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Linguistic Data Analysis Using Nagel Point-Based Ranking Fuzzy Numbers for Financial Risk Management

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Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Studies of financial management show the importance of various types of financial risks as these risks will influence the financial outlook of banks. Nonetheless, the true risks leading to financial crises are indecisive. This paper aims to suggest the rank of the selected financial risks that contributed to financial crises in the banking sector using a method of ranking fuzzy numbers (RFN). The linguistic data given in triangular fuzzy numbers are analyzed using the method of RFN based on Nagel point to determine the highest risk. Five experts were requested to offer qualitative linguistic evaluation about the financial risks. The proposed method of Nagel point, which considers Cartesian coordinates of Nagel point and function N of triangular fuzzy numbers, is implemented in financial risk management. The transformation function N suggests that ‘credit risk’ is the highest risk in financial management. It is important for banks to address the credit risk and in return, it would avoid financial crises.

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Author information

Authors and Affiliations

  1. Management Science Research Group, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, 20130, Kuala Nerus, Malaysia

    Lazim Abdullah & Ahmad Termimi Ab Ghani

  2. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, 21030, Kuala Nerus, Malaysia

    Nurnadiah Zamri

Authors
  1. Lazim Abdullah
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  2. Ahmad Termimi Ab Ghani
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  3. Nurnadiah Zamri
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Corresponding author

Correspondence to Lazim Abdullah .

Editor information

Editors and Affiliations

  1. Poornima College of Engineering, Jaipur, India

    Garima Mathur

  2. Poornima College of Engineering, Jaipur, India

    Mahesh Bundele

  3. Rajasthan Technical University, Kota, Rajasthan, India

    Mahendra Lalwani

  4. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Abdullah, L., Ab Ghani, A.T., Zamri, N. (2022). Linguistic Data Analysis Using Nagel Point-Based Ranking Fuzzy Numbers for Financial Risk Management. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_27

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