A Naive Bayes approach to fraud prediction in loan default

被引：1

作者：

Eweoya, I. O. ^{[1
]}

Adebiyi, A. A. ^{[1
,2
]}

Azeta, A. A. ^{[1
]}

Chidozie, F. ^{[1
]}

Agono, F. O. ^{[1
]}

Guembe, B. ^{[1
]}

机构：

[1] Covenant Univ, Dept Comp & Informat Sci, Ota, Nigeria

[2] Landmark Univ, Dept Comp Sci, Omu Aran, Nigeria

来源：

3RD INTERNATIONAL CONFERENCE ON SCIENCE AND SUSTAINABLE DEVELOPMENT (ICSSD 2019): SCIENCE, TECHNOLOGY AND RESEARCH: KEYS TO SUSTAINABLE DEVELOPMENT | 2019年 / 1299卷

关键词：

Confusion matrix; fraud; machine learning; loan default; Naive Bayes;

D O I：

10.1088/1742-6596/1299/1/012038

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 Naive 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.

引用

页数：4

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