Author
Boz, Z., Danış, Dilek Günneç, Birbil, S. I., Öztürk, M. K.
Publication Date
2018-11-26
Publication Place
-
Taylor & Francis
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0883-9514
Record ID
de759f2c-49be-4b56-a285-6f282a3b68b7
Library Location
Industrial Engineering
Date
2018-11-26
Sample Text
Credit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. We use a real data set from one of the prominent loan companies in Turkey. The information provided by the applicants form the variables in our analysis. The company’s experts have already labeled the clients as bad and good according to their ongoing payments. Using this labeled data set, we execute several methods to classify the bad applicants as well as the significant variables in this classification. As the data set consists of applicants who have passed the initial scoring system, most of the clients are marked as good. To deal with this imbalanced nature of the problem, we employ a set of different approaches to improve the performance of predicting the applicants who are likely to default. In the second part of this study, we aim to predict the payment behavior of clients based on their static (demographic and financial) and dynamic (payment) information. Furthermore, we analyze the effect of the length of the payment history and the staying power of the proposed prediction models.
DOI
10.1080/08839514.2018.1525517
Cilt
32
