Data driven financial risk assessment method for power marketing

Technologies like big data and cloud computing are revolutionizing power company operations, especially in finance. This article proposes a data-driven approach for financial risk assessment in power marketing. Using a framework of risk indicators and data-driven evaluation, the method leverages regression, time series, and machine learning for real-time risk prediction. Strategic suggestions for risk identification, management optimization, and integration are provided. A case study demonstrates improved financial performance and reduced risk after implementing the method, with a 25.1% net profit increase and a 34.1% bad debt rate decrease.