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A multi-scale convolutional neural network-based model for clustering economic risk detection.

Yi Zhao1

  • 1School of Management, Wuhan University of Bioengineering, Wuhan, China.

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Summary
This summary is machine-generated.

This study introduces a novel multi-scale convolutional neural network (MCNN) model for early detection of economic risks following public health events. The MCNN model accurately predicts financial risk anomalies, providing crucial time for emergency response.

Keywords:
Aggregation anomaly predictionEconomic riskMCNNPSO-ELMRisk detection

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Area of Science:

  • Financial Risk Analysis
  • Public Health Economics
  • Artificial Intelligence in Finance

Background:

  • The COVID-19 pandemic highlighted the need for better prediction of economic hazards linked to public health crises.
  • Existing methods for detecting financial risk anomalies are limited and often detect issues only after aggregation.

Purpose of the Study:

  • To develop an advanced model for early detection and prediction of aggregated economic risks.
  • To improve the timeliness and accuracy of financial risk anomaly detection.

Main Methods:

  • A multi-scale convolutional neural network (MCNN) was developed for financial risk anomaly detection.
  • Economic risk statistics, precursor coordinates, distribution entropy, distance, potential energy, and density were extracted.
  • A particle swarm optimization-based extreme learning machine (PSO-ELM) was used for prediction modeling.

Main Results:

  • The MCNN model demonstrated high timeliness in detecting abnormal aggregated economic risks.
  • Achieved a forecast accuracy of 97.68%, outperforming existing algorithms.
  • The model provides additional time for implementing emergency actions.

Conclusions:

  • The proposed MCNN model offers an efficient and accurate solution for early warning systems against economic risks.
  • This approach significantly enhances the ability to manage financial repercussions of public health events.
  • The findings contribute to more resilient economic planning in the face of global health crises.