This project showcases an end-to-end analysis of Home Loan applicants using SQL. The study combines technical SQL querying with business insights to understand customer patterns, product performance, and operational efficiency within a lending framework.
The analysis examines customer demographics, loan applications, sanctions, disbursals, recoveries, and delinquency trends — transforming raw data into actionable business intelligence. It demonstrates proficiency in data extraction, aggregation, and interpretation using SQL for analytical problem-solving.
- Analyze customer distribution across gender, age, occupation, and income groups.
- Evaluate loan application patterns and product preferences.
- Assess sanction, disbursal, and recovery performance metrics.
- Identify branch and channel performance variations.
- Measure delinquency trends to highlight potential risk areas.
The dataset contains anonymized records of home loan applicants, including demographic, financial, and loan transaction details. It was provided by a mentor for analytical and educational purposes.
- Database: MySQL
- Processing & Analysis: SQL Queries
- Visualization & Reporting: Excel, Word / PDF
- The loan sanction rate stands at 100%, reflecting efficient screening and approval processes.
- Salaried customers aged 26–32 dominate the applicant pool, showing strong engagement from early-career professionals.
- The Online channel drives most applications, emphasizing digital adoption.
- Loans + Group Insurance is the most profitable product with the highest sanctioned value.
- Delinquency rates remain stable across demographics (~66%), indicating balanced risk exposure.
- Urban branches like Mumbai and Bengaluru lead in both disbursal and recovery volumes.
The project report includes detailed SQL outputs, interpretations, and summarized results across key analytical categories:
- Customer Demographics
- Loan Application Analysis
- Sanction & Disbursal Trends
- Recovery Performance
- Product, Branch, and Channel Analysis
- Financial & Risk Evaluation
📄 Detailed Report: Refer to the attached Home_Loans_Report.pdf for the complete analysis and SQL output documentation.
- Strengthen focus on digital loan channels due to high conversion and engagement.
- Maintain and expand bundled product offerings like Loans + Insurance to enhance profitability.
- Prioritize risk monitoring for mid-career segments (ages 44–55) showing slightly higher delinquency.
- Leverage insights from high-performing branches (Mumbai, Bengaluru) for replication in emerging regions.
Name: [Ashirbad Routray] Date: [25.10.2025]
This project demonstrates practical SQL data analysis and business interpretation — suitable for data analyst and business intelligence roles.