Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 197-205.doi: 10.11896/jsjkx.210900195

• Artificial Intelligence • Previous Articles     Next Articles

Prediction Model of Enterprise Resilience Based on Bi-directional Long Short-term Memory Network

SONG Mei-qi1,2,3, FU Xiang-ling1,2,3, YAN Chen-wei1,2,3, WU Wei-qiang3, REN Yun3   

  1. 1 School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications,BUPT),Ministry of Education,Beijing 100876,China
    3 Smart Bank Joint Laboratory of Beijing University of Posts and Telecommunications,BUPT and Bohai Bank,Tianjin 300204,China
  • Received:2021-09-23 Revised:2022-04-21 Online:2022-11-15 Published:2022-11-03
  • About author:SONG Mei-qi,born in 1998,master.Her main research interests include AI+Finance,deep learning and natural language processing.
    FU Xiang-ling,born in 1975,Ph.D,associate professor.Her main research interests include AI+Finance,deep lear-ning and text mining.
  • Supported by:
    National Natural Science Foundation of China(72274022).

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Abstract: Traditional risk management methods focus on identifying,predicting and assessing potential risks.However,when enterprises are exposed to uncertainty and unexpected risks,traditional methods cannot deal with those risks.Therefore,the academia gradually shifts the perspective of risk management from predicting and avoiding risks to improving the ability of enterprises to withstand and recover from risks,that is,the enterprise resilience.This paper proposes a prediction method to predict the enterprise resilience based on temporal features,which utilizes Bi-LSTM to encode the temporal features to obtain the feature representation of every enterprise,and the classification results of enterprise resilience are obtained by a softmax classifier.The proposed method is validated on the real-world datasets from listed companies in China,and the macro-F1 value reaches 89.0%,which is improved compared with those models without considering temporal features,such as RF,XGBoost and LightGBM.This paper further discusses the importance of various factors that have an influence on the enterprise resilience.In this paper,the machine learning methods are applied to the evaluation and prediction of enterprise resilience for the first time,which provides theoretical and methodological guidance for enterprises to deal with unexpected risks.

Key words: Enterprise resilience, Temporal features, Risk management, Bi-directional long short-term memory

CLC Number: 

  • TP183
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