Evaluating Machine Learning Systems for Banking Fraud Recognition: A Comprehensive Research

Abstract

Financial institutions must prioritize the detection and prevention of fraudulent activities due to the increasing use of digital banking. Integrating intelligent systems is necessary since conventional rule-based systems can't detect new and complicated forms of fraud. Various machine learning (ML) algorithms for banking fraud detection are covered in this study. These algorithms range from more traditional classifiers like Logistic Regression (LR) and Decision Trees (DT) to more advanced models like Random Forest (RF), AdaBoost (AB), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Class imbalance, idea drift, and real-time fraud detection are some of the issues brought up in the review.  Research shows that hybrid and ensemble models greatly improve recall, precision, and accuracy. Hybrid methods that combine models, such as HMM and Gradient Boosting (GB), provide enhanced adaptability, while techniques such as Synthetic Minority Oversampling Technique (SMOTE) handle data imbalance. F1-score, recall, accuracy, and precision are among the common measures used in performance evaluation. The paper concludes by identifying the strengths and limitations of each method. It suggests future directions, including using explainable AI and real-time learning for more effective fraud detection systems.