Abstract

This chapter explores the pivotal role of stochastic processes in financial mathematics, with an emphasis on their application in modeling market uncertainty and randomness. By investigating five key areas—option pricing, risk management, portfolio optimization, credit risk analysis, and the integration of stochastic processes with machine learning—it identifies how these processes innovate and transform financial theories and practices. Applying a quantitative methodology, the paper examines relationships between stochastic methods like differential equations and Monte Carlo approaches with dependent variables like accuracy in pricing, risk measures, and optimization results. The findings confirm that stochastic processes improve predictive precision, optimize financial strategies, and improve dynamic risk analysis. Findings also reveal the synergy between the potential of combining machine learning and stochastic methods to push financial modeling further. This chapter emphasizes stochastic processes as the very foundation driving innovation in financial mathematics while identifying gaps and future research opportunities for broader applicability.