySpark, Airflow
Modeling: Scikit-learn, XGBoost, SHAP
APIs: Flask, FastAPI
Dashboards: Power BI
Infrastructure: AWS EC2, S3, Lambda

Client Testimonial

“The ML-based credit risk platform delivered by Durga Analytics has transformed our underwriting process. The predictive accuracy and real-time decisioning capability have not only improved our portfolio quality but also empowered our field officers to make faster and smarter lending decisions.”
— Chief Risk Officer, NBFC Client

Next Steps

We’re now extending the platform to include top-up loan prediction, prepayment risk, and UPI-based behavioral scoring.

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