Credit Card Fraud Detection using Machine Learning

Abstract

Evolving technologies make human life easier with increasing challenges. Online payments have become an integral part of our lives in the era of digitalization. The credit card payment system has made transactions hassle-free. This led to E-Commerce appraisal. Digitalization of transactions has given rise to new forms of fraud and cyberattacks that can affect individuals and organizations. This had set hackers at a great deal to steal the cardholder details using different schemes. Credit card companies must recognize these fraudulent transactions at the earliest to retain credibility among the stakeholders. Traditional methods of fraud detection have proven ineffective in identifying and preventing these fraudulent activities and cyberattacks in real time. This paper discusses various Machine Learning algorithms that predict fraudulent transactions in real-time. Fraudulent activities are solved using data science and machine learning techniques with substantial processing power and the capacity to manage massive datasets. The model is trained on large volumes of the dataset. This paper emphasizes comparison of various machine learning algorithms' performance over the input. The accuracy and efficiency of several machine learning algorithms are measured and analyzed through tabulation and comparison. The trained model is integrated with a website to categorize financial transactions as either legitimate or fraudulent. On utilizing advanced machine learning algorithms, credit card fraud detection systems have become more refined and accurate in recent years. As a result, financial organizations and customers are protected against such fraudulent activities, leading to increased trust and confidence in utilization credit card payments.