A Naive Bayes approach to fraud prediction in loan default

Autor:	B Guembe, Ambrose A. Azeta, Ibukun Eweoya, Ayodele Ariyo Adebiyi, Felix Chidozie, F O Agono
Rok vydání:	2019
Předmět:	History Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Actuarial science Credit default swap Profit (accounting) Work (electrical) Loan Default Business Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1299:012038
ISSN:	1742-6596 1742-6588
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f3968585a891975a089f294ba1cbb017 https://doi.org/10.1088/1742-6596/1299/1/012038 Zobrazit plný text záznamu