Fraud prediction in bank loan administration using decision tree

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.