Detection of Malicious Activity on Credit Cards Using Machine Learning

In the contemporary digital era, credit card fraud has become a top concern for financial institutions and credit card companies. Machine learning algorithms are routinely used to detect fraudulent transactions in real time by examining enormous amounts of historical transaction data and discovering patterns of fraudulent behavior. The thought drifts, on the other hand, may make this process more challenging because factors may alter over time in unanticipated ways, leading to data imbalances and lowering the precision of fraud detection algorithms. To overcome the issue of notion drift, this research uses a feedback mechanism that adapts to changing circumstances over time. This approach relies on keeping track of crucial transaction data and forecasting fraud using the SVM, random forest, and decision tree algorithms. The research looks for the classifier with the highest rating value in an effort to create a reliable method of detecting fraud in credit card transactions. The challenges caused by idea drift in the detection of card fraud appear to be amenable to being overcome by the suggested technique. This system, which continuously adapts to changing parameters over time, may boost the accuracy and dependability of the finding of cheating models in a real-world situation.