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Bank Credit Risk Management Based on Weighted k-NN Method with Information Entropy

In: Economic Management and Big Data Application Proceedings of the 3rd International Conference

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  • Zhonglong Wen

Abstract

Since the global financial crisis of 2008, it has been common sense that it is significant to identify and evaluate the risk to survive in the banking industry. The goal is to achieve a more accurate and efficient method of managing bank credit risk. Weighted k-nearest neighbor (k-NN) with information entropy is proposed, and real data is utilized to conduct comparison experiments with other different models and algorithms. As the result shows, weighted k-NN with information entropy is the best tool in contrast with other models. The method of weight k-NN with entropy could be applied in reality.

Suggested Citation

  • Zhonglong Wen, 2024. "Bank Credit Risk Management Based on Weighted k-NN Method with Information Entropy," World Scientific Book Chapters, in: Sikandar Ali Qalati (ed.), Economic Management and Big Data Application Proceedings of the 3rd International Conference, chapter 97, pages 1077-1086, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811270277_0097
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    Keywords

    Big Data; Information Management; Economic; Data Applications; Blockchain; E-commerce;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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