Abstract: Recent analysis have identified a significant rise in transactional fraud, where bad actors seek to deceive individuals or firms into unauthorized financial actions. Traditional fraud detection systems frequently struggle to effectively identify such activities, leading to financial damages and security breaches. Addressing this problem requires employing sophisticated machine learning techniques specially designed to detect transactional fraud. This study presents a novel fraud detection method called “filter”, aimed at uncovering misleading transactional behaviours. By employing tailored features to reveal fraudulent patterns and activities, our filter achieves a remarkable accuracy of over 99.01% in distinguishing fraudulent transactions from legitimate ones, while maintaining a low false positive rate. Our approach was evaluated with a dataset comprising 746 instances of fraudulent transactions and 4822 instances of legitimate transactions. The results underscore the superior performance of our filter compared to existing methods, particularly in accurately detecting fraudulent transactions. Moreover, our hybrid NB-ANN model achieves the highest accuracy of 99.01%, outperforming both Naïve Bayes (98.57%) and Artificial Neural Network (98.12%) techniques. This highlights the effectiveness of the hybrid method in boosting detection accuracy for transactional fraud. Implementing our filter and leveraging the hybrid NB-ANN model, organizations can greatly improve their ability to detect and prevent fraudulent activities thereby protecting their financial assets and maintaining customer trust.
Keywords: Machine learning, Predictive model, transaction fraud, dataset, hybrid NB-ANN, Filter, Legitimate.
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DOI:
10.17148/IJARCCE.2025.14601
