Fraud prediction in bank loan administration using decision tree
Loading...
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing: 3rd International Conference on Science and Sustainable Development (ICSSD 2019)
Abstract
The rate at which banks looses funds to loan beneficiaries due to loan default is
alarming. This trend has led to the closure of many banks, potential beneficiaries deprived of
access to loan; and many workers losing their jobs in the banks and other sectors. This work
uses past loan records based on the employment of machine learning to predict fraud in bank
loan administration and subsequently avoid loan default that manual scrutiny by a credit officer
would not have discovered. However, such hidden patterns are revealed by machine learning.
Statistical and conventional approaches in this direction are restricted in their accuracy
capabilities. With a large volume and variety of data, credit history judgement 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 decision tree method to predict fraud and it
delivers an accuracy of 75.9 percent.
Description
Staff Publication
Keywords
Confusion matrix,, decision tree,, fraud,, machine learning,, prediction.