How Artificial Intelligence Is Reshaping Financial Markets in 2026
AI Is No Longer the Future of Finance — It Is the Present
Artificial intelligence has moved well beyond the experimental phase in global financial markets. In 2026, institutions from investment banks to asset managers are deploying machine learning models, deep learning systems, and large language models across every function — from real-time trade execution to portfolio construction, credit underwriting, and regulatory compliance. The transformation is structural, accelerating, and irreversible.
For professionals, researchers, and academic libraries seeking to understand this shift with scholarly rigour, Artificial Intelligence Applications in Financial Markets, edited by Afef Rachid Amdouni and published by IGI Global Scientific Publishing (2026), is an essential reference. Across nine research chapters contributed by international scholars, the volume maps the full landscape of AI in modern finance.
The Global Context: Why This Book Matters Now
1. The IMF Has Declared AI Cyber Risk a Financial Stability Threat
In May 2026, the International Monetary Fund published a landmark analysis warning that financial stability risks are mounting as AI fuels cyberattacks. Advanced AI models reduce the time and cost needed to exploit vulnerabilities in financial infrastructure, raising the prospect of simultaneous, correlated attacks across interconnected systems. The same AI that protects markets is being weaponised against them — a paradox that demands rigorous academic analysis of AI regulation and risk governance.
Artificial Intelligence Applications in Financial Markets addresses this directly. Chapters on AI regulation, market transformation in intelligent finance, and systemic risk provide the conceptual frameworks finance professionals and policymakers need to navigate this challenge.
2. $3 Trillion in AI Infrastructure Investment Is Flowing Through Global Markets
Morgan Stanley Research estimates that nearly $3 trillion of AI-related infrastructure investment will flow through the global economy by 2028, with more than 80% of that spending still ahead. AI now accounts for nearly half of all investment-grade bond issuance and 87% of global venture capital funding — underscoring how deeply the AI investment cycle has penetrated every corner of finance.
Understanding where this capital is going, how AI-driven markets behave, and what risks it creates is not optional for finance professionals — it is a core competency. This book provides the scholarly grounding that practitioner literature cannot.
3. Nearly 85% of Firms Plan to Increase AI Use in Bond Trading
According to The TRADE’s 2026 AI predictions report, nearly 85% of financial firms plan to increase their use of AI in corporate bond trading over the next year, up sharply from 57% in 2024. Machine-learning components are becoming standard inside commercial algorithmic trading software, and the most successful systems remain hybrid — with AI embedded within broader rules-based frameworks. Chapter 6 of this book, Real-Time AI-Driven Stock Trading Systems: From Prediction to Execution, examines exactly this architecture.
4. Large Language Models Are Entering Portfolio Management
Research published in Portfolio Management Research confirms that LLMs such as GPT-4, Gemini, and Llama are being tested for portfolio optimization, earnings call analysis, and macro forecasting. JPMorgan, BlackRock, and Fidelity are among the institutions exploring AI-powered financial planning tools that parse investor preferences and process macroeconomic datasets at scale. Chapter 8 of this volume, The Use of Large Language Models in Financial Forecasting, investigates the potential of these models with real financial data.
5. AI Value Is Concentrating — Only 20% of Companies Are Capturing 74% of the Gains
PwC’s 2026 AI Performance Study reveals a stark divide: nearly three-quarters of AI’s economic value is captured by just one-fifth of organisations. The gap between AI leaders and the rest of the market is widening rapidly. For financial institutions and academic collections, understanding the strategic, technical, and regulatory dimensions of AI adoption is now a competitive necessity.
What the Book Covers
Spanning 306 pages and nine chapters, Artificial Intelligence Applications in Financial Markets covers the full spectrum of AI in modern finance:
- Comprehensive survey of AI and deep learning models in stock market trading
- Emerging trends in AI-driven finance using topic modelling
- AI-powered forecasting and portfolio optimization from predictive models to strategic investment decisions
- Deep learning vs. classical statistical techniques for stock price prediction
- AI in volatile and less liquid markets
- Large language models for financial forecasting and decision-making
- Machine learning classification of digital wallet applications by transaction intensity
- ESG and sustainable finance across AI-driven financial systems
Who Should Read This Book
This title is essential for finance and economics libraries, business schools, financial institutions, investment firms, fintech research departments, and professional collections building a current reference base in AI and financial markets. It is equally relevant for portfolio managers, quantitative analysts, financial regulators, and academic faculty in economics, finance, and data science programmes.
Also Available: Financial Markets 2026 Catalogue
Browse our curated selection of new 2026 titles in financial markets, fintech, AI in finance, and investment strategy. Download the PDF catalogue:
📘 Download Financial Markets 2026 Catalogue (PDF)
Q&A
What does Artificial Intelligence Applications in Financial Markets cover?
The book covers AI technologies transforming financial analysis and decision-making across established and emerging markets, including algorithmic trading, deep learning for stock price forecasting, portfolio optimization, large language models in financial forecasting, ESG investing, and real-time trading systems. It is edited by Afef Rachid Amdouni and published by IGI Global Scientific Publishing in 2026.
How is AI being used in financial markets in 2026?
In 2026, AI is deployed across trading, portfolio management, credit underwriting, fraud detection, regulatory compliance, and financial forecasting. Machine learning models analyse market data in real time, while large language models process earnings calls, news sentiment, and macroeconomic reports to support investment decisions.
What are the risks of AI in financial markets?
The IMF has warned that AI amplifies cyber threats to financial stability, enabling faster, cheaper, and more widespread attacks on interconnected financial infrastructure. Systemic risks also include herding behaviour in AI-driven markets, reduced liquidity in stress scenarios, and regulatory gaps in AI-generated financial advice.
Can large language models be used for stock market prediction?
Research shows that LLMs such as GPT-4 can assist in financial forecasting by processing large volumes of financial text data, analysing earnings calls, and generating macroeconomic narratives. They perform best as components within hybrid systems that combine AI inference with structured quantitative models.
Where can I buy Artificial Intelligence Applications in Financial Markets?
You can order Artificial Intelligence Applications in Financial Markets directly at CLNZ Books. The price includes worldwide shipping via international courier. Payment is accepted by credit card and PayPal.