In today's rapidly changing financial markets

financial risk management has become a crucial area. The stochastic process

as a mathematical tool for describing and analyzing stochastic phenomena over time

provides strong support for understanding and predicting the dynamics of financial markets. In this paper

we first introduce the basic concepts of stochastic processes

including Markov chain

Brownian motion and Poisson processes

and explore their applicability in the financial field. Subsequently

this paper analyzes the application status of stochastic process in portfolio optimization

credit risk assessment and market risk measurement

which shows the practical value of stochastic process theory in financial risk management. However

stochastic processes also face challenges in practical applications

such as model risk and data quality issues. Model risk mainly stems from simplified assumptions about the market and borrower behavior

while data quality issues involve the accuracy

completeness

and representativeness of the data. To address these issues

this paper proposes a range of strategies

including introducing more realistic assumptions

rigorous cleaning and preprocessing of the data

and establishing mechanisms for the data quality assessment. Finally

the paper summarizes the importance of stochastic process in financial risk management and gives prospects for future research directions. With the continuous development of financial markets and the emergence of new technologies

the application of stochastic process theory in financial risk management will be more extensive

providing more effective risk assessment and control tools for investors and financial institutions. The research in this paper not only provides a new perspective in the field of financial risk management

but also provides a valuable reference for future researchers.