Based on graduate-level courses at Nanyang Technological University in Singapore, this engaging textbook presents mathematical tools used for financial risk modeling and related analytics. Organized into three parts, the book begins with stochastic modeling. Part II focuses on classical risk measures, and Part III presents more advanced concepts in credit risk. Designed to be largely self-contained, the text assumes only a basic knowledge of undergraduate probability and statistics. Detailed proofs and derivations encourage a thorough understanding. Statistical concepts are illustrated with statistical experiments using actual data, helping students to bridge the gap between theory and practice. All code examples are presented in both Python and R. The code is available within the text and as an online supplement, alongside the complete exercise solutions. This serves as a core text for financial risk management courses within BSc and MSc programs in risk analytics, financial mathematics, actuarial science, and data analytics.