Artificial Intelligence in Finance
From Theory to Practice
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Beschreibung
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.
Migdat Hodžić is Principal and Chief Technology Officer at ARTI Analytics and a professor at the International University of Sarajevo and Džemal Bijedić 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 Bijedić 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 Mešković 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.
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.
Bridges machine learning, GenAI, and finance across credit risk, fraud detection, and markets Supports students and practitioners with applied AI, RAG, LLMs, and regulatory-ready frameworks Advances AI-driven financial decision systems from modeling to governance and real-world deployment
Autor*in
Migdat Hodžić
Themen in »Artificial Intelligence in Finance«
Machine learning for financial services Generative AI in banking Large language models (LLMs) in finance Retrieval-augmented generation (RAG) AI agents in financial operations Credit risk modelling and decision systems AI-driven fraud detection Monte Carlo methods and stochastic simulation in finance Financial market prediction using AI Cryptocurrency valuation AI-enabled digital transformation in banking No-code AI platforms in financial institutions AI infrastructure Artificial general intelligence (AGI) and finance Greenwashing detection using AI