Fintech SQL Analysis

This project demonstrates SQL analysis on a sample fintech banking dataset. It covers customer profiles, accounts, loans, transactions, and support tickets, then uses SQL queries to explore customer segmentation, balances, loan behavior, support activity, and window-function based rankings.

Project Files

• Fintech SQL Analysis Cleaned.sql - database setup, sample inserts, and analysis queries
• ER_Diagram.md - Mermaid ER diagram for the database schema
• dataset/ - CSV exports of each sample table

Database Schema

The project uses five related tables:

• CUSTOMERS - customer demographic and credit information
• ACCOUNTS - account type, balance, status, and open date
• LOANS - loan amount, interest rate, and approval status
• TRANSACTIONS - credit/debit transactions linked to accounts
• SUPPORT_TICKETS - customer support issues and resolution time

Key SQL Concepts Used

• Table creation with primary keys and foreign keys
• INSERT statements for sample data
• String functions such as CONCAT, UPPER, LEFT, and SUBSTRING_INDEX
• Null handling with COALESCE and NULLIF
• Conditional logic with CASE
• Inner joins
• Aggregate functions such as COUNT, MAX, and AVG
• Subqueries
• Common table expressions
• Window functions such as RANK, DENSE_RANK, and running totals

Example Analysis Questions

• Which customers have credit scores above 750?
• Which account types have above-average balances?
• Who are the top two customers by balance within each account type?
• Which customers have never taken a loan?
• Which customers have raised support tickets?
• Which loans have interest rates below the overall average?
• Which customers have balances above the average for their city?

How to Run

1. Open MySQL Workbench or another MySQL-compatible SQL client.
2. Run Fintech SQL Analysis Cleaned.sql.
3. The script creates the FINTECH_ANALYTICS database, loads sample data, and includes analysis queries.

ER Diagram

The ER diagram is available in ER_Diagram.md. GitHub can render the Mermaid diagram directly in Markdown.

Dataset

The dataset folder contains CSV files matching the inserted SQL sample data:

• customers.csv
• accounts.csv
• loans.csv
• transactions.csv
• support_tickets.csv

Tools

• SQL
• MySQL
• GitHub Markdown
• Mermaid ER diagrams