The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Service risk of energy industry international trade supply chain based on artificial intelligence algorithm
Open accessAbstract
With the deepening of the process 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 supply chain enterprise financing. 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 dilemma 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, because the international trade supply chain financial service still faces the influence of risks such as corporate credit risk, bank operational risk, and supply chain enterprise information transmission risk, its function of providing financing has not been fully exerted. Early warning and control of risks existing in international trade supply chain financial services can fully play the role of international trade supply chain financial services in promoting the development of the energy industry. Therefore, this article 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 about the financial services of the international trade supply chain of the energy industry was constructed, and an experimental study on the risk early-warning model was carried out. Research showed that the risk early warning model based on 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 forecast accuracy of external environmental risks by 3.52%, and reduced bank operational risk by 6.58% and legal and regulatory risk by 7.06%.
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“However, this involves high modeling complexity due to diverse constraints and dynamics [35], overcoming data fragmentation from historically siloed operations ( [23]; linking to policy/governance, Section 3.2.3),addressing interoperability deficits [33], and navigating regulatory/market designs often ill-suited for cross-sector optimization [38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [23]
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“a lack of interoperability across heterogeneous systems hinders holistic AI-driven operations [23] and multi-energy coordination”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [23]
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Sornalakshmi, M., Balamurali, S., Venkatesulu, M. A., Krishnan, M. N., Ramasamy, L. K., Kadry, S., Manogaran, G., Hsu, C., & Muthu, B. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04862-2 · Cited by 53
RetractedMulti-source information fusion to identify water supply pipe leakage based on SVM and VMD
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Resilience optimization of multi-modal energy supply systems: Case study in German metal industry
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Modeling dependency between industry production and energy market via stochastic copula approach
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Cited by
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Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector
Park, C. (2025) · Sustainability · doi:10.3390/su17135764 · Cited by 17
Metadata
- Authors
- Liu, C., Yang, S., Hao, T., & Song, R.
- Affiliations
- Wonkwang University (KR)
- Corresponding author
- Yang, Shixin
- ORCID
- Liu, Chang
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 8
- Pages
- 13211-13219
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W4306399852
- Cited by
- 22
- References
- 6
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 5 · 2023: 3
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energy Reports (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wonkwang University
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Business, Management and Accounting › Accounting › Working Capital and Financial Performance
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Evaluation and Optimization Models
- Keywords
- energy sector, corporate credit risk, bank operational risk, creditworthiness assessment, artificial neural networks, genetic algorithm, particle swarm optimization, SME financing
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- Liu, C., Yang, S., Hao, T., & Song, R. (2022). Service risk of energy industry international trade supply chain based on artificial intelligence algorithm. Energy Reports. https://doi.org/10.1016/j.egyr.2022.09.182
