Financial supply chain analysis with borrower identification in smart lending platform
作者：
Highlights：
• Defines the importance of financial parameters and its impact on supply chain.
• Provides the proof of dependency of the financial parameter on the cost of debt.
• Prove dependency of the financial parameter on the ratio of the debt-to-equity.
• Proposes the k-Random Boosting Classification model to identify defaulters.
• Identifies crucial features to enhance the probability of getting loans approval.
摘要
•Defines the importance of financial parameters and its impact on supply chain.•Provides the proof of dependency of the financial parameter on the cost of debt.•Prove dependency of the financial parameter on the ratio of the debt-to-equity.•Proposes the k-Random Boosting Classification model to identify defaulters.•Identifies crucial features to enhance the probability of getting loans approval.
论文关键词：Supply chain finance,Peer-to-peer lending platform,Machine learning,Random forest,Collaborative filtering,Similarity estimation
论文评审过程：Received 24 February 2022, Revised 26 June 2022, Accepted 29 June 2022, Available online 6 July 2022, Version of Record 21 July 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118026
