Integrating Statistics and Computer Science in Dynamic Financial Risk Assessment and Investment Strategies

Abstract

Financial risk assessment and investment strategies have evolved significantly with the integration of statistical models and computational advancements. This review explores the role of statistical techniques such as regression analysis, Bayesian inference, and time-series forecasting, alongside computer science methodologies like machine learning, artificial intelligence (AI), and big data analytics in financial risk assessment. The paper highlights how these tools enhance predictive accuracy, optimize portfolio management, and improve decision-making processes. Case studies of AI-driven risk models and algorithmic trading strategies demonstrate the practical implications of this integration. Future research directions, including quantum computing and explainable AI, are also discussed.