Open AccessA Naive Bayes approach to fraud prediction in loan default
Author(s) -
Ibukun Eweoya,
Ayodele Ariyo Adebiyi,
A. A. Azeta,
Felix Chidozie,
Frank Agono,
Blessing Guembe
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1299/1/012038
Subject(s) - loan , default , naive bayes classifier , probability of default , actuarial science , profit (economics) , bayes' theorem , credit risk , business , work (physics) , machine learning , computer science , artificial intelligence , finance , economics , support vector machine , bayesian probability , engineering , microeconomics , mechanical engineering
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.