An In-Depth Review of Machine Learning Techniques in Loan Approval Predictions
Keywords:
Banking industry, Banking system, Classification techniques, Credit risk, Loan approving, Machine learning in bankingAbstract
As more loan applications are presented daily, the world of technology has significantly impacted the banking system. In appraising such applications, the banks are expected to employ some standards so that they select the best applicants to approve them. However, the manual verification and suggestion process is lengthy and may potentially be hazardous. In modern world of advanced technology machine learning has proved to be a game changer, with the learning algorithms having the capability to operate and manage various applications on the increase. To predict the outcomes of loan applications, several machine learning algorithms are generated and tested, especially classification algorithms. This review paper looks at the combination of machine learning techniques and loan acceptance prediction in the banking sector. Based on historical data of the bank customers, a predictive model is developed based on both supervised and unsupervised machine learning processes. It also provides in detail an analysis of the dangers involved in the banking industry, the various categories of classification that are practiced, and loan qualifying terms. This comprehensive analysis enhances our understanding of the interrelationship that exists between machine learning and the process of banking lending.
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