Banking Customer Transaction Analytics using Oracle SQL

About the Project

I created this project to practice Oracle SQL by analyzing a real-world banking dataset. The dataset contains information about customers, accounts, transactions, loans, and districts.

The dataset was downloaded from Kaggle, and I used it to practice writing SQL queries that answer business-related questions. The project includes data validation, data cleaning, customer analysis, transaction analysis, loan analysis, and advanced SQL concepts.

Dataset

The dataset used in this project was obtained from Kaggle. It contains eight related tables:

• Accounts
• Clients
• Cards
• Disposition
• District
• Loan
• Order
• Transactions

Tools Used

• Oracle SQL
• Oracle SQL Developer
• GitHub

Project Structure

Banking_Customer_Transaction_Analytics_Oracle_SQL │ ├── Dataset ├── SQL Scripts ├── Screenshots └── README.md

What I Worked On

Data Validation

• Verified imported tables
• Checked row counts
• Previewed the data before analysis

Data Cleaning

• Checked for duplicate records
• Checked for NULL values
• Renamed district table columns for better readability

Customer Analytics

• Total customers
• Customers by region
• Customers with multiple accounts
• Customers without loans

Transaction Analytics

• Transaction overview
• Transaction type analysis
• Banking operation analysis
• Monthly transaction trends
• Transaction purpose analysis
• Top customer accounts

Loan Analytics

• Loan status analysis
• High-risk loans
• Loan payment analysis

Advanced SQL

The project includes the use of:

• Joins
• Aggregate Functions
• GROUP BY
• HAVING
• CASE Expressions
• Window Functions (RANK, DENSE_RANK, LAG)
• Common Table Expressions (CTEs)
• Views
• Indexes

Business Insights

Some key findings from the analysis include:

• Customer distribution varies across different regions.
• Many customers do not currently have loans, making them potential targets for future loan offerings.
• Cash withdrawal is the most frequently used banking operation.
• A small number of accounts contribute a significant share of transaction value.
• High-risk loans can be monitored to reduce financial risk.

Screenshots

The Screenshots folder contains sample outputs from the SQL queries used in this project.

Skills Demonstrated

• Oracle SQL
• Data Validation
• Data Cleaning
• Joins
• Aggregate Functions
• Window Functions
• Views
• Indexes
• Business Analysis

Author

Susmitha T

This project was built as part of my Data Analyst portfolio to strengthen my SQL skills using a real-world banking dataset.