A Naive Bayes approach to fraud prediction in loan default
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Conf. Series: Journal of Physics: Conf. Series
Abstract
The 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.
Description
Staff Publication
Keywords
Confusion matrix,, fraud,, machine learning,, loan default,, Naïve Bayes