Fraud detection in customers’ electricity consumption in Nigeria using machine learning approach

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dc.contributor.authorSulaiman Olaniyi Abdulsalam*, Micheal Olaolu Arowolo , Ronke Babatunde , Musa Isiaka , Shakirat Oluwatosin Haroon Sulyman
dc.date.accessioned2023-07-18T12:36:23Z
dc.date.available2023-07-18T12:36:23Z
dc.date.issued2021-12-06
dc.description.abstractElectricity theft is estimated to have cost Nigeria billions of Naira over the years. Electric utilities use data analytics to discover unusual consumption patterns and possible fraud in order to prevent electricity theft. This work uses data analysis to detect electricity theft, as well as a measure that uses this threat model to compare and evaluate anomaly detectors. This study employs machine learning algorithms to categorize fraud detection in customers’ electricity use, as data mining techniques has helped multiple companies and sectors better their various types of technology. Support Vector Machine (SVM) and C4.5 Decision Tree classification algorithms were used to detect fraud using consumer electricity use data. The accuracy of SVM and C4.5 was 63.4 percent and 65.9%, respectively. As a result, the Map-Reduced-ANOVA with SVM attained an accuracy of 77.5 %. Keywords: Fraud detection; Machine learning; classification; support vector machine; decision tree
dc.description.sponsorshipKWASU PRESS AND PUBLISHING
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/432
dc.language.isoen
dc.publisherKWASU PRESS AND PUBLISHING
dc.titleFraud detection in customers’ electricity consumption in Nigeria using machine learning approach
dc.typeArticle
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