Leveraging Electric Power Data for Enhanced Credit Assessment and Risk Control: A Framework for Data Monetization and Innovation

• Main Authors: Zhao, Yingying, Guo, Naiwang, Wu, Yi, Tian, Yingjie, Su, Yun
• Format: Conference Proceeding
• Language: English
• Subjects: big data mining; data fusion; Data models; Decision making; Electricity; enterprise portrait; Evaluation models; Power credit; Power systems; scorecard model; Systematics; Technological innovation
• ISSN: 2693-8928
• DOI: 10.1109/CSCloud62866.2024.00034

This paper responds to official policies by exploring the positive role of electric power data in areas such as financial risk control. It aims to provide decision support for State Grid Corporation in data monetization and innovative business models, while addressing pain points for governments, financial institutions, and other businesses. By integrating electric power data, the project establishes an enterprise credit evaluation model to overcome the limitations of traditional credit profiling. Leveraging data mining methods and statistical modeling techniques, a credit scorecard model is developed based on electric power data, enabling the calculation of credit scores and corresponding risk levels. This model enhances risk prevention and decision-making effectiveness for relevant institutions. The project contributes to the systematic, normalized, and sustainable development of electric power big data credit reporting, serving other credit markets and promoting the improvement of various credit platforms. The findings provide a foundation for market participants to assess overall risk situations and improve credit risk management. This paper demonstrates the value of integrating electric power data in credit assessment and highlights its potential for enhancing risk control and decision-making processes.