MULTI-CLASSIFIERS SYSTEM FOR CREDIT CARD FRAUD DETECTION

The business of issuing credit cards is extremely important to the functioning of the economy since it facilitates the use of a straightforward method of payment in a variety of contexts, such as online banking, commercial transactions, and financial dealings. Nonetheless, using credit cards is also associated with fraudulent activity and non-payment, both of which constitute a considerable danger to customers and the business as a whole. Detecting and preventing fraudulent activity involving credit cards is complex and time-consuming because of the ever-changing nature of fraudulent and expected behavior and the unequal class label and overlapping of class instances inside the data sets. The class imbalance in credit card data sets poses a significant obstacle in detecting fraud. Biased models may result from a substantially lower number of fraudulent cases compared to non-fraudulent cases. The study underlines the influence of oversampling and under-sampling techniques on single-classifier methods and stresses the significance of selecting an appropriate classifier algorithm. The results indicate that multi-classifier methods, particularly COPOD + RFC and IForest + RFC, can substantially improve credit card fraud detection compared to single-classifier methods. These results demonstrate the potential advantages of integrating multiple unsupervised and supervised learning algorithms to improve credit card fraud detection while decreasing false positives. Overall, the study emphasizes the significance of employing a combination of machine learning techniques to resolve the difficulties of credit card fraud detection. The proposed method can assist credit card companies in accurately and efficiently identifying fraudulent activities, thereby reducing the risk of financial loss and enhancing customer confidence.