Probability Models and Risk Management for Actuaries With Python: A Code-First Guide to Insurance Risk, Capital, and Decision-Making

By Grant Richman

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 itTight, no-fluff structure: theory you can trust, checks for understanding, and full Python implementations in every chapterDesigned for working actuaries and advanced students: life, P&C, and ERM applications throughoutBuilt for production: methods scale from classroom to capital planning, with robust diagnostics and validationWhat you'll masterProbability and statistical foundations: transforms, convergence, asymptotics, change of measureInsurance severity and frequency modeling: Pareto/GB2/Weibull, Poisson/NB/zero-inflation, GLMs, Tweedie, GLMMsDependence and tail risk: copulas (elliptical/Archimedean/vine), common-shock, multivariate EVT, GEV/GPDAggregate risk and computation: compound models, Panjer recursion, De Pril, FFT, saddlepoint, importance samplingBayesian and credibility methods: hierarchical models, MCMC, empirical Bayes, experience ratingTime series and processes: NHPP, renewal, Hawkes, INAR/INGARCH, volatility modelingReserving and development: chain ladder, Mack, GLM reserving, bootstrap, IFRS 17 measurementLife contingencies and survival: hazards, frailty, multiple decrement, Thiele equationsCapital and solvency: VaR/TVaR/expectiles, Euler allocation, Solvency II/RBC, ORSA, model risk, stress testingALM and markets: stochastic interest and inflation, ESGs, reinsurance optimization, ruin theoryCode you can runClean, commented Python that implements estimation, simulation, and validationPractical toolchain with NumPy, SciPy, pandas, statsmodels, and visualizationReproducible workflows for pricing, reserving, capital, and ERM analyticsPerfect forPracticing actuaries building pricing, reserving, or capital modelsERM and risk professionals responsible for aggregation and allocationQuantitative analysts and data scientists entering insuranceGraduate-level actuarial and risk management courses
Upgrade your actuarial toolkit with reproducible, regulator-ready methods

• Format: Paperback
• Language: English
• ISBN: B0FQ3LK4JC
• ISBN13: 9798264066801
• Release Date: September 2025
• Publisher: Independently Published
• Length: 350 Pages
• Weight: 1.79 lbs.
• Dimensions: 0.7" x 8.5" x 11.0"