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BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Author

Listed:
  • Fan Yang

    (Xi’an Jiaotong University)

  • Yanan Qiao

    (Xi’an Jiaotong University)

  • Yong Qi

    (Xi’an Jiaotong University)

  • Junge Bo

    (Xi’an Jiaotong University)

  • Xiao Wang

    (Xi’an Jiaotong University)

Abstract

Credit evaluation is of high scientific significance and practical use, especially in today’s plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

Suggested Citation

  • Fan Yang & Yanan Qiao & Yong Qi & Junge Bo & Xiao Wang, 2025. "BACS: blockchain and AutoML-based technology for efficient credit scoring classification," Annals of Operations Research, Springer, vol. 345(2), pages 703-723, February.
  • Handle: RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-022-04531-8
    DOI: 10.1007/s10479-022-04531-8
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    References listed on IDEAS

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    1. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    2. Michael Doumpos & Constantin Zopounidis, 2007. "Model combination for credit risk assessment: A stacked generalization approach," Annals of Operations Research, Springer, vol. 151(1), pages 289-306, April.
    3. Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021. "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, vol. 306(1), pages 27-56, November.
    4. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LLC, vol. 20(1), pages 3-29, March.
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