vikashkumar040203 / MAJOR-1-Fraud

MAJOR-1

Credit Card Fraud Detection Using Machine Learning

source code link : https://drive.google.com/drive/folders/19jEjiS8WXu3ZXmol0Jogs7OvtuG6NNQd?usp=sharing

Problem Statement

Credit card fraud is a persistent issue that poses significant financial risks to financial institutions. The unauthorized use of credit cards for obtaining goods or services results in substantial financial losses for both cardholders and financial institutions. Traditional fraud detection methods, such as rule-based systems, are becoming increasingly ineffective due to the evolving nature of fraudulent transactions. Fraudsters continuously devise new techniques to bypass traditional detection systems, making it increasingly difficult to identify and prevent fraudulent activities.

Objectives

1. Identify the most effective ML algorithms for detecting fraudulent credit card transactions.
2. Evaluate the performance of different ML algorithms using real-world data.
3. Develop a practical and effective credit card fraud detection system using ML.

Methodology

Frontend

• HTML, CSS, and Bootstrap were used to create the user interface of the application.
• HTML structured the content and provided a semantic layout for the page.
• CSS styled the layout and visual elements, ensuring a consistent design.
• Bootstrap facilitated responsive design and pre-built components for efficient development.

Backend

• Flask and Python served as primary technologies for the backend.
• Flask defined routes, handled user authentication, and interacted with the database.
• Python processed data tasks, such as calculating results or analyzing external data.
• JavaScript added interactive functionality to the frontend, responding to user inputs.
• Third-party libraries or packages used in the backend development should be listed.

Project Members

• Arpita Kumari
• Arihant Vardhan
• Adarsh Tripathi
• Vikash Kumar

Project Guide

• Dr. Akashdeep Bhardwaj

Future Scope

1. Real-Time Fraud Detection and Prevention:

   • Develop a real-time fraud detection system integrated into the credit card payment processing pipeline.
   • Implement real-time scoring and risk assessment for dynamic evaluation of fraud risk.
   • Automate fraud prevention measures for high-risk transactions.

2. Fraud Pattern Analysis and Trend Identification:

   • Analyze detected fraudulent transactions for recurring patterns, trends, and emerging techniques.
   • Develop tools for visualizing and analyzing fraud patterns to gain insights.
   • Share insights with other financial institutions to enhance collective fraud prevention.

3. Integration with External Data Sources:

   • Incorporate additional data sources like social media or customer behavior data to improve fraud detection.
   • Explore federated learning techniques for collaborative fraud prevention while maintaining data privacy.
   • Investigate blockchain technology for securing and sharing fraud detection data.

4. Explainable AI and Model Transparency:

   • Develop techniques for enhancing the explainability of machine learning models.
   • Provide transparent explanations for fraud predictions to stakeholders.
   • Build trust in machine learning models by demonstrating fairness, accountability, and regulatory compliance.

References

1. Jain R., Gour B., Dubey S. (2016). A hybrid approach for credit card fraud detection using rough set and decision tree technique. International Journal of Computer Applications, Vol.139, Issue.10.

2. Rishi Banerjee et.al (2018). Comparative Analysis of Machine Learning Algorithms through Credit Card Fraud Detection. IEEE MIT Undergraduate Research Technology Conference (URTC).

3. Hala Z Alenzi, Nojood O Aljehane (2020). Fraud Detection in Credit Cards using Logistic Regression. International Journal of Advanced Computer Science and Applications, Vol. 11, No. 12.

4. Aashi Maharjan, Partha Chuda (2019). Comparative Analysis of Algorithms for Credit Card Fraud Detection. KEC Conference.