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%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
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
Wang, Z.-F., He, X., Shen, H., Fan, S., & Zeng, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102819 · Cited by 66
Sankar, S., Ramasubbareddy, S., Parvathala, B. R., et al. (2021) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2021.100645 · Cited by 34
Resilience optimization of multi-modal energy supply systems: Case study in German metal industry
Hoettecke, L., Thiem, S., Schäfer, J., et al. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107824 · Cited by 9
Leukel, J., & Sugumaran, V. (2021) · Journal of Enterprise Information Management · doi:10.1108/jeim-11-2020-0478 · Cited by 4
Modeling dependency between industry production and energy market via stochastic copula approach
Yıldırım, E., & Cengiz, M. A. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1691228 · Cited by 3
Research works printed but not linked: 14
- Bao, 2018, Design of reactive power optimization analysis software based on artificial intelligence algorithm, Power Syst. Protect. Control, 46, 89
- Chen, 2018, Risks and countermeasures of financial services in international trade supply chain, Mod. Bus., 42, 89
- Chen, 2022, Analysis on the financing methods and countermeasures of international trade supply chain finance, Bus. Exhib. Econ., 37, 45
- Gao, 2021, Predictive value of artificial intelligence algorithm model in the diagnosis of lower extremity venous thromboembolism in trauma patients, Chin. J. Trauma, 37, 932
- Li, 2020, Risk assessment of wireless mobile communication system based on artificial intelligence algorithm, Mod. Electron. Technol., 43, 12
- Peng, 2018, Comparison of database technology curriculum automatic examination of multiple artificial intelligence algorithms, Comput. Syst. Appl., 27, 210
- Song, 2020, Analysis of financing and supply chain financial services in comprehensive foreign trade services, Int. Publ. Relat., 25, 290
- Wang, 2017, Risks and prevention of commercial bank supply chain finance, Financ. Theory Pract., 16, 1
- Wei, 2019, Risk analysis and countermeasures of international trade supply chain financial services, Consum. Guide, 38, 230
- Yang, 2019, On the risk prevention of supply chain trade financing of large and medium-sized state-owned foreign trade enterprises, Invest. Entrep., 14, 31
- Yang, 2019, Construction and analysis of gasoline yield prediction model for catalytic cracking unit based on artificial intelligence algorithm, Chin. J. Pet. (Pet. Process.), 35, 807
- Zhang, 2018, Application of artificial intelligence algorithm in image processing, Commun. World, 38, 63
- Zhou, 2021, Exploring the application of artificial intelligence algorithms in acupuncture research based on brain signal analysis, Shanghai J. Acupunct., 40, 507
- Zhu, 2018, Analysis of the influencing factors of supply chain financial risk transmission and their interaction based on international logistics, Logist. Eng. Manag., 40, 1
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
