Case Study

Improve Collections and Optimize Efforts

Analytical Framework

One of India’s largest NBFCs offering flexible loan products and innovative repayment plans, operating with multiple lending partners and a growing loan portfolio.

Type

Industry

Fintech

NBFCs play a critical role in financial inclusion by offering customer-focused loan products and flexible repayment plans.

While this approach enables growth and differentiation, it also increases credit and collection risk. For this NBFC, key challenges included: – Managing rising delinquency and default risk – Improving cash flow through better collections – Allocating limited collections resources efficiently – Reducing losses while supporting growth With higher risk tolerance and customized products, the company needed a data-driven way to optimize collections without increasing effort.

The Solution

iTuring.ai was used to build predictive default and delinquency movement models across the entire loan portfolio.

The solution: – Predicted customer movement across delinquency buckets (pre-delinquency, early, late, and recovery) – Forecasted defaults for the immediate next month – Provided behavioral explanations to guide collection strategies With an existing default rate of approximately 12%, iTuring developed models achieving ~86% predictive accuracy, enabling monthly portfolio-level risk management. Customers were segmented into 9 risk groups based on: – Probability of default – Value at risk By focusing collection efforts on the top 30% highest-risk customers, the company captured 72% of likely defaulters, significantly improving collections efficiency.

IMPACT

Collections

116% ↑

Predictive Accuracy

86%

Time to Deployment

2 weeks

WHY ITURING.AI

iTuring helps NBFCs move from reactive to precision-led collections.

The platform:

Accurately predicts which customers are likely to default

Segments customers by risk and value at risk

Enables focused collection strategies with maximum impact

Improves cash flow without increasing operational load

FAQs

1. What collections challenge was the NBFC facing?

The NBFC needed to improve cash flow and reduce default risk without increasing collection resources.

2. How did iTuring help predict customer defaults?

iTuring built models that forecasted delinquency movement and default risk month over month across the portfolio.

3. Why were customers segmented into risk groups?

Segmentation helped the collections team prioritize customers based on default probability and value at risk.

4. How did the business optimize collections effort?

By targeting only the top 30% high-risk customers, the company captured 72% of likely defaulters.

5. What measurable impact did this solution deliver?

The NBFC improved collections by 116% with the same collection effort and deployed the solution in just 2 weeks.

Contact us to find out more

Unlock the power of AI with iTuring.ai!

Share this resource

Related Case Study