Entrepreneurial borrowing overdue prediction based on stacking model transfer learning
ISSN: 0025-1747
Article publication date: 20 July 2023
Issue publication date: 29 August 2024
Abstract
Purpose
By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.
Design/methodology/approach
The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.
Findings
The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.
Practical implications
From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.
Originality/value
This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.
Keywords
Acknowledgements
This work was supported by Guangxi Society of Finance 2023 Key Project “Research on Differentiated Development Strategies of Guangxi Local City Commercial Banks in County Areas under the Background of Rural Revitalization”.
The first two authors contribute equally.
Expression of concern: The publisher of Management Decision is issuing an Expression of Concern for the following article “Shengdong, M., Yunjie, L. and Jijian, G. (2023), “Entrepreneurial borrowing overdue prediction based on stacking model transfer learning”, Management Decision, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MD-10-2022-1462.” which was submitted to the guest-edited special issue ‘Navigating the role of circular economy in entrepreneurship: Opportunities and challenges’. An investigation by the publisher found a number of articles with multiple concerns, including but not limited to compromised editorial handling and reviewing, undisclosed conflicts of interest, and lack of suitability for the scope of the journal. As a result of these concerns and as trust in the content is central to the integrity of the publication process, the Editor-in-Chief and publisher have taken the decision to publish an Expression of Concern for all articles within this special issue. The journal has not been able to confirm whether the authors were aware of these concerns. An investigation is ongoing and is currently unresolved. Further information will be provided by Management Decision as it becomes available.
Citation
Shengdong, M., Yunjie, L. and Jijian, G. (2024), "Entrepreneurial borrowing overdue prediction based on stacking model transfer learning", Management Decision, Vol. 62 No. 8, pp. 2599-2620. https://doi.org/10.1108/MD-10-2022-1462
Publisher
:Emerald Publishing Limited
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