Article
Adaptive Machine Learning Approach for Banking Transaction Fraud Identification
Financial fraud has become one of the major challenges faced by banking and digital payment systems due to the increasing volume of online transactions. Detecting fraudulent activities at an early stage is essential for minimizing financial losses and improving the security of banking services. This paper presents a machine learning-based fraud detection system that identifies suspicious banking transactions by analyzing historical transaction records. Before model training, the collected dataset undergoes preprocessing to eliminate missing values, remove non-numeric attributes, and transform the data into a suitable format for classification. Several supervised learning algorithms, including Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Decision Tree, AdaBoost, Multi-Layer Perceptron (MLP), Deep Neural Network (DNN), and Random Forest, are implemented and evaluated using performance measures such as accuracy, precision, recall, and F1-score. Experimental analysis shows that the Random Forest classifier outperforms the remaining models by providing the highest classification accuracy and more reliable fraud prediction. The developed application offers an interactive interface for dataset preprocessing, model training, algorithm comparison, and fraud prediction, enabling users to analyze transaction data efficiently. The comparative evaluation demonstrates that ensemble learning techniques can significantly improve the detection of fraudulent banking transactions while reducing false classifications. The proposed framework provides an effective, scalable, and practical solution for enhancing fraud detection capabilities in modern banking systems and supporting secure digital financial transactions.
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