Build actuarial-grade probability models and risk management workflows—end to end, in Python Turn deep actuarial theory into real, working models. This comprehensive, code-driven reference takes you from probability foundations through solvency capital, with a laser focus on practical implementation. Each of the 33 dense chapters follows the same high-impact flow: rigorous theory → exam-style multiple-choice questions → complete, runnable Python demonstrations for real insurance problems. Whether you price risks, set reserves, allocate capital, or build internal models, this book shows you exactly how to do it—step by step, with reproducible code and clear actuarial reasoning. Why you’ll love it Tight, no-fluff structure: theory you can trust, checks for understanding, and full Python implementations in every chapter - Designed for working actuaries and advanced students: life, P&C, and ERM applications throughout - Built for production: methods scale from classroom to capital planning, with robust diagnostics and validation What you’ll master Probability and statistical foundations: transforms, convergence, asymptotics, change of measure - Insurance severity and frequency modeling: Pareto/GB2/Weibull, Poisson/NB/zero-inflation, GLMs, Tweedie, GLMMs - Dependence and tail risk: copulas (elliptical/Archimedean/vine), common-shock, multivariate EVT, GEV/GPD - Aggregate risk and computation: compound models, Panjer recursion, De Pril, FFT, saddlepoint, importance sampling - Bayesian and credibility methods: hierarchical models, MCMC, empirical Bayes, experience rating - Time series and processes: NHPP, renewal, Hawkes, INAR/INGARCH, volatility modeling - Reserving and development: chain ladder, Mack, GLM reserving, bootstrap, IFRS 17 measurement - Life contingencies and survival: hazards, frailty, multiple decrement, Thiele equations - Capital and solvency: VaR/TVaR/expectiles, Euler allocation, Solvency II/RBC, ORSA, model risk, stress testing - ALM and markets: stochastic interest and inflation, ESGs, reinsurance optimization, ruin theory Code you can run Clean, commented Python that implements estimation, simulation, and validation - Practical toolchain with NumPy, SciPy, pandas, statsmodels, and visualization - Reproducible workflows for pricing, reserving, capital, and ERM analytics Perfect for Practicing actuaries building pricing, reserving, or capital models - ERM and risk professionals responsible for aggregation and allocation - Quantitative analysts and data scientists entering insurance - Graduate-level actuarial and risk management courses Upgrade your actuarial toolkit with reproducible, regulator-ready methods