Case Study

Reduce Loan Drop-Out

Analytical Framework

One of Africa’s largest education lenders with a long operating history and a large student loan portfolio.

Type

Industry

Fintech

Categories

AI GovernanceCollections & Recovery

Industry

Model Risk Management

The Challenge

Most organizations understand their customers only after a transaction is completed.

However, the biggest opportunity lies in understanding prospects during the onboarding journey—while decisions are still being made. In lending, a significant number of prospects abandon the process even after loan approval. This leads to: – Lost revenue – Higher customer acquisition costs – Inefficient sales and onboarding processes Around 40% of consumers abandon onboarding during new banking relationships. Identifying who is likely to drop out—and why—in real time is critical to improving conversion rates.

The Solution

iTuring.ai enabled proactive intervention during the onboarding journey using machine-learning-driven prediction models.

iTuring: – Analyzed historical onboarding and drop-off behavior – Predicted which prospects were likely to abandon onboarding – Identified individual-level reasons for drop-out in real time For the education lending company, iTuring built a Non-Take-Up Model that identified high-risk prospects immediately after loan risk assessment and approval. The business used these insights to: – Deliver personalized offers in real time – Adjust product and policy programs – Improve onboarding experience and reduce acquisition costs

IMPACT

Onboarding (Loan Take-Up) Rate

14% ↑

Revenue Increase

USD 0.6 million ↑ annually

Model Accuracy

82%

WHY ITURING.AI

iTuring helps businesses act before customers abandon.

The platform:
Predicts which prospects are likely to drop out
Explains why abandonment is likely at an individual level
Enables real-time, personalized acquisition strategies
Reduces cost and risk in customer acquisition

FAQs

01

1. What is loan drop-out or non-take-up?

+

It refers to customers abandoning the onboarding process after loan approval but before completing the loan setup.

02

2. Why was drop-out a major issue for the lender?

+

High drop-out rates led to lost revenue and increased acquisition costs despite successful loan approvals.

03

3. How did iTuring predict onboarding abandonment?

+

iTuring analyzed historical onboarding behavior and built models to predict abandonment risk in real time.

04

4. How did the business act on these predictions?

+

The lender delivered personalized offers and adjusted product and policy programs to improve completion rates.

05

5. What outcomes did this solution achieve?

+

The lender increased onboarding rates by 14% and generated an additional USD 0.6 million in annual revenue.

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