Dynamic Effectiveness of Random Forest Algorithm in Financial Credit Risk Management for Improving Output Accuracy and Loan Classification Prediction

Abstract

With technology impacting several sectors, it can be imagined that the financial sector has a lot to benefit from the increasing level of technological innovations. These institutions take from the surplus of the economy and lend to the deficit sectors of the economy. Individuals and organizations obtain credit facilities from financial institutions to meet basic needs and boost their businesses. However, the stability of the economy is better guaranteed when borrowers pay back the loans availed to them rather than default. This study aims to identify the effectiveness of Random Forest in credit scoring using 32,581 observations. The study proved that Random Forest provides better output accuracy of 91% based on Gini Index for variable selection according to the level of importance when compared to Decision Tree with an output of 83%. It offers better credit scoring accuracy and credit rating as a result of its classification power. The objective of the study is to point out the random forest predictive strength using an unprocessed German credit dataset from Kaggle and to provide an explainable framework sufficient for Financial Institutions and banks to make decisions when granting loans to existing and new applicants.