Credit Card Fraud Transaction Detection Using a Hybrid Machine Learning Model

The rapid growth of online financial transactions, predominantly facilitated by credit cards, necessitates a robust security framework to mitigate potential financial loss in cases of card compromise. The financial impact on individuals facing credit card loss or unauthorized access to their card information is significant. Malicious actors, commonly known as hackers, exploit vulnerabilities in the online environment with the intent to defraud individuals. Safeguarding online credit card transactions is imperative to maintain trust and security in digital financial transactions. This research addresses the pressing need for enhanced security by leveraging machine learning techniques to identify and prevent online credit card fraud effectively. The primary objective of this initiative is to develop an advanced system capable of analyzing and combatting credit card-related fraud. A key aspect of this endeavor involves implementing a hybrid machine-learning model that discerns between illegitimate and legitimate transactions. The proposed approach integrates AdaBoost, logistic regression, and random forest techniques to construct a powerful hybrid model. Through a comprehensive training process, this model gains the ability to accurately predict output values, thereby enabling real-time identification of potentially fraudulent transactions. By amalgamating these machine learning methodologies, the system enhances fraud detection and significantly contributes to the overall security of online credit card transactions.