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The lending risk predicting of the folk informal financial organization from big data using the deep learning hybrid model

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  • Shi, Tao
  • Li, Chongyang
  • Wanyan, Hong
  • Xu, Ying
  • Zhang, Wei

Abstract

This article is first to predict and earlier warning folk lending risk used deep learning hybrid model, we find that the LSTM hybrid model has a higher predict accuracy on lending risk forecasting and earlier warning of the FIFO, with an obviously improvement of the average value of forecasting accuracy. The predict accuracy of LSTM-GRU and LSTM-CNN models on lending risk forecasting of the FIFO is higher than others during COVID-19 pandemic. Therefore, we believe that the LSTM hybrid model, especially the LSTM-GRU model can better predict and early warn lending risk of the FIFO on big data.

Suggested Citation

  • Shi, Tao & Li, Chongyang & Wanyan, Hong & Xu, Ying & Zhang, Wei, 2022. "The lending risk predicting of the folk informal financial organization from big data using the deep learning hybrid model," Finance Research Letters, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004172
    DOI: 10.1016/j.frl.2022.103212
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    Cited by:

    1. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).

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