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

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

Finance Research Letters, 2022, vol. 50, issue C

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.

Keywords: Folk lending risk forecasting; LSTM; Hybrid model; COVID-19 pandemic (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004172

DOI: 10.1016/j.frl.2022.103212

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