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Improving the Efficiency of Customer's Credit Rating with Machine Learning in Big Data Cloud Computing

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Abstract

Today, the use of large networks under the cloud and big data has become very common, as well as learning methods and artificial intelligence have been designed for them. One of the main issues in the banking and Big Data industry is granting bank facilities to people. The main challenge in evaluating the customers’ credit in the banking industry is that, due to the large volume of the data, it is impossible to review them manually, as we have to use computer algorithms for this purpose. Hence, the accuracy of the algorithm used for this purpose is essential and influential. In this paper, several predictive algorithms are employed on the problem, so that the result would be a prediction based on the combination of the results of each of them. To integrate the results of the algorithms, the weight to each of the algorithms is allocated, which presents the amount of importance of the algorithm in the result. The Ordered weighting averaging is used in this research. By applying these techniques, the proposed algorithms are described in this paper to achieve better accuracy than the existing ones.

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Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, 510006, China

    Amir Javadpour & Guojun Wang

  2. School Financial Engineering, Faculty of Financial Science, Kharazmi University, 1477893855, Tehran, Iran

    Khatereh Saedifar

  3. Department of Computer Science and Information Engineering, Providence University, Taichung City, Taiwan

    Kuan-Ching Li

  4. Faculty of Management, University of Tehran, Tehran, Iran

    Fatemeh Saghafi

Authors
  1. Amir Javadpour
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  2. Khatereh Saedifar
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  3. Guojun Wang
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  4. Kuan-Ching Li
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  5. Fatemeh Saghafi
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Correspondence to Amir Javadpour.

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Javadpour, A., Saedifar, K., Wang, G. et al. Improving the Efficiency of Customer's Credit Rating with Machine Learning in Big Data Cloud Computing. Wireless Pers Commun 121, 2699–2718 (2021). https://doi.org/10.1007/s11277-021-08844-y

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