Artificial Intelligence in Finance

From Theory to Practice
Erscheinungsjahr 2027
ISBN: 978-3-032-41747-3
Verlag: Palgrave Macmillan

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This textbook examines how artificial intelligence, machine learning, generative AI, and data-driven decision systems are reshaping financial services. Moving from core AI concepts to applied financial use cases, it covers credit risk assessment, fraud detection, Monte Carlo methods, stock and cryptocurrency valuation, knowledge graphs, AI-enabled IoT, large language models, Retrieval-Augmented Generation (RAG), AI agents, and no-code AI platforms. It also looks at infrastructure choices for enterprise AI, including cloud and on-premise deployment, before turning to sustainable finance, greenwashing detection, and AI governance in regulated environments. Throughout, the book emphasizes decision quality, transparency, accountability, model risk management, and practical implementation. It will be of interest to upper-level undergraduate and postgraduate students in finance, banking, FinTech, risk management, and business analytics, as well as practitioners seeking a structured introduction to AI applications and governance in finance.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Hodžic, Migdat

Hubana, Tarik

Meškovic, Admir

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
• Wirtschaftswissenschaften Betriebswirtschaft Management

Inhaltsverzeichnis

Chapter 1: Introduction.- Part I: Basics of Artificial Intelligence.- Chapter 2: Theory of Artificial Intelligence.- Part II: AI in Safety and Risk Management.- Chapter 3: New Approaches to Credit Risk Assessment: Fusion and Balancing of Human and Machine Data.- Chapter 4: Detection of Bank Fraud Using Artificial Intelligence.- Chapter 5: Monte Carlo Methods in Artificial Intelligence – Tools That Empower Financial Professionals.- Part III: AI in the Financial Markets.- Chapter 6: Application of Artificial Intelligence in the Stock Market.- Chapter 7: Artificial Intelligence in the Valuation of Cryptocurrencies.- Chapter 8: Knowledge Graphs in Finance and Insurance.- Part IV: Digital Transformation and Business Processes.- Chapter 9: The Role of AI-Enabled IoT in the Digital Transformation of the Financial Sector.- Chapter 10: Optimizing Business Processes Using Large Language Models.- Chapter 11: Agents Based on Large Language Models – How Agents Are Changing the Rules of the Game of the Financial Industry.- Chapter 12: Application of “No-Code” Artificial Intelligence Platforms in the Financial Sector – Faster Implementation and Greater Efficiency.- Chapter 13: How to Choose an AI Model for Business Application – Comparative Analysis of Large Language Models.- Part V: AI Infrastructure and Trends.- Chapter 14: Banks Facing a Choice: HPC On-Premise or Cloud for Generative AI?.- Chapter 15: The Status of Artificial General Intelligence (AGI) and Implications for the Financial Sector.- Part VI: AI for Responsible Finance.- Chapter 16: Artificial Intelligence in Sustainable Finance.- Chapter 17: Retrieval-Augmented Generation for Sustainable Finance and Greenwashing Detection.- Part VII: AI Governance in Finance.- Chapter 18: AI Governance in Finance: Scope, Accountability, and Decision Grade AI.- Chapter 19: Regulatory Expectations Translated into Obligation Domains and Controls.- Chapter 20: Governance Operating Model: Committees, Roles, Tiering, and Gates.- Chapter 21: Lifecycle Governance and Assurance: Data, Validation, Monitoring, Gen-AI, and Vendors.- Chapter 22: External AI Governance Frameworks in Finance: A Practical Crosswalk.

Über Autor(innen)

Migdat Hodžic is Principal and Chief Technology Officer at ARTI Analytics and a professor at the International University of Sarajevo and Džemal Bijedic University of Mostar. He holds a PhD from Santa Clara University and works across artificial intelligence, machine learning, optimization, and quantitative finance. He has led applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.

Tarik Hubana is Chief Operating Officer at ARTI Analytics and Assistant Professor at Džemal Bijedic University of Mostar. He holds a PhD in technical sciences from TU Graz and works on applied AI, machine learning, predictive modelling, and enterprise AI systems. His academic and industry experience spans analytics, digital transformation, and AI deployment in data-intensive sectors. He leads applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.

Admir Meškovic is Assistant Professor of Finance at the International University of Sarajevo and Manager of the Center for Islamic Finance, Innovation and Sustainability. He holds a PhD in Finance from the University of Sarajevo and an MBA from Durham University Business School. His research focuses on Islamic finance, FinTech, risk management, and sustainable finance.