Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Abstract

Artificial intelligence (AI) proves decisive in today’s rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Reviewing the use of big data in supply chain optimization in the USA

“…The future trends in supply chain optimization also encompass the application of machine learning for supply chain financial risk prevention (Lei et al, 2023;Lukong et al, 2022). This involves the systematic review and analysis of the necessity and urgency of applying AI technology to identify financial difficulties in supply chains.…”

Machine Learning in Financial Transaction Fraud Detection and Prevention

“…This includes the use of complex passwords and multi-factor authentication, regular monitoring of account activity, and security awareness training for employees. At the same time, regulators and fintech companies are developing more sophisticated tools and algorithms that use machine learning and artificial techniques to identify and prevent fraud [7]. Nonetheless, fraudsters continue to evolve their tactics, and the detection and prevention of financial transaction fraud remains an ongoing struggle that requires the concerted efforts and cooperation of society as a whole.…”

A Novel Risk-Perception Model Based on Blockchain for Supply Chain Finance of China Real Estate

“…To compensate for the defects of a single model, Zhu et al [27] proposed a new integrated machine-learning method to construct an integrated model of random subspace-real AdaBoost to predict the credit risk of SMEs, which was applied to analysis and prediction of multisource data. Under the framework of an integrated learning model, Lei et al [8] constructed the chaotic grasshopper optimization algorithm to extract the financial features of enterprises through the complex data-preprocessing process, then used SVM to classify the data, and finally optimized it using the sticky mushroom algorithm to construct the SCF risk precautionary system. Table 1 presents a comparison of the models, accuracy, and sample sizes proposed by some scholars.…”