Intelligent Analysis and Processing of Gaussian Algorithm in Financial Big Data

In the era of huge financial data, traditional manual risk assessment is prone to subjectivity, inaccuracy, and inefficiency. The widely used K-Means algorithm has some drawbacks, including difficulty in determining the number of clusters, choosing the original cluster center, identifying and removing outliers, and measuring distance and similarity. This paper proposes a method for handling financial big data using the Gaussian mixture clustering algorithm. A financial evaluation index system is also established, which includes repayment ability, operation ability, and profitability. This system addresses the drawbacks of artificial risk evaluation and enhances the precision and efficacy of risk assessment and prediction. In addition, the clustering findings' graphical display makes the analysis results simpler to comprehend and provides greater decision-making assistance for financial management staff. And protecting the safety and soundness of the financial system and raising management standards are crucial for businesses. The methodologies and conclusions reported in this work have a broad variety of possible applications and may provide novel ways to the analysis and processing of financial big data.