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Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit

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Abstract

Some scholars have shown that the machine learning methods based on a single-source data can successfully monitor the risks of formal financial activities, but not those of informal financial activities. This is because the data generated by formal financial activities, whether it is the structured or unstructured data, are of high quality and quantity, while the data generated by informal financial activities are not. Therefore, multi-source data are the key to monitor the risks of informal financial activities through machine learning. Although a few studies attempted to use multi-source data for financial risk prediction, they simply stack the obtained multi-source data, but ignore the original sources, heterogeneity, mutual redundancy and other characteristics of the data, so that the improvement of the prediction effect is not obvious. Therefore, TSAIB_RS method based on the two-stage adaptive integration of multi-source heterogeneous data was constructed in the paper, in which the data with different sources and different distributions were adaptively integrated. In order to test the reliability of TSAIB_RS method, the paper takes the default risk of microcredit in China as the test target and compares the prediction results of various test methods. It concludes that TSAIB_RS method can significantly improve the prediction effects.

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Acknowledgements

The paper is one of mid-term results of the humanities and social science planning project funded by the ministry of education of PRC, named “Researches of the Formation Mechanism of Low Efficiency of Poverty Alleviation of Microcredit and Innovation Practice Model in Jiangsu Province” (20YJA790028), a major project of philosophy and social science research in Jiangsu universities, “Researches on the Optimization of Fintech Innovation Supervision Path (2019SJZDA060)” as well as Anhui Province Social Science Association Project (2019CX079).

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Authors and Affiliations

  1. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Anzhong Huang

  2. School of Law, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Fei Wu

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  1. Anzhong Huang
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  2. Fei Wu
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Correspondence to Fei Wu.

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Huang, A., Wu, F. Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit. Neural Comput & Applic 33, 4065–4075 (2021). https://doi.org/10.1007/s00521-020-05489-z

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  • DOI: https://doi.org/10.1007/s00521-020-05489-z

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