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Due to the rapid advancement of electronic commerce technologies, the use of credit cards has increased significantly. Given that credit cards are the most common form of payment, the incidence of credit card fraud has also risen. With the rise of online money transfers in the cashless economy and the migration of businesses to the internet, fraud detection has become a crucial aspect of transaction security. With the advent of technological advancement and the emergence of new e- service payment options, such as e-commerce and mobile payments, credit card transactions have become more common. To prevent credit card fraud, a robust and reliable fraud detection system is necessary. Several approaches, including predictive approaches and algorithms, have been proposed to detect credit card fraud. These algorithms establish a set of logically sound principles that permit the classification of data as either normal or dubious. However, credit card fraud has persisted despite the adoption of more sophisticated techniques. This study presents an approach for detecting credit card fraud using random forests. The dataset and the user's current dataset are analysed using the random forest technique. Before analysing a subset of given data to detect fraud, the method increases the precision of the outcome data. In addition, a comprehensive comparison and analysis of current and future fraud detection measures is presented. The dataset is applied Random Forest-based classification models, and the model's performance is evaluated using graphical representations of precision and classification accuracy. |
