Real-Time Financial Risk Assessment in Dynamic Markets: A Review of Computational and Analytical Approaches

Abstract

Financial markets are increasingly characterised by rapid structural changes, high volatility, and the interplay of multiple risk types, including market, credit, liquidity, and operational risks. Traditional risk-assessment frameworks, which rely on historical data and static end-of-day models, often fail to capture the speed and complexity of modern financial systems. This review
surveys recent advances in real-time financial risk assessment, highlighting interdisciplinary approaches that integrate economics, mathematics, machine learning, and behavioural finance.
The paper examines the evolution of risk-modelling paradigms, from classical statistical and stochastic models to adaptive frameworks that leverage streaming data architectures and high-frequency analytics. Quantitative methods, such as scenario-based stress testing and stochastic differential modelling, are discussed alongside machine-learning techniques, including supervised, unsupervised, and reinforcement-learning algorithms, for dynamic prediction and risk mitigation. The role of behavioural and sentiment-driven indicators, derived from news, social media, and investor psychology, is also considered in enhancing predictive accuracy.
Applications in derivatives pricing, hedging, and dynamic portfolio management demonstrate the practical relevance of these approaches, enabling proactive responses to market shocks and tail-risk events. Regulatory and compliance considerations, including model transparency, explainable AI, and adherence to evolving financial regulations, are addressed to ensure responsible deployment.