Advances in Quantitative and AI-Driven Approaches for Financial Risk Management and Portfolio Optimization: A Review

Abstract

The field of financial risk management and portfolio optimization has undergone a profound transformation with the integration of quantitative models and artificial intelligence (AI) techniques. Traditional approaches, such as mean–variance optimization, capital asset pricing models (CAPM), and risk parity, have long provided foundational frameworks for investment decision-making and risk assessment. However, these methods often face limitations in addressing nonlinear relationships, dynamic market conditions, high-dimensional datasets, and complex dependencies among assets. Recent advancements in AI—including machine learning, deep learning, and reinforcement learning—have enabled more sophisticated, data-driven approaches to both risk management and portfolio optimization. These AI-driven techniques allow for enhanced predictive accuracy, dynamic portfolio adjustment, real-time risk monitoring, and improved risk-adjusted returns across diverse financial markets.

This review synthesizes the latest research on AI applications in finance, highlighting their role in credit risk assessment, market risk prediction, algorithmic trading, and portfolio diversification. It also discusses the challenges associated with AI adoption, such as model interpretability, overfitting, data quality and availability, computational complexity, and compliance with regulatory standards. Furthermore, the paper identifies emerging trends, including hybrid models that combine traditional quantitative methods with AI, explainable AI (XAI) for transparent decision-making, and sustainable finance applications integrating environmental, social, and governance (ESG) criteria into portfolio strategies.