Credit card default prediction by using Heterogeneous Ensemble

Wook Lee; Sangmin Lee; Junhee Seok

doi:10.1109/ICUFN57995.2023.10199756

Credit card default prediction by using Heterogeneous Ensemble

Wook Lee, Sangmin Lee, Junhee Seok

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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 language	English
Title of host publication	ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	907-910
Number of pages	4
ISBN (Electronic)	9798350335385
DOIs	https://doi.org/10.1109/ICUFN57995.2023.10199756
Publication status	Published - 2023
Event	14th International Conference on Ubiquitous and Future Networks, ICUFN 2023 - Paris, France Duration: 2023 Jul 4 → 2023 Jul 7

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2023-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	14th International Conference on Ubiquitous and Future Networks, ICUFN 2023
Country/Territory	France
City	Paris
Period	23/7/4 → 23/7/7

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

Access to Document

10.1109/ICUFN57995.2023.10199756

Cite this

Credit card default prediction by using Heterogeneous Ensemble. / Lee, Wook; Lee, Sangmin; Seok, Junhee.
ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2023. p. 907-910 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, W, Lee, S & Seok, J 2023, Credit card default prediction by using Heterogeneous Ensemble. in ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2023-July, IEEE Computer Society, pp. 907-910, 14th International Conference on Ubiquitous and Future Networks, ICUFN 2023, Paris, France, 23/7/4. https://doi.org/10.1109/ICUFN57995.2023.10199756

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title = "Credit card default prediction by using Heterogeneous Ensemble",

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.",

keywords = "Deep Learning, Ensemble Learning, Gradient Boosting, Machine Learning, TabNet",

author = "Wook Lee and Sangmin Lee and Junhee Seok",

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year = "2023",

doi = "10.1109/ICUFN57995.2023.10199756",

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AB - 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.

KW - Deep Learning

KW - Ensemble Learning

KW - Gradient Boosting

KW - Machine Learning

KW - TabNet

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SP - 907

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BT - ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks

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ER -

Credit card default prediction by using Heterogeneous Ensemble

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this