Contemporary financial and insurance data analytics is a complex, nuanced, and layered subject. Students and practitioners in the area are often overwhelmed by the mathematical theory underlying it, coming away from the topic confused. A resource that combines a focus on practical solutions--but also elegantly explains the mathematical and statistical foundation--is sorely needed. In Financial Data Analytics, an interdisciplinary team including an actuarial professional, an applied mathematician and statistician, a working data analyst, and a quant delivers an authoritative and enduring combination of traditional financial statistics, effective machine learning tools, and mathematics. This book explains contemporary techniques used for data analytics in finance and insurance with a strong emphasis on mathematical understanding and statistical principles. It connects those techniques and principles with common, hands-on financial problems and illustrates their solutions with working Python and R code examples. The book can also be viewed as a research monograph, aiming to introduce to readers cutting-edge results stemming from the authors' own research findings, in the hope of clearly depicting the research discipline and scope of financial data analytics. The authors demonstrate how to correctly evaluate financial and insurance data quality and use the distilled knowledge obtained from data to make timely, profitable financial decisions. They explain how to apply data dimension reduction tools to enhance supervised learning and describe how to select suitable data analytics tools for a variety of given datasets and purposes. Financial Data Analytics includes extensive coverage of the materials tested by several professional examinations, including the Stochastic Risk Modelling (SRM), Predictive Analytics (PA) and Advanced Topics in Predictive Analytics (ATPA) exams offered by the Society of Actuaries, and the Actuarial Statistics exam offered by the Institute and Faculty of Actuaries. An intuitive and hands-on resource for senior undergraduate and graduate students studying financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and AI mathematics, the book will also earn a place in the hands of practicing quantitative analysts working in investment and commercial banking.