Artificial Intelligence in Financial Services
Concepts and the Applications
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Beschreibung
Artificial Intelligence in Financial Services is to show how machine learning is reshaping banking, insurance, and capital markets. Moving beyond buzzwords, the book shows professionals, students, and researchers why AI matters in finance services—and how to deploy it responsibly.
Organized in five concise parts, it opens with the fundamentals of machine learning, deep learning, and large language models. Then, the book walks readers through real-world use cases: credit-scoring engines that out-perform traditional logistic models, robo-advisors that rebalance portfolios in minutes, fraud-detection networks that save insurers millions, and high-frequency trading systems that mine news and social-media sentiment in real time. Dozens of corporate case studies, academic findings, and code-ready project ideas illustrate what works—and what fails—at each stage of the AI pipeline. Dedicated chapters on governance, regulation, explainable AI, and bias mitigation give readers the tools to satisfy regulators while protecting consumers. The final section forecasts how reinforcement learning, DeFi, and human–AI collaboration will shape the next decade of financial innovation.
Readers will learn to evaluate algorithms, engineer features for noisy time-series data, benchmark model performance, and anticipate ethical pitfalls. Finance professionals gain an action plan for piloting AI safely; students and researchers discover up-to-date research agendas and hands-on projects. A basic grasp of statistics and financial terminology is helpful, but no prior coding expertise is required.
Packed with practical insights and future-ready strategies, this book equips readers to turn artificial intelligence from headline hype into competitive advantage.
Artificial Intelligence in Financial Services is to show how machine learning is reshaping banking, insurance, and capital markets. Moving beyond buzzwords, the book shows professionals, students, and researchers why AI matters in finance services—and how to deploy it responsibly.
Organized in five concise parts, it opens with the fundamentals of machine learning, deep learning, and large language models. Then, the book walks readers through real-world use cases: credit-scoring engines that out-perform traditional logistic models, robo-advisors that rebalance portfolios in minutes, fraud-detection networks that save insurers millions, and high-frequency trading systems that mine news and social-media sentiment in real time. Dozens of corporate case studies, academic findings, and code-ready project ideas illustrate what works—and what fails—at each stage of the AI pipeline. Dedicated chapters on governance, regulation, explainable AI, and bias mitigation give readers the tools to satisfy regulators while protecting consumers. The final section forecasts how reinforcement learning, DeFi, and human–AI collaboration will shape the next decade of financial innovation.
Readers will learn to evaluate algorithms, engineer features for noisy time-series data, benchmark model performance, and anticipate ethical pitfalls. Finance professionals gain an action plan for piloting AI safely; students and researchers discover up-to-date research agendas and hands-on projects. A basic grasp of statistics and financial terminology is helpful, but no prior coding expertise is required.
Packed with practical insights and future-ready strategies, this book equips readers to turn artificial intelligence from headline hype into competitive advantage.
Incorporates real-life cases of AI in financial services area Provides fundamental knowledge of machine learning, deep learning, LLM etc., which help reader understand easily Walks readers through cases, and provides hands-on deployment techniques
Autor*in
Wookjae Heo
Themen in »Artificial Intelligence in Financial Services«
Deep Learning Large Language Models Credit Scoring Risk Management Wealth Management Robo-Advisors Algorithmic Trading Fraud Detection InsurTech Feature Engineering Explainable AI Regulatory Compliance FinTech Governance Data Ethics