IDEAS home Printed from https://ideas.repec.org/a/eee/oprepe/v13y2024ics2214716024000150.html
   My bibliography  Save this article

Research on green supply chain finance risk identification based on two-stage deep learning

Author

Listed:
  • Liu, Ying
  • Li, Sizhe
  • Yu, Chunmei
  • Lv, Mingli

Abstract

As a resonance product between financial services and the upgrading of the green industry, green supply chain finance has garnered extensive attention in the process of ecological civilization construction. Effectively promoting the green transformation of small and medium-sized enterprises and achieving the "dual carbon" goals necessitate the avoidance of corporate green risks. However, the complex interdependence and information asymmetry among green supply chain finance enterprises result in data characteristics such as multi-source small samples and high-dimensional imbalance. To address these issues, this paper proposes a risk assessment model based on two-stage deep learning. In the first stage, we employ Generative Adversarial Network (GAN) to generate minority class default samples, and utilize Stacked Auto-Encoder (SAE) to extract data features with closed-form parameter calculation capability. In the second stage, the obtained features are input into a Deep Neural Network (DNN), and parameter learning and model optimization are conducted through joint training. Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. The paper is grounded in the green innovation production of enterprises, collecting financial data of 176 upstream and downstream enterprises and corresponding core enterprise green indicators from 2013 to 2022. Experimental results demonstrate that GAN oversampling technique not only enhances the model's AUC metric but also significantly improves the F1 score. Compared with traditional deep learning methods, the proposed two-stage deep integration model effectively reduces training loss and exhibits superiority in identifying green supply chain finance risks.

Suggested Citation

  • Liu, Ying & Li, Sizhe & Yu, Chunmei & Lv, Mingli, 2024. "Research on green supply chain finance risk identification based on two-stage deep learning," Operations Research Perspectives, Elsevier, vol. 13(C).
  • Handle: RePEc:eee:oprepe:v:13:y:2024:i:c:s2214716024000150
    DOI: 10.1016/j.orp.2024.100311
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214716024000150
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.orp.2024.100311?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meiyan Li & Yingjun Fu, 2022. "Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    2. Dong, Ciwei & Chen, Chenyi & Shi, Xiutian & Ng, Chi To, 2021. "Operations strategy for supply chain finance with asset-backed securitization: Centralization and blockchain adoption," International Journal of Production Economics, Elsevier, vol. 241(C).
    3. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    4. Musarra, Giuseppe & Kadile, Vita & Zaefarian, Ghasem & Oghazi, Pejvak & Najafi-Tavani, Zhaleh, 2022. "Emotions, culture intelligence, and mutual trust in technology business relationships," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Toorajipour, Reza & Oghazi, Pejvak & Sohrabpour, Vahid & Patel, Pankaj C. & Mostaghel, Rana, 2022. "Block by block: A blockchain-based peer-to-peer business transaction for international trade," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    6. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    7. Aniruddha Deka & Parag Jyoti Das & Manob Jyoti Saikia, 2024. "Advanced Supply Chain Management Using Adaptive Serial Cascaded Autoencoder with LSTM and Multi-Layered Perceptron Framework," Logistics, MDPI, vol. 8(4), pages 1-25, October.
    8. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    9. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    10. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    11. Pattanayak, Sirsha & Ramkumar, M. & Goswami, Mohit & Rana, Nripendra P., 2024. "Blockchain technology and supply chain performance: The role of trust and relational capabilities," International Journal of Production Economics, Elsevier, vol. 271(C).
    12. Yiyu Xia, 2022. "A Study on Evolution Game of Accounts Receivable Pledge Financing in Supply Chain Finance Model," International Business Research, Canadian Center of Science and Education, vol. 15(12), pages 1-39, December.
    13. Yang Li & Quanlong Liu, 2023. "The Operational Mechanism of Agricultural Products Supply Chain Finance Based on the Mode-Capability-Customer Matching Approach," Sustainability, MDPI, vol. 15(23), pages 1-24, December.
    14. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    15. Amon Simba & Patient Rambe & Samuel Ribeiro Navarrete & Maria Teresa Palomo Vadillo, 2024. "A technostress–entrepreneurship nexus in the developing world," International Entrepreneurship and Management Journal, Springer, vol. 20(3), pages 2019-2046, September.
    16. Biswajit Debnath & Amit K. Chattopadhyay & T. Krishna Kumar, 2024. "An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    17. Liu, An & Wang, Xinyu & Tang, Jiafu, 2024. "Optimizing multi-channel procurement planning under disruption risks," International Journal of Production Economics, Elsevier, vol. 275(C).
    18. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    19. Wang, Kaike & Zhang, Xin & Wang, Shuhong, 2024. "Blockchain technology concerns and corporate financial risk prevention—A quasi-natural experiment for Chinese listed A-share companies," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1496-1512.
    20. Pandey, Dharen Kumar & Hassan, M.Kabir & Kumari, Vineeta & Zaied, Younes Ben & Rai, Varun Kumar, 2024. "Mapping the landscape of FinTech in banking and finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 67(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:oprepe:v:13:y:2024:i:c:s2214716024000150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/operations-research-perspectives .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.