Credit Card Fraud Detection Using Machine Learning

Abstract

1. Introduction

Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information. Many machinelearning algorithms can be used for detection. This project proposes to develop a machine-learning model to detect credit card fraud. The model will be trained on a dataset of historical credit card transactions and evaluated on a holdout dataset of unseen transactions. Keywords: Credit Card Fraud Detection, Fraud Detection, Fraudulent Transactions, K- Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree.

With the increase of people using credit cards in their daily lives, credit card companies should take special care of the security and safety of the customers. According to (Credit card statistics 2021), the number of people using credit cards worldwide was 2.8 billion in 2019; also, 70those users own a single card. Reports of Credit card fraud in the U.S. rose by 44.7in 2020. There are two kinds of credit card fraud, and the first is having a credit card account opened under your name by an identity thief. Reports of this fraudulent behaviour increased 48to 2020. The second type is when an identity thief uses an existing account you created, usually by stealing the information on the credit card. Reports on this type of Fraud increased 9to 2020(Daly, 2021). Those statistics caught We’s attention as the numbers have increased drastically and rapidly throughout the years, which motivated We to resolve the issue analytically by using different machine learning methods to detect fraudulent credit card transactions within numerous transactions.

2. Objective

Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.

This project demonstrates how Jupyter Notebook, Machine Learning, Python can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/shakiliitju/Credit-Card-Fraud-Detection-Using-Machine-Learning.git
cd Credit-Card-Fraud-Detection-Using-Machine-Learning

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by shakiliitju and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.