Business Introduction
The client is a well-established home loan company with a vast network of branches across multiple states and cities. With over 25 years of trusted service, their mission is to provide affordable housing finance solutions to middle-class families and improve access to quality housing schemes.
Beyond their core business, they are deeply committed to social causes—actively supporting girls’ education, running an NGO to shelter vulnerable women and children, and promoting health and hygiene awareness among underprivileged women.
As a leading player in the home loan market, managing repayment delinquencies has been a persistent challenge impacting their operational efficiency and growth.
Business Objectives
The client sought a forward-looking solution to predict customers likely to default on their EMI payments before the start of each month. This would allow them to intervene proactively, send timely reminders, and reduce delinquency rates—thereby improving collections and customer relationships.
Manual follow-ups after defaults were inefficient and often too late, sometimes disrupting the company’s broader business strategies.
Scope of Work
Logesys was engaged in designing and delivering a predictive analytics model that could analyze historical repayment data, identify patterns indicating probable delinquency, and provide actionable predictions for upcoming payment cycles.
Challenges & Solutions
Challenge 1: Handling Large Volumes of Data
The client provided nearly 100,000 records covering one full year, sourced from multiple branches. Processing such a large and diverse dataset in Excel format poses significant challenges for data quality and computational efficiency.
Solution:
We implemented scalable data processing workflows using Azure Machine Learning to clean, transform, and prepare this data efficiently. This ensured that the model was trained in reliable and well-structured data without performance issues.
Challenge 2: Data Quality and Consistency Issues
Inconsistencies like null fields, misspellings (e.g., “college” vs. “clg”), and outliers were prevalent. Such errors could mislead the model and reduce its prediction accuracy.
Solution:
Our data scientists performed comprehensive data cleaning and normalization, including standardizing categorical variables and imputing missing values, thereby ensuring the data fed into the model was accurate and meaningful.
Challenge 3: Imbalanced Dataset Bias
With 90% of records reflecting on-time payments, the dataset was heavily skewed towards non-delinquent cases. This imbalance risked biasing the model to predict no delinquency too often, weakening its predictive power for actual defaulters.
Solution:
Logesys applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples of the delinquency cases, balancing the workload and enabling the model to learn the subtle indicators of default more effectively.
Solution
We developed a binary logistic regression model, supplemented by Lasso regression for feature selection and improved prediction accuracy. These models were implemented in Python and deployed on Azure Machine Learning, allowing automated retraining and scalability as new data becomes available.
Results
The project was completed within three months, delivering a predictive model that accurately identifies customers at risk of missing payments in the upcoming cycle. Key outcomes include:
- Early identification enabling timely customer outreach and reminders
- Over 50% reduction in delinquency rates observed in the following months
- Streamlined collection process, reducing manual effort and follow-up delays
- Enhanced operational efficiency and improved cash flow management
The client praised the solution for its accuracy, ease of use, and tangible impact on their business.
Conclusion
With Logesys’ expertise, the client transformed their approach to loan repayment management—shifting from reactive to proactive delinquency handling. This project showcases how the power of data science and machine learning can drive real-world business improvements, supporting financial inclusion and responsible lending.
By predicting risks in advance, the client not only protects their bottom line but also strengthens customer trust and experience—fueling sustained growth in a competitive market.