000			02263nam a22002057a 4500
003			OSt
005			20220107122841.0
008			180605b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aVishnu Anilkumar (91616015) _914339
245			_aCredit scoring for Indonesian micro financing
300			_aMSC MI 2016-2018
500			_aCredit scoring was a risk assessment approach introduced in 1950s. Credit scoring began with the application of statistical methods of classification in classifying good and bad loans. Credits scoring initially focused on whether one should grant credit to a new applicant, later come to known as applicant scoring. Credit scoring has been successful because of this singular objective. Lenders such as banks and credit card companies while reviewing a client’s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now, it has been found that the problem can be optimized by using various statistical models. Credit scores are important indicators of consumer’s credit profiles, and they are used by mortgage lenders, credit card issuers, and many other financial institutions to assess consumers’ willingness and ability to repay their financial obligations. Credit scoring models are commonly built on a sample of accepted applicants whose repayment and behavior information is observable once the loan has been issued. However, in practice these models are regularly applied to new applicants, which may cause sample bias. This bias is even more pronounced in online lending, where over 90% of total loan requests are rejected. Here my client company is a financial start-up, which provides small and large loans. They have manually created a label for defaulters based on their previous payment history. So, my task was to implement a model that will categorize future applicants as defaulters and non-defaulters based on previous tagged data. Keywords: Credit Risk, ROC Curve, Imbalanced Data, Machine Learning
502			_bMSC MI _c2016-2018 _dINT _eMr. Pradeep Kumar K
650			_aCREDIT RISK _914340
650			_aROC CURVE _914341
650			_aIMBALANCED DATA _914342
650			_aMACHINE LEARNING _914343
942			_2ddc _cPR
999			_c6123 _d6123