We cannot ask a human person to check all transactions one by one if it is fraudulent or not. In other words banks and companies need automatic systems able to support fraud detection. Credit card frauds may occur in various ways. just to mention some, we can have stolen card fraud, cardholder-not-present fraud and application fraud:
Stolen card fraud is the most common type of fraud where the fraudster usually tries to spend as much as possible and as quickly as possible.
Cardholder-not-present fraud is often observed in e-business.
Application fraud corresponds to the application for a credit card with false personal information.
With this extensive use of credit cards, fraud appears as a major issue in the credit card business.
Credit card fraud detection relies on the analysis of recorded transactions.
Fraud detection is a collection of processes and techniques designed to identify, monitor, and prevent fraud.
Detecting fraud is the first step in identifying where the risk lies. You can then prevent it automatically or even manually using some fraud detection software.
- In this project, we are required to use Excel and Google Sheets for data cleaning and analysis, and then provide data visualization in Tableau. First of all, I have used python to provide a sample of my main dataset and then import it in Excel and Tableau to do my data analysis.
The main dataset contains transactions made by credit cards in 2016 by Capital One, including 641914 instances and 29 columns. and my sample data includes 1300 instances and 29 columns.
- Available Money
- Credit Limit
- Current Balance
- Transaction Amount
- Merchant Name
- Merchant Category
- Transaction Type
- Data sampling: Python
- Data cleaning and analysis: Excel, Google Sheets
- Visualizations: Excel, Google Sheets, Tableau
- Data: Output2.csv & Output3.csv
- business_project.xlsx
- business_project_data_sampling.ipynb
- Presentation slides used to present this project: Presentation_Business.pdf
- Write up the project.
- https://fraud-detection-handbook.github.io/fraud-detection-handbook/Foreword.html
- https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.403.2235&rep=rep1&type=pdf
- https://www.kaggle.com/code/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets