Using Hybrid Approaches for Credit Application Scoring

No Thumbnail Available

Date

2023

Authors

Mirza,F.K.
Ogrenci,A.S.

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Journal Issue

Abstract

Using various methods of computational intelligence, scores for credit applications are predicted for making a decision to accept or to reject. Past data about credits accepted in a financial institution are used to develop hybrid models implementing gradient boosting and attention supported neural networks. The performance (Gini scores above 0.7), limitations and further research directions are discussed. © 2023 IEEE.

Description

Keywords

attention mechanism, credit scoring, gradient boosting, neural networks, unbalanced data

Turkish CoHE Thesis Center URL

Fields of Science

Citation

0

WoS Q

Scopus Q

Source

IEEE 23rd International Symposium on Computational Intelligence and Informatics, CINTI 2023 - Proceedings -- 23rd IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2023 -- 20 November 2023 through 22 November 2023 -- Budapest -- 196335

Volume

Issue

Start Page

111

End Page

116