Based solely on the provided article body, artificial intelligence in finance uses machine learning, natural language processing, and generative AI to automate processes, assess credit risk, and support decision making. AI applications span credit scoring, algorithmic trading, fraud detection, finance automation, and AI agents. The financial services industry has moved past pilot-stage experimentation. Artificial intelligence is expected to save the banking industry about $1 trillion by 2030, and the market value of AI in finance is estimated to exceed $166 billion by 2035. The guide walks finance teams through primary use cases, data science practices, and governance controls. Data science underpins every AI application, as data scientists clean, label, and structure data before it reaches a model. Finance leaders rank use cases by revenue impact, risk reduction, and implementation effort, with fraud detection and finance automation typically delivering the fastest return on investment. Each use case needs a clear function owner, with risk teams owning credit scoring, treasury and trading desks owning algorithmic trading, and compliance teams owning AML monitoring.