International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
CREDIT CARD FRAUD DETECTION AND AUTHENTICATION SYSTEM USING MACHINE LEARNING Kavitha G L1, S Harini Sree2, Sakshi Nagarajarao Jadhav3, Yuktha N4 1Assistant Professor, Dept. of Information Science and Engineering, Bangalore 234Student, Dept. of Information Science and Engineering, Bangalore, Karnataka, India
---------------------------------------------------------------------***--------------------------------------------------------------------One effective way to determine the legitimacy of a Abstract - This project's objective is to create a reliable system that can detect fraudulent transactions and authenticate credit card users before the transaction gets completed. The increase in the use of credit cards for transactions has led to a rise in fraudulent activities, making it crucial to develop a system that can identify and prevent such activities. The system will use various machine learning algorithms, such as decision trees, random forests, KNN algorithm to analyze transactional data and detect any suspicious activities. It will be trained on a dataset containing information on past fraudulent activities to identify patterns and recognize similar fraudulent transactions. Furthermore, the system will implement diverse authentication methods, including face recognition detection and authentication and one-time passwords to verify the legitimacy of the credit card user. The implementation of this system is expected to increase the security of credit card transactions and prevent fraudulent activities by detecting and authenticating the fraud before it takes place, leading to significant savings for consumers and financial institutions.
Key Words: Fraudulent transactions, authentication, onetime passwords, face recognition, KNN Algorithm, Random Forest, Naïve Bayes.
1. INTRODUCTION This research work focuses on the identification of fraudulent transactions made through credit cards. The objective is to create a fraud detection algorithm that can accurately and efficiently classify transactions as either fraudulent or non-fraudulent by utilizing machine learningbased classification algorithms. With the increasing use of online payments and the decline of cash payments, fraudsters are taking advantage of the anonymity provided by these transactions. Online payments, in particular, require only the card number, expiration date, and CVV, making it easy for data to be lost or stolen without the cardholder's knowledge. In some cases, cardholders may not even be aware that their information has been compromised due to fraudulent purchases made through phishing techniques. This highlights the importance of keeping card details private, though there are instances where this is not possible due to the prevalence of phishing sites and cases of lost or stolen cards.
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transaction is to analyze the spending patterns of the cardholder using existing data and applying machine learning algorithms. This can help identify anomalies in spending that may indicate fraudulent activity. There are various types of credit card fraud, including online and offline fraud, card theft, data phishing, application fraud, and telecommunication fraud. It is essential to address these different types of fraud to prevent fraudulent transactions and protect cardholders' data.
2. LITERATURE REVIEW Parth Roy, Prateek Rao, Jay Gajre [1]. IFA suggested using machine learning to identify fraudulent Master Card transactions. The strategies are used to improve the best solution for issues with fraud detection. Methods for reducing false alarm rates and increasing the rate at which scams are discovered are still proven. Since European card holders have had 284,807 communications, data on card transactions continues to be collected. A modified version of these methods can be implemented to the bank's credit card scam detection system to help identify and stop fraud. Ishika Sharma, Shivjyoti Dalai, Venktesh Tiwari, Ishwari Singh , Seema Kharb [2] presented various techniques such as Naive Bayes, Random Forest and Logistic Regression are utilized to tackle this problem. This transaction is evaluated individually, and whatever works best is carried out. The primary purpose is to detect fraud by filtering the aforementioned strategies in order to achieve a better outcome. Anuruddha Thennakoon, Chee Bhagyani, Sasitha Premadasa, Shalitha Mihiranga, Nuwan Kuruwitaarachchi [3offered an evaluation that offers a thorough manual for choosing the best algorithm for the kind of frauds, and we use an adequate performance metric to show the evaluation. In order to determine if a particular transaction is legitimate or fraudulent, they also created the use of predictive analytics performed by the implemented machine learning models and an API module H. Najadat, O. Altiti, A. A. Aqouleh, and M. Younes [4] carried out a thorough experimental investigation using the answers to the imbalance classification issue. They investigated these options and the machine learning fraud
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