Financial Transaction Analysis

Project Overview

This project demonstrates an end-to-end financial transaction analysis using SQL and Excel. The objective is to convert raw transaction data into business-ready insights through clearly defined KPIs and a single-page finance dashboard.

The analysis focuses on revenue performance, operational efficiency, fraud risk visibility, and customer value concentration.

Dataset

• Approximately 5,000 financial transaction records
• Synthetic and anonymized dataset
• Includes multiple countries and transaction channels

Key Fields

• transaction_id
• customer_id
• transaction_date
• transaction_type (CREDIT / DEBIT)
• transaction_status (SUCCESS / FAILED)
• amount
• channel
• country
• fraud_flag

Tools Used

• SQL for data extraction, aggregation, and KPI calculations
• Microsoft Excel for KPI summaries, pivot tables, and dashboard creation

Key KPIs

• Total Revenue – successful credit transactions
• Total Spend – successful debit transactions
• Failed Transaction Percentage – operational efficiency metric
• Fraud Transactions Count – risk indicator
• Top Customer Value – customer concentration metric

Dashboard Summary

The Excel dashboard highlights:

• Channel-wise failed transaction amount
• Country-wise revenue distribution
• Monthly revenue trend
• Top 10 customers by transaction value

Workflow

1. Raw transaction data analyzed using SQL
2. KPIs calculated and validated
3. Aggregated results visualized in Excel
4. Final one-page finance dashboard prepared