CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS

Damla Ilter; Ozan Kocadaglı

CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS

Abstract

The credit scoring is one of the major activities in the banking sector. Because of growing market and increasing the loan applications, this field still continues its concern in terms of rating the applicants and assessing the credit amounts. To reduce the number of wrong decisions in the credit evaluation process, the decision makers focus on estimating more robust models. However, the traditional methods are criticized due to various pre-requisites and linear approximations in the high dimensional and excessive nonlinear cases. For this reason, artificial intelligence techniques are mostly preferred to handle the credit scoring problems accurately. This study presents an efficient procedure that is based on ANNs with cross-entropy and fuzzy relations in the context of the credit scoring. In the implementations, the proposed procedure is applied to a couple of benchmark credit scoring data sets and its performance is compared with traditional approaches.

Keywords

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Damla Ilter This is me
0000-0002-9844-4616
Türkiye

Ozan Kocadaglı This is me
Türkiye

Publication Date

September 1, 2020

Submission Date

November 22, 2018

Acceptance Date

March 28, 2019

Published in Issue

Year 2019 Volume: 37 Number: 3

IZ

https://izlik.org/JA39FG58JY

Cite

RIS / Bibtex

APA

Ilter, D., & Kocadaglı, O. (2020). CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. Sigma Journal of Engineering and Natural Sciences, 37(3), 855-870. https://izlik.org/JA39FG58JY

AMA

1.Ilter D, Kocadaglı O. CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. SIGMA. 2020;37(3):855-870. https://izlik.org/JA39FG58JY

Chicago

Ilter, Damla, and Ozan Kocadaglı. 2020. “CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS”. Sigma Journal of Engineering and Natural Sciences 37 (3): 855-70. https://izlik.org/JA39FG58JY.

EndNote

Ilter D, Kocadaglı O (September 1, 2020) CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. Sigma Journal of Engineering and Natural Sciences 37 3 855–870.

IEEE

[1]D. Ilter and O. Kocadaglı, “CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS”, SIGMA, vol. 37, no. 3, pp. 855–870, Sept. 2020, [Online]. Available: https://izlik.org/JA39FG58JY

ISNAD

Ilter, Damla - Kocadaglı, Ozan. “CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS”. Sigma Journal of Engineering and Natural Sciences 37/3 (September 1, 2020): 855-870. https://izlik.org/JA39FG58JY.

JAMA

1.Ilter D, Kocadaglı O. CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. SIGMA. 2020;37:855–870.

MLA

Ilter, Damla, and Ozan Kocadaglı. “CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS”. Sigma Journal of Engineering and Natural Sciences, vol. 37, no. 3, Sept. 2020, pp. 855-70, https://izlik.org/JA39FG58JY.

Vancouver

1.Damla Ilter, Ozan Kocadaglı. CREDIT SCORING BY ARTIFICIAL NEURAL NETWORKS BASED CROSS-ENTROPY AND FUZZY RELATIONS. SIGMA [Internet]. 2020 Sep. 1;37(3):855-70. Available from: https://izlik.org/JA39FG58JY