research-article

Credit Risk Models using Rule-Based Methods and Machine-Learning Algorithms

Authors:

Bingling ChenAuthors Info & Claims

CSAI '22: Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

Pages 203 - 209

https://doi.org/10.1145/3577530.3577588

Published: 30 March 2023 Publication History

Abstract

This study applies machine-learning techniques and rule-based methods to construct nonlinear nonparametric models to forecast retail consumer and medium-sized enterprises (SMEs) credit risk. By combining customer transactions and enterprise data from 2018 to 2020 sampled from a major business district in the People’s Republic of China, forecasts were constructed that significantly improved the classification rates of customer and enterprise delinquencies and defaults. Moreover, the time-series patterns of the estimated delinquency rates and credit scores over multiple dimensions produced by this model suggest that aggregated credit risk analytics may have important applications in forecasting systemic risk, which might shed some light on obtaining prospective insights regarding consumer credit that can be gleaned from historical data especially pandemic period.

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Cited By

Tian MZhao FHuang BMing YZhang TLi H(2024)UUTD-SVM: A Framework for Understanding Unstructured Tax Documents Utilizing RPA and SVM Methodology2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692771(169-174)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692771
Zhou CWang WTian MLiang JZhang DLi H(2024)The Application of Big Data and AI Analytics in Rating Industrial and Smart Manufacturing Districts: Challenges and Opportunities2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00020(70-78)Online publication date: 12-Jan-2024
https://doi.org/10.1109/BDICN62775.2024.00020
Qayyum MAhmad ETahir AAcharya S(2023)Modeling and Analysis of the Fuzzy-Fractional Chaotic Financial System Using the Extended He–Mohand Algorithm in a Fuzzy-Caputo SenseInternational Journal of Intelligent Systems10.1155/2023/30288242023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3028824
Show More Cited By

Index Terms

Credit Risk Models using Rule-Based Methods and Machine-Learning Algorithms
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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CSAI '22: Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

December 2022

341 pages

ISBN:9781450397773

DOI:10.1145/3577530

Copyright © 2022 ACM.

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Published: 30 March 2023

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CSAI 2022

CSAI 2022: 2022 6th International Conference on Computer Science and Artificial Intelligence

December 9 - 11, 2022

Beijing, China

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Cited By

Tian MZhao FHuang BMing YZhang TLi H(2024)UUTD-SVM: A Framework for Understanding Unstructured Tax Documents Utilizing RPA and SVM Methodology2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692771(169-174)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692771
Zhou CWang WTian MLiang JZhang DLi H(2024)The Application of Big Data and AI Analytics in Rating Industrial and Smart Manufacturing Districts: Challenges and Opportunities2024 3rd International Conference on Big Data, Information and Computer Network (BDICN)10.1109/BDICN62775.2024.00020(70-78)Online publication date: 12-Jan-2024
https://doi.org/10.1109/BDICN62775.2024.00020
Qayyum MAhmad ETahir AAcharya S(2023)Modeling and Analysis of the Fuzzy-Fractional Chaotic Financial System Using the Extended He–Mohand Algorithm in a Fuzzy-Caputo SenseInternational Journal of Intelligent Systems10.1155/2023/30288242023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/3028824
Tian MLiang JZhang DZhang XWang ZLi H(2023)Detection of Financial Fraudulent Activities with Machine Learning:A Case Study of Detecting Potential Tax and Invoice FraudProceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence10.1145/3638584.3638669(33-39)Online publication date: 8-Dec-2023
https://dl.acm.org/doi/10.1145/3638584.3638669

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