Efficient Bank Fraud Detection with Machine Learning

Abstract

The rapid expansion of IT technology has led to a significant increase in financial transactions, accompanied by a corresponding rise in fraudulent activities. This paper tackles the challenge of detecting fraud in various forms, such as credit card fraud, banker cheque fraud, and online funds transfer fraud, which have become increasingly sophisticated. Traditional methods struggle to keep pace with these evolving fraud techniques, necessitating advanced approaches. We propose the use of machine learning algorithms to enhance the detection of fraudulent transactions. Utilizing the BankSim dataset from Kaggle, which includes features like age, gender, payment domain, and transaction amount, we conducted a comprehensive analysis. The dataset was preprocessed to handle missing values and balance the instances of fraud. We then applied several machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM), training these models on a training set and evaluating them on a test set. The performance of these models was assessed using precision, recall, and F1-measure metrics. Our findings demonstrate that the SVM algorithm achieved the highest accuracy at 99.23%, significantly outperforming the other algorithms and previous studies. This study highlights the effectiveness of machine learning, particularly SVM, in developing robust fraud detection systems, offering a promising solution to improve financial security.