Reassessment and monitoring of loan applications with machine learning

Name: Reassessment and monitoring of loan applications with machine learning
Author: Boz, Z., Danış, Dilek Günneç, Birbil, S. I., Öztürk, M. K.

İsim	Reassessment and monitoring of loan applications with machine learning
Yazar	Boz, Z., Danış, Dilek Günneç, Birbil, S. I., Öztürk, M. K.
Basım Tarihi:	2018-11-26
Basım Yeri	- Taylor & Francis
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0883-9514
Kayıt Numarası	de759f2c-49be-4b56-a285-6f282a3b68b7
Lokasyon	Industrial Engineering
Tarih	2018-11-26
Örnek Metin	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

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Reassessment and monitoring of loan applications with machine learning

Yazar Boz, Z., Danış, Dilek Günneç, Birbil, S. I., Öztürk, M. K.

Basım Tarihi 2018-11-26

Basım Yeri - Taylor & Francis

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0883-9514

Kayıt Numarası de759f2c-49be-4b56-a285-6f282a3b68b7

Lokasyon Industrial Engineering

Tarih 2018-11-26

Örnek Metin 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