Fraud Detection and Authentication of Credit Card using ML
Shoaib Attan Khan, Shubham Arora, Tiwari Sahil Mukesh, Zeeshan Khan, Prakruthi M K
Fraud detection, deep learning, machine learn- ing, online fraud, credit card fraud, transaction data analysis.
This study aims to tackle the increasing issue of potential fraudulent activities within the sphere of credit card transactions. As the number of card transactions increases, there is a vital necessity for a strong and reliable system that could address and highlight such transactions as potentially fraudulent. The study suggests employing machine learning techniques and algorithms, more precisely decision trees and a random forest, for analyzing transactional data and selecting suspicious patterns indicative of such behavior. The system’s training on a dataset containing historical information about fraudulent activities enables it to learn and recognize similar patterns, providing an advanced and proactive method of approaching to tackle credit card fraud. In addition to machine learning, the research implements application of varied authentication approaches, such as one-time passwords and security questions, to confirm the authenticity pertaining to credit card. By integrating these authentication measures with the analytical power of Naive Bayer’s machine learning model, the system showed an accuracy of 98.75%. The expected outcome is not limited to finding and stopping fraud but also the enhanced authentication course, which leads to billions of dollars saved on an annual basis for consumers and financial organizations together. In the end, it is the goal of this study to generate a reliable solution to secure transactions involving credit cards relying on an algorithmic synergy for machine learning alongside strong authentication.
Article Details
Unique Paper ID: 163982

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 953 - 958
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