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

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

by Grant Richman
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Book cover type: Paperback
  • ISBN13: 9798264066801
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 350
  • Original Price: GBP 29.64
  • Language: English
  • Edition: N/A
  • Item Weight: 812 grams
  • BISAC Subject(s): Insurance / Risk Assessment & Management

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

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