Business Introduction
The client is a reputable home loan company with a strong presence across multiple states in India, serving customers in major cities and towns. With over 25 years of experience, the company is deeply committed to providing affordable housing finance to middle-class families. Beyond business, they are renowned for their social responsibility efforts—supporting girls’ education, running an NGO for sheltering distressed women and children, and promoting awareness about women’s health and hygiene in underprivileged communities.
As a leader in the home loan sector, managing timely repayments is critical to their business. However, they faced significant challenges with customers missing their EMIs (Equated Monthly Installments), leading to operational inefficiencies and financial risk.
Business Objectives
The client wanted to proactively identify customers who were likely to default on their loan repayments at the beginning of each month. This would enable early follow-ups and intervention, reduce the rate of delinquency, and improve overall recovery. Relying on manual post-default follow-ups has proven ineffective and often too late to influence outcomes.
Scope of Work
Logesys was engaged to develop a predictive analytics solution capable of forecasting delinquency probabilities using the client’s historical repayment data.
Challenges & Solutions
Challenge 1: Massive Data Volume
With operations spread across India, the client provided over 100,000 records spanning a full year. Handling such a large dataset, especially Excel files, requires efficient data processing to enable accurate modeling.
Solution:
Our team used scalable data wrangling techniques to clean and prepare the dataset. By leveraging Azure ML’s capabilities, we ensured that the large volume of data was processed efficiently without performance bottlenecks.
Challenge 2: Data Quality Issues
The dataset contained null values, spelling inconsistencies (e.g., “college” vs. “clg”), and outliers that threatened the integrity of any model built on it.
Solution:
We conducted thorough data cleansing, including standardizing categorical variables and imputing missing values. This preprocessing step was crucial to build a reliable predictive model.
Challenge 3: Highly Imbalanced Data
About 90% of the records reflected on-time payments, leaving only 10% as delinquent cases. This imbalance risked the model being biased toward predicting non-delinquency, limiting its usefulness.
Solution:
Logesys applied SMOTE (Synthetic Minority Over-sampling Technique) to artificially balance the dataset by generating synthetic examples of minority class (delinquencies). This approach enabled the model to better learn the patterns leading to defaults.
Solution
Logesys designed and implemented a robust predictive solution based on binary logistic regression to classify the likelihood of delinquency. To enhance model accuracy and feature selection, Lasso regression was also utilized.
Both models were developed in Python and executed seamlessly within Azure Machine Learning, providing scalability, automation, and easy deployment.
Results
Delivered within 3 months, the solution has empowered the client to accurately forecast delinquent borrowers ahead of the payment cycle. Key benefits include:
- Proactive follow-ups and reminders to at-risk customers before EMI due dates
- Significant reduction in late payments and missed installments
- More than 50% decrease in delinquency rates over subsequent months
- Streamlined loan recovery process, reducing manual effort and operational costs
The client has expressed high satisfaction with Logesys’ expertise, timely delivery, and impactful results.
Conclusion
This project exemplifies how data-driven insights can transform traditional financial operations. With Logesys’ predictive analytics solution, the client has shifted from reactive collection efforts to a proactive strategy—ensuring more customers pay on time, improving cash flow, and strengthening business resilience. The collaboration highlights the power of combining domain knowledge with advanced machine learning techniques to solve real-world challenges.