A Review of Monte Carlo Simulation in the Field of Financial Credit Risk Prediction

In recent years, global economic volatility has intensified, and rising market uncertainties have continuously exacerbated, the international situation has shifted, and the uncertainty of the market environment continues to rise. So the operating pressure on small and medium-sized enterprises is constantly increasing. Consequently, the transmissibility and complexity of credit risk have significantly increased. The complexity of credit risk renders traditional static financial risk prediction methods incapable of coping with the complex scenarios involving multiple factors interacting with each other. As a numerical analysis method based on random sampling, Monte Carlo simulation, with its core advantages of being capable of handling multiple variables and supporting dynamic simulation, is increasingly widely applied in the field of financial risk assessment. This essay takes credit risk prediction as the starting point, outlining the core principles of Monte Carlo simulation and the implementation process for risk analysis, and elaborate on their application pathways and modeling logic in three specialized financial fields: real estate investment appraisal, insurance actuarial risk control, and financial derivatives pricing, compare and analyse the adaptability advantages and inherent limitations of this method in different situations. The findings indicate that Monte Carlo simulation can effectively depict the multi-risk resonance effect and the extreme tail loss distribution, addressing the vulnerabilities of traditional single-factor analysis and static rating methods. However, there are also common problems such as weak stability of small samples, high dependence on parameters, and insufficient depiction of heavy-tailed risks. This article summarizes the general characteristics and optimization directions of this method, which can serve as a reference for basic financial risk analysis.