Probability Theory Fundamentals
Core probability concepts including axioms, conditional probability, Bayes theorem, and random variables.
Deep dives into the core mathematical, statistical, and professional topics that span actuarial preliminary exams.
Core probability concepts including axioms, conditional probability, Bayes theorem, and random variables.
Time value of money, interest theory, annuities, bonds, and financial derivatives.
Descriptive statistics, hypothesis testing, confidence intervals, and data visualization.
Linear and logistic regression, GLMs, model selection, and predictive analytics.
AR, MA, ARIMA models, forecasting, stationarity, and seasonal decomposition.
Parametric loss models, severity and frequency distributions, and aggregate claims.
Buhlmann, Buhlmann-Straub, classical credibility, and empirical Bayes methods.
Life insurance and annuity valuation, survival models, and benefit reserves.
Defined benefit plan valuation, funding methods, and pension risk management.
Pure premium method, loss ratio method, experience rating, and classification.
Chain ladder, Bornhuetter-Ferguson, and stochastic reserving techniques.
Proportional and non-proportional reinsurance, excess of loss, and stop-loss pricing.
VaR, TVaR, expected shortfall, coherent risk measures, and ERM frameworks.
Kaplan-Meier estimation, Cox regression, hazard functions, and censoring.
Prior and posterior distributions, conjugate families, MCMC, and Bayesian estimation.
Decision trees, random forests, gradient boosting, neural networks, and clustering.
Markov chains, Poisson processes, Brownian motion, and their actuarial applications.
Yield curves, duration, convexity, immunization, and bond portfolio management.
NAIC framework, risk-based capital, solvency standards, and regulatory filings.
ASOPs, Code of Professional Conduct, qualification standards, and ethical obligations.