Introduction
This project is a comprehensive data analysis of banking transactions to identify patterns indicative of fraudulent activity. The goal is to demonstrate a workflow for data cleaning, analysis, and visualization using professional tools like Excel and Power BI to derive key insights.
Disclaimer
The dataset used in this project is a fictional, anonymized file created for educational and portfolio purposes only. It is not connected to or representative of Canara Bank or any other real financial institution. The analysis and findings are based solely on this sample data and should not be used for any real-world financial or legal decisions.
Features
- Data cleaning and preprocessing to prepare the dataset for analysis.
- Creation of an interactive dashboard in Power BI to visualize key metrics.
- Identification of fraudulent transaction patterns and trends.
- Summary of key findings and recommendations.
Tools & Technologies
- Microsoft Excel: Used for initial data manipulation and cleaning.
- Power BI: Employed for data visualization and dashboard creation.
Methodology
- Data Ingestion: The raw
.xlsxfile was imported into both Excel and Power BI. - Data Transformation: Data was cleaned by handling missing values and incorrect data types.
- Visualization: A dashboard was built to display key performance indicators (KPIs) and charts to visualize transaction trends, fraud rates, and other relevant metrics.
- Analysis: Key insights were extracted from the dashboard to understand the characteristics of fraudulent transactions.