Predication Approval for Bank Loan Using Random Forest Algorithm

Abstract

Banking Industry always needs a more accurate predictive modeling system for many issues. Predicting credit defaulters is a difficult task for the banking industry. The loan status is one of the quality indicators of the loan. It does not show everything immediately, but it is a first step of the loan lending process. The loan status is used for creating a credit-scoring model. The credit-scoring model is used for accurate analysis of credit data to find defaulters and valid customers. The objective of this is to create a credit-scoring model for credit data. Various machine-learning techniques are used to develop the financial credit-scoring model. For this classification we use the ’Random Forest Algorithm’. This proposed provides the important information with the highest accuracy. It is used to predict the loan status in commercial banks using machine-learning classifier.