Interdisciplinary Perspectives on Financial Risk: Integrating Economics, Mathematics, and AI for Smarter Investments

Abstract

Financial risk management has undergone a profound transformation by integrating insights
from economics, mathematics, statistics, and artificial intelligence (AI). While traditional risk
assessment techniques, such as Value-at-Risk (VaR), scenario analysis, and stochastic modeling,
provide foundational tools for quantifying financial risk, they often fall short in capturing the
complex, nonlinear, and dynamic behaviors of modern financial markets. Increasingly, financial
institutions operate in environments characterized by high-frequency trading, large-scale data
flows, and rapidly shifting market conditions, which require more adaptive, real-time, and data-
driven decision-making frameworks.
Interdisciplinary approaches that combine economic theory, quantitative modeling, and AI-based
analytics offer a more holistic understanding of risk, enabling improved forecasting, anomaly

detection, and scenario planning. The integration of machine learning, deep learning, and
reinforcement learning techniques allows for the modeling of intricate relationships among
market variables, investor behavior, and systemic risk factors, while also supporting dynamic
portfolio optimization and automated risk mitigation strategies.
This review systematically examines the theoretical foundations, quantitative methodologies, and
practical applications of AI in financial risk management, emphasizing how these tools enhance
prediction accuracy, portfolio performance, and operational resilience. It also evaluates empirical
evidence and case studies demonstrating the effectiveness of AI-augmented approaches in credit,
market, liquidity, and operational risk domains. Furthermore, the paper discusses implementation
challenges, regulatory considerations, and governance issues, as well as emerging trends and
future research directions aimed at building robust, adaptive, and explainable risk management
systems.