{"title":"Prediction of credit risks in lending bank loans using machine learning","authors":"Mohit Lakhani, Bhavesh Dhotre, Saurabh Giri","doi":"10.5958/2319-1422.2019.00003.1","DOIUrl":null,"url":null,"abstract":"1,2,3Student, Dept. of IT Engineering, NMIMS College, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Looking at the current scenario there are huge risks involved for Banks to provide Loans. So as to reduce their capital loss; banks should assess and analyse credibility of the individual before sanctioning loan. In the absence of this process there are many chances that this loan may turn in to bad loan in near future. Due to the advanced technology associated with Data mining, data availability and computing power, most banks are renewing their business models and switching to Machine Learning methodology. Credit risk prediction is key to decision-making and transparency. In this review paper, we have discussed classifiers based on Machine and deep learning models on real data in predicting loan default probability. The most important features from various models are selected and then used in the modelling process to test the stability of binary classifiers by comparing their performance on separate data.","PeriodicalId":436614,"journal":{"name":"SAARJ Journal on Banking & Insurance Research","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAARJ Journal on Banking & Insurance Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5958/2319-1422.2019.00003.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
1,2,3Student, Dept. of IT Engineering, NMIMS College, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Looking at the current scenario there are huge risks involved for Banks to provide Loans. So as to reduce their capital loss; banks should assess and analyse credibility of the individual before sanctioning loan. In the absence of this process there are many chances that this loan may turn in to bad loan in near future. Due to the advanced technology associated with Data mining, data availability and computing power, most banks are renewing their business models and switching to Machine Learning methodology. Credit risk prediction is key to decision-making and transparency. In this review paper, we have discussed classifiers based on Machine and deep learning models on real data in predicting loan default probability. The most important features from various models are selected and then used in the modelling process to test the stability of binary classifiers by comparing their performance on separate data.