Research on green supply chain finance risk identification based on two-stage deep learning
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DOI: 10.1016/j.orp.2024.100311
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- Amon Simba & Mahdi Tajeddin & Léo-Paul Dana & Domingo E. Ribeiro Soriano, 2024. "Deconstructing involuntary financial exclusion: a focus on African SMEs," Small Business Economics, Springer, vol. 62(1), pages 285-305, January.
- Tahereh Zaefarian & Atieh Fander & Saeed Yaghoubi, 2024. "A dynamic game approach to demand disruptions of green supply chain with government intervention (case study: automotive supply chain)," Annals of Operations Research, Springer, vol. 336(3), pages 1965-2008, May.
- Shengli Chen & Dong Wang & Zheng Wan & Sundarapandian Vaidyanathan, 2022. "Credit Risk Assessment of Small and Medium-Sized Enterprises under the Financial Model of Online Supply Chain," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-13, December.
- Haojie Liao & Huabo Yue & Yibin Lin & Dong Li & Lei Zhang & Wei Liu, 2022. "Enterprise Financing Risk Analysis and Internal Accounting Management Based on BP Neural Network Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, May.
- Kowalski, Michał & Lee, Zach W.Y. & Chan, Tommy K.H., 2021. "Blockchain technology and trust relationships in trade finance," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
- Waseem Ahmed Abbasi & Zongrun Wang & Yanju Zhou & Shahzad Hassan, 2019. "Research on measurement of supply chain finance credit risk based on Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
- Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
- Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
- Shiyan Yin & Kai Yao & Thanaset Chevapatrakul & Rong Huang, 2024. "Reduced disclosure and default risk: analysis of smaller reporting companies," Review of Quantitative Finance and Accounting, Springer, vol. 63(1), pages 355-395, July.
- Ying Liu & Lihua Huang, 2020. "Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477209, January.
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Keywords
Dual carbon; Green supply chain finance; Two-stage deep learning; Ensemble learning; Credit risk;All these keywords.
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