A Systematic Review on Recently Developed Models for Supply Chain Financial Risk Evaluation

The financing cost of Supply Chain Management (S CM) is decreased by the emergence of supply chain finance. There is a deficiency in risk in terms of strategy and practice due to the growth of a diversified supply chain financial area. Numerous automated risk assessment frameworks are developed using deep learning and machine learning techniques in recent years to address these issues. Hence, industries are faced with the supply chain sustainability risk that is emerged from various resources. In existing research works, the field of supply chain finance is very limited due to the lack of integration. However, it does not address the small and medium sized enterprise which is used in the real time economy. The purpose of this survey is to conduct a literature review on estimating the financial risks associated with SCMs. This study includes various literature works considered for the financial risk evaluation, where the evaluation of deep learning techniques, performance analysis and their challenges while performing the financial risk evaluation are researched. However, the conventional techniques are presented with diverse challenges in estimating financial risks, and this review paper is highly useful for future research work. This research analyzes the estimation techniques, dataset utilized for evaluating the financial risks, implementation tools and performance metrics involved in the evaluation models. Moreover, various research gaps in the existing research works are explored which helps to show the enhanced performance of the supply chain financial risk evaluation model. Here, it explores various financial risk analyses which will be helpful for future research. Finally, the overall challenges and future works on the financial risk evaluation models are discussed to support future works.