FRAUD DETECTION IN BANKING USING MACHINE LEARNING
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Date
2024-03-28
Journal Title
Journal ISSN
Volume Title
Publisher
The European Academic Journal (EAJ)
Abstract
Financial institutions, particularly banks, have a challenge of fraud detection. Fraud poses a substantial financial risk to both institutions and their customers since fraudulent activities can result in significant monetary losses and erode customer trust. Recent research has shown that machine learning techniques can be used to detect fraud in the banking sector.
In this project, we applied logistic regression, random forest, K-Nearest Neighbours, and decision trees to detect fraudulent transactions to the problem of fraud detection in the banking industry. The dataset was obtained from Kaggle and has 31 variables. Logistic regression had the lowest performance metrics with an accuracy of 87.91% while the decision tree had the highest
performance metrics with an accuracy of 97.17%.