The Role of Artificial Intelligence in Pricing Financial Derivatives

Abstract

Artificial intelligence (AI) is playing an increasingly important role in the pricing of financial derivatives. Traditional pricing models, such as the Black-Scholes model and binomial tree method, have significant limitations in dealing with complex market characteristics such as volatility smile, path dependence, and stochastic volatility. AI technologies such as machine learning, deep neural networks, and reinforcement learning have effectively improved the accuracy and computational efficiency of derivative pricing through data-driven approaches, providing more flexible valuation frameworks for equity, interest rate, and credit derivatives. This paper reviews the definition and classification of financial derivatives, traditional pricing methods and their limitations, and systematically analyzes the specific applications of AI in this field. Research shows that AI has become an important direction for innovation in modern financial pricing.