The Challenge
A bank needed to expand its personal loan business by converting existing depositors into borrowers. With a current acceptance rate of only 9.6% (480 of 5,000 customers), broad marketing campaigns were expensive and inefficient. The bank needed a data-driven approach to identify which customers would be most receptive to a personal loan offer.
Approach & Methodology
The project followed a structured ML pipeline: Data Collection → Data Cleaning → Feature Engineering → Exploratory Data Analysis → Model Building → Pruning & Tuning → Evaluation & Selection.
Key steps:
- Analyzed 5,000 customer records across 14 attributes (demographic, financial, banking)
- Applied IQR method for outlier detection
- Used one-hot encoding for categorical variables
- Addressed significant class imbalance (90.4% vs 9.6%)
- Built Decision Tree classifier optimized for recall (minimizing missed opportunities)
Key Findings
The model identified four primary drivers of loan acceptance:
See How the Model Would Classify Your Customer
Adjust the customer attributes below to see how the model would classify loan acceptance probability based on the key findings above.
Disclaimer: This demo is built on a third-party training dataset for educational purposes only. It should not be used to evaluate real customers or inform actual lending decisions.
Business Impact
- Enables highly targeted campaigns reducing costs by 60–70% vs broad outreach
- Dramatically improves conversion rates through precise customer segmentation
- Converts existing depositors into borrowers, expanding loan portfolio
- Identifies high-value segments for focused product development