Predicting Defaulter in Loan Approval using Machine Learning

In wide range of improving banking sector in recent times and the increasing trend of taking loans is one of a large population applies for bank loans. But the major problem all banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The analysis of risks and assessment of default becomes crucial thereafter. Banks hold huge volumes of customer behavior related data from which they are unable to arrive at a judgement if an applicant can be defaulter or not. In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Decision Trees (ii) Ensemble Boosting