دانشگاه آزاد اسلامی واحد تهران جنوب

صفحه اصلی / دومین همایش دوسالانه پژوهش های نوپدید در حسابداری،مالی و مدیریت

AI and Advanced Tools in Capital Market Risk Management

نویسندگان :

Javad Ahmadirad¹

1- دانشگاه آزاد اسلامی واحد قم

کلمات کلیدی :

AI(artificial intelligence)،machine learning،reinforcement learning،risk management،capital markets،RegTech

چکیده :

The rapid convergence of artificial intelligence (AI), machine learning (ML), deep learning (DL), natural language processing (NLP), and big data technologies with traditional risk management has transformed the analytical capabilities available to capital market participants. This article provides a comprehensive conceptual and theoretical review of how advanced analytical tools influence risk management across markets, instruments, and institutions. We synthesize the evolution from classical risk models to AI-enabled approaches, identify the research gaps, and propose an integrative framework that links data sources, methodological choices, risk typologies (market, credit, liquidity, operational, and systemic risk), and governance considerations. We present a systematic literature review (SLR) protocol and discuss how different AI methods—supervised and unsupervised ML, reinforcement learning (RL), DL, NLP, and hybrid models—address key risk management tasks such as volatility forecasting, value-at-risk (VaR) and expected shortfall (ES), credit risk scoring, default prediction, risk segmentation, stress testing, and regulatory technology (RegTech). The findings highlight the potential gains in predictive performance, scenario analysis, and real-time risk monitoring, while also emphasizing challenges in data quality, model interpretability, regulatory compliance, model risk management (MRM), and ethical concerns. The article concludes with implications for investors, financial institutions, and regulators, and outlines limitations and directions for future research, including standardized benchmarks, data provenance, cross-market transferability, and integration with ESG and climate-related financial risks.