This comprehensive academic introduction to artificial intelligence applications in financial services covers the theoretical foundations and practical deployments of machine learning, natural language processing, and deep learning across the spectrum of financial industry use cases. The book begins with accessible introductions to the core AI and machine learning concepts — supervised and unsupervised learning, neural networks, reinforcement learning, and NLP — before systematically applying them to the financial domain. Coverage includes AI applications in asset management, from factor model enhancement and portfolio optimization to sentiment analysis of financial news and earnings call transcripts. Risk management applications covered include credit scoring, fraud detection, stress testing, and real-time transaction monitoring. The book also addresses regulatory compliance automation, algorithmic customer service through robo-advisors and chatbots, and the systematic challenges of deploying AI in regulated financial environments where explainability and auditability requirements often conflict with the opacity of deep learning models. Critically, the authors address the specific data quality, bias, and overfitting challenges that make AI in finance more treacherous than in other domains, where abundant, clean, stationary data is more readily available. Written for practitioners and graduate students with some technical background, this is a solid foundational reference for anyone entering the intersection of AI and financial services.