The financial industry is increasingly adopting Artificial Intelligence (AI) technologies to improve risk assessment and decision-making processes. Traditional financial risk assessment models often rely on historical data, statistical assumptions, and limited predictive capabilities. AI-based financial risk assessment models utilize machine learning, deep learning, natural language processing, and predictive analytics to identify, evaluate, and mitigate financial risks with greater accuracy and efficiency. This study examines the evolution, methodologies, applications, benefits, challenges, and future prospects of AI-driven risk assessment systems. Through a review of current literature and industry practices, the research demonstrates that AI-based models significantly enhance credit risk evaluation, fraud detection, market risk prediction, and operational risk management. The findings suggest that AI technologies provide financial institutions with real-time insights and improved predictive performance, although concerns regarding transparency, bias, data privacy, and regulatory compliance remain critical challenges.