Data-Driven Credit Risk Assessment and Optimization Strategy Exploration

With the rapid development of data-driven technology, the financial sector is increasingly reliant on data-driven approaches to credit risk assessment. This paper analyzes the application of decision tree, support vector machine, neural network and other models in credit risk assessment, discusses the current problems of data quality, bias, transparency and computing resources, and puts forward optimization strategies, such as strengthening data cleaning, reducing data bias, improving algorithm fairness, enhancing model transparency and optimizing computing resource allocation. The goal is to improve the accuracy and efficiency of assessments.