Journal of Intelligent Strategic Management

Journal of Intelligent Strategic Management

Strategic Risk Management of the Energy Industry Based on Artificial Intelligence Algorithm

Document Type : Original Article

Author
PhD Student in Business Administration, University of Tehran, Tehran, Iran
Abstract
With the deepening of the trend of global economic integration, international trade supply chain financial services have also flourished. International trade supply chain financial services have played an important role in solving the enterprise financing of the supply chain. As far as the energy industry is concerned, international trade supply chain financial services can provide sufficient credit support for energy companies. This solves the financing problem of small and medium-sized energy companies in import and export trade, and can also improve the capital turnover rate of large energy companies. However, since international trade supply chain financial services are still affected by risks such as enterprise credit risk, bank operational risk, and supply chain company information transmission risk, its function in financing has not been fully implemented. Early warning and control of risks in international trade supply chain financial services can play the role of international trade supply chain financial services in promoting the development of the energy industry. Therefore, this paper used three artificial intelligence (AI) algorithms including artificial neural network, genetic algorithm and particle swarm algorithm to analyze the risk of financial services in the international trade supply chain of the energy industry. A risk early warning model of the financial services in the international trade supply chain of the energy industry was built, and an empirical study on the risk early warning model was conducted. The research showed that the risk early warning model based on the AI ​​algorithm enabled banks to improve the accuracy of corporate credit assessment by 7.43% and the accuracy of information collection by 5.61%. It improved the prediction accuracy of external environmental risks by 3.52%, and reduced the bank's operational risk by 6.58% and legal and regulatory risk by 7.06%.
Keywords

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