This project analyzes retail banking data to understand customer financial behavior, loan performance, and credit utilization.
The goal is to derive business-relevant KPIs and insights that support lending decisions, customer segmentation, and risk assessment.
The analysis is based on structured banking data covering:
- Customers and demographics
- Savings and current accounts
- Loans and credit cards
- Transactions and loan status
Key focus areas include:
- Customer balance patterns across age groups
- Loan approval and rejection trends by loan type
- Credit utilization behavior and lending KPIs
Insight:
Customers aged 36–50 and 51–65 maintain higher average balances, especially in savings accounts, indicating stronger financial stability during mid to late career stages.
Insight:
Mortgage loans show higher approval volumes, while personal loans have comparatively higher rejection counts, reflecting stricter risk evaluation for unsecured lending.
A Power BI dashboard was built to provide a consolidated view of loan performance, credit utilization, and key lending KPIs.
Dashboard Highlights
- Total Loan Amount issued
- Loan Approval Rate
- Average Credit Utilization
- Loan status distribution (Approved, Closed, Rejected)
- Loan amount by loan type
- Credit utilization distribution
- SQL (database schema and table creation)
- Python (Pandas, NumPy, Matplotlib)
- Jupyter Notebook for analysis
- Power BI for interactive dashboards
- Identified financially strong customer segments for targeted banking strategies
- Highlighted loan types with higher rejection risk
- Enabled KPI-driven monitoring of lending and credit utilization
- Supported data-backed decision-making for retail banking operations