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International Conference on Information and Communication Technology and Applications

ICTA 2020: Information and Communication Technology and Applications pp 158–169Cite as

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

Abstract

Financial credit risk analysis management has been a foremost influence with a lot of challenges, especially for banks reducing their principal loss. In this study, the machine learning technique is a promising area used for credit scoring for analyzing risks in banks. It has become critical to extract beneficial knowledge for a great number of complex datasets. In this study, a machine learning approach using Chi-Square with SVM-RBF classifier was analyzed for Taiwan bank credit data. The model provides important information with enhanced accuracy that will help in predicting loan status. The experiment achieves 93% accuracy compared to the state-of-the-art.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Kwara State University, Malete, Nigeria

    Kayode Omotosho Alabi & Sulaiman Olaniyi Abdulsalam

  2. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Roseline Oluwaseun Ogundokun & Micheal Olaolu Arowolo

Authors
  1. Kayode Omotosho Alabi
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  2. Sulaiman Olaniyi Abdulsalam
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  3. Roseline Oluwaseun Ogundokun
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  4. Micheal Olaolu Arowolo
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Corresponding author

Correspondence to Roseline Oluwaseun Ogundokun .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Alabi, K.O., Abdulsalam, S.O., Ogundokun, R.O., Arowolo, M.O. (2021). Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69142-4

  • Online ISBN: 978-3-030-69143-1

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