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dc.contributor.authorEweoya, Ibukun-
dc.contributor.authorAdebiyi, A A-
dc.contributor.authorAzeta, A A-
dc.contributor.authorChidozie, F-
dc.contributor.authorAgono, F O-
dc.contributor.authorGuembe, B-
dc.descriptionStaff Publicationen_US
dc.description.abstractThe essence of granting loans to individuals and corporate beneficiaries is to boost the economy while the lenders make profit from the interest that accrues to the lending. However, due to non-compliance to basic rules, fraud is prevalent in credit administration and traditional methods of detecting fraud have failed. Furthermore, they are time-consuming and less accurate. This work uses a supervised machine learning approach, specifically the Naïve Bayes to predict fraudulent practices in loan administration based on training and testing of labeled dataset. Previous works either predict credit worthiness or detect loan fraud but not predicting fraud in credit default. The approach employed in this work yielded 78 % accuracy.en_US
dc.publisherIOP Conf. Series: Journal of Physics: Conf. Seriesen_US
dc.subjectConfusion matrix,en_US
dc.subjectmachine learning,en_US
dc.subjectloan default,en_US
dc.subjectNaïve Bayesen_US
dc.titleA Naive Bayes approach to fraud prediction in loan defaulten_US
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