Credit scoring for Indonesian micro financing (Record no. 6123)

MARC details
000 -LEADER
fixed length control field 02263nam a22002057a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122841.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180605b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Vishnu Anilkumar (91616015)
9 (RLIN) 14339
245 ## - TITLE STATEMENT
Title Credit scoring for Indonesian micro financing
300 ## - PHYSICAL DESCRIPTION
Extent MSC MI 2016-2018
500 ## - GENERAL NOTE
General note Credit scoring was a risk assessment approach introduced in 1950s. Credit scoring<br/>began with the application of statistical methods of classification in classifying good and bad<br/>loans. Credits scoring initially focused on whether one should grant credit to a new applicant,<br/>later come to known as applicant scoring. Credit scoring has been successful because of this<br/>singular objective. <br/>Lenders such as banks and credit card companies while reviewing a client’s<br/>request for loan use credit scores. Credit scores help measure the creditworthiness of the client<br/>using a numerical score. Now, it has been found that the problem can be optimized by using<br/>various statistical models. Credit scores are important indicators of consumer’s credit profiles,<br/>and they are used by mortgage lenders, credit card issuers, and many other financial institutions<br/>to assess consumers’ willingness and ability to repay their financial obligations. Credit scoring<br/>models are commonly built on a sample of accepted applicants whose repayment and behavior<br/>information is observable once the loan has been issued. However, in practice these models are<br/>regularly applied to new applicants, which may cause sample bias. This bias is even more<br/>pronounced in online lending, where over 90% of total loan requests are rejected. Here my client<br/>company is a financial start-up, which provides small and large loans. They have manually<br/>created a label for defaulters based on their previous payment history. So, my task was to<br/>implement a model that will categorize future applicants as defaulters and non-defaulters based<br/>on previous tagged data. <br/>Keywords: Credit Risk, ROC Curve, Imbalanced Data, Machine Learning
502 ## - DISSERTATION NOTE
Degree type MSC MI
Name of granting institution 2016-2018
Year degree granted INT
-- Mr. Pradeep Kumar K
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element CREDIT RISK
9 (RLIN) 14340
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ROC CURVE
9 (RLIN) 14341
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element IMBALANCED DATA
9 (RLIN) 14342
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 14343
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   05/06/2018   R-1417 05/06/2018 05/06/2018 Project Reports