Application of AI in Real-time Credit Risk Detection

In recent years, with the rapid development of the economy, the credit bond market has accumulated great risks. Under the background of increasing regulatory pressure, frequent defaults and external "black swan" events, market liquidity depletion events occur from time to time, which in turn leads to credit bond pricing failure and hinders the normal operation of the market. Based on the LSSVM model, this study combines the nonlinear and time-varying characteristics of the credit bond market, optimizes the risk detection method, and analyzes the performance of the model through empirical research. The experimental part takes the credit bonds issued by enterprises as the research object, selects relevant financial and market variables, and verifies the efficiency and accuracy of LSSVM in credit bond default prediction through sample data screening and modeling analysis. The results show that the model not only improves risk prediction capabilities, but also shows significant advantages in data complexity and calculation efficiency, providing new technical ideas for risk prevention and control in China's bond market.