Credit card default prediction by using Heterogeneous Ensemble

Wook Lee, Sangmin Lee, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Credit card companies calculate an accurate credit score by utilizing the personal information and credit data of new applicants. To analyze and predict credit ratings, there have been many studies using machine learning. However, previous research had limitations in improving prediction accuracy using single algorithms such as ensembles or deep learning and could not consider the problem of multiple histories of the same customer using different cards. This study proposes a hybrid algorithm that combines heterogeneous ensembles and TabNet, a deep learning algorithm specialized in tabular data, to address these issues. The study conducted comparative experiments with several state-of-the-art machine learning algorithms that have been used for credit card delinquency prediction.

Original languageEnglish
Title of host publicationICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages907-910
Number of pages4
ISBN (Electronic)9798350335385
DOIs
Publication statusPublished - 2023
Event14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 - Paris, France
Duration: 2023 Jul 42023 Jul 7

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2023-July
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference14th International Conference on Ubiquitous and Future Networks, ICUFN 2023
Country/TerritoryFrance
CityParis
Period23/7/423/7/7

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Deep Learning
  • Ensemble Learning
  • Gradient Boosting
  • Machine Learning
  • TabNet

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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