APPLYING MACHINE LEARNING TO AUDIT DATA: ENHANCING FRAUD DETECTION, RISK ASSESSMENT AND AUDIT EFFICIENCY

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dc.contributor.authorÖzbaltan, Nihan
dc.date.accessioned2025-03-20T09:44:56Z
dc.date.available2025-03-20T09:44:56Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractMachine learning (ML) is used globally as a tool for predictive analysis. Within auditing, the use of audit data helps to uncover fraud indicators, identify risk areas and implement predictive models for continuous audit monitoring. Researchers are using various machine learning methods to analyze large and complex audit data to facilitate prediction. In this study, an online UCI dataset of 776 lines and 27 features is used. Out of these 27 features, 13 are eliminated due to their low impact on the target dataset or due to the ‘important feature selection’ algorithm. In this analysis, I used supervised learning methods, namely K-Nearest Neighbors, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, Gaussian Naive Bayes, Extra Tree Algorithm, Gradient Boosting Algorithm, Ada Boosting Algorithm and XGBoost Algorithms. The experimental results highlight the power of eight neighbor KNN and evaluate its effectiveness, sensitivity, precision, accuracy and F1 score in comparison with other methods such as Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier and Random Forest Classifier. © Copyright 2024 Taylor & Francis–All rights reserved.
dc.identifier.doi10.1080/07366981.2024.2376793
dc.identifier.endpage86
dc.identifier.issn0736-6981
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85198523016
dc.identifier.scopusqualityQ3
dc.identifier.startpage70
dc.identifier.urihttps://doi.org/10.1080/07366981.2024.2376793
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2083
dc.identifier.volume69
dc.indekslendigikaynakScopus
dc.institutionauthorÖzbaltan, Nihan
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofEDPACS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250319
dc.subjectalgorithm
dc.subjectdata set
dc.subjectdetection method
dc.subjectexperimental study
dc.subjectmachine learning
dc.subjectrisk assessment
dc.subjectsupervised learning
dc.titleAPPLYING MACHINE LEARNING TO AUDIT DATA: ENHANCING FRAUD DETECTION, RISK ASSESSMENT AND AUDIT EFFICIENCY
dc.typeArticle

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