Credit scoring for Indonesian micro financing

Credit 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

MSC MI 2016-2018 INT Mr. Pradeep Kumar K