Fraud prediction in loan default using support vector machine
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Conf. Series: Journal of Physics: Conf. Series
Abstract
The concept of taking loan has been in existence since inception of the
human race but it is now taking diverse dimensions. This spans through personal
exchange of loans for repayment based on personal track records, enjoying loans as
proceeds of daily contribution without collaterals, except for the banking sector that
requests collaterals for official loans. The uniform occurrence of being unable to pay
the debts and resulting in a default is evident to the level of bank closures and nations’
bankruptcy is experienced across board. With a large volume and variety of data,
credit history judgment by man is inefficient; case-based, analogy-based reasoning
and statistical approaches have been employed but the 21st century fraudulent attempts
cannot be discovered by these approaches, hence; the machine learning approach
using the support vector machine. This work employs a supervised learning approach
based on machine learning to predict the possibility of a fraud in a loan application
through hidden trends in data instead of giving loans which ordinarily should not be
approved; past occurrences discovered through machine learning reveals risky loans
and a possible fraud by humans in approvals that can result in a default. Machine
learning approaches are able to detect fraudulent financial statements to avert business
comatose
Description
Staff Publication
Keywords
Confusion matrix,, fraud,, machine learning,, loan default,, support vector machine