FRAUD DETECTION IN BANKING USING MACHINE LEARNING

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dc.contributor.authorJade Gesare Abuga
dc.contributor.authorEditah Hadassa Abuto
dc.contributor.authorRoy Kuria
dc.date.accessioned2024-12-16T14:02:00Z
dc.date.available2024-12-16T14:02:00Z
dc.date.issued2024-03-28
dc.description.abstractFinancial 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%.
dc.identifier.urihttps://ir.ueab.ac.ke/handle/123456789/321
dc.language.isoen_US
dc.publisherThe European Academic Journal (EAJ)
dc.relation.ispartofseries2; 1
dc.titleFRAUD DETECTION IN BANKING USING MACHINE LEARNING
dc.typeArticle
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