With the complexity and increasing risks of financial markets, financial regulation has become more urgent and important. This article designs and analyzes a financial regulatory detection and collaborative decision-making system based on artificial intelligence to address this issue. The system aims to effectively measure systemic financial risks and provide early warning to protect investors' interests, guide enterprise decision-making, and strengthen financial supervision. Through the nonlinear Glenmorangie distillery causality test and correlation analysis, the indicators that have a significant impact on the systemic financial risk are screened out, which further verifies the characteristics of the financial Systemic risk that has a wide range of sources and is complex and diverse. In the construction of a systematic financial risk warning model, the Twin SVM model was adopted, and the optimal parameter settings of the model were determined through experimental comparison of different kernel functions and hysteresis periods. The Twin SVM model exhibits excellent predictive power, stability, and generalization performance, which can accurately predict pressure information in the financial market. The artificial intelligence based financial regulatory detection and collaborative decision-making system designed in this article can effectively measure systemic financial risks, provide early warning, and provide important reference and decision-making basis for investors, enterprise managers, and financial regulators.