With the rapid development of e-commerce, third-party payment enterprises play a crucial role in modern financial ecology. The accompanying financial risks are also increasingly prominent, and the traditional risk assessment methods are difficult to meet the market's demand for real-time monitoring and early warning. Based on Z-Score model and efficiency coefficient method, this paper discusses the effectiveness of building financial risk early warning system of third-party payment enterprises. First of all, Z-Score model can effectively predict the probability of enterprise bankruptcy through comprehensive analysis of key indicators in financial statements. The core of the model lies in the reasonable use of liquidity, profitability and financial leverage. By weighting the importance of different financial indicators, including data collection, indicator selection and model construction, the efficiency coefficient method provides a multi-dimensional evaluation framework, and finally realizes the comprehensive monitoring of the financial status of enterprises. Regular calculation of Z-Score value and combined with efficiency coefficient method for risk assessment can provide timely early warning information for third-party payment enterprises, so as to help enterprises develop more effective risk management strategies. The research in this paper not only provides theoretical support for the risk management of the third-party payment industry, but also provides practical suggestions for the actual operation of related enterprises, which has important application value and practical significance. In the future, combined with big data and artificial intelligence technology, the accuracy and applicability of the financial risk early warning system will be further improved, providing a strong guarantee for the healthy development of the industry.

Tianyou Huang. Construction and Application of Financial Risk Warning System for Third-Party Payment Enterprises Based on Z-Score Model and Efficacy Coefficient Method. Academic Journal of Business & Management (2024) Vol. 6, Issue 12: 121-126. https://doi.org/10.25236/AJBM.2024.061216.

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