Actuarium

Exams

Study guides, original practice questions with worked solutions, and past-paper strategy for every CAS, SOA and CPCU exam — each linked to the Library so you study the concept, not just the syllabus.

43 exams & requirements361 practice questionsKaTeX-rendered solutions

Casualty Actuarial Society — ACAS and FCAS for property & casualty.

Preliminary

Exam 1 Probability (P)

Preliminary

Exam P/1 tests calculus-based probability: set theory, conditional probability and Bayes, discrete and continuous univariate distributions, transformations, moment generating functions, and joint/conditional/marginal distributions. It is shared with the SOA and is the usual first exam.

30 multiple-choice3 hours300 study hrs9 MC · 2 written

Exam 2 Financial Mathematics (FM)

Preliminary

Exam FM/2 covers interest theory: accumulation functions, nominal and effective rates, annuities, loan amortization and sinking funds, bond pricing and amortization, yield curves and spot/forward rates, duration, convexity, and immunization. Derivatives were removed from the FM syllabus in 2022; the emphasis is now firmly on cash-flow valuation.

30 multiple-choice2.5 hours250 study hrs9 MC · 2 written

MAS-I Modern Actuarial Statistics I

Preliminary

MAS-I covers Poisson processes and Markov chains, survival and reliability, parametric estimation and hypothesis testing, GLMs with an actuarial lens, and ARIMA time-series basics. It is the first exam with an explicit modeling mindset.

45 multiple-choice4 hours350 study hrs9 MC · 2 written

MAS-II Modern Actuarial Statistics II

Preliminary

MAS-II is the credibility and modern-modeling exam: classical and Bayesian credibility, conjugate priors and MCMC diagnostics, penalized regression, tree ensembles, and linear mixed models as a generalization of Bühlmann–Straub.

45 multiple-choice4 hours350 study hrs9 MC · 2 written

Associateship (ACAS)

Fellowship (FCAS)

Non-exam requirement

Study-plan builder

Choose your exam, dates and weekly time blocks. The builder weights weeks by syllabus topic, reserves the final 15% for review and mock exams, and tracks every session you complete.

Pick your study days; block lengths are scaled to hit your weekly target.

Mon1.5h
Wed1.5h
Sat5h

Suggested timeline: 37 weeks at 8 h/week (≈250 recommended hours incl. 15% review)

TopicWeightHoursResourcesPractice
Basics of statistical learning
  • Distinguish supervised from unsupervised learning and regression from classification problems.
  • Apply the bias-variance tradeoff and cross-validation to assess and tune model complexity.
  • Compute and interpret common model evaluation metrics (RMSE, AUC, confusion matrix, lift).
  • Describe the train/validation/test workflow and the risk of overfitting and data leakage.
15%32 1 MC · 1 written
Linear models
  • Fit and interpret multiple linear and logistic regression models, including coefficient interpretation and significance testing.
  • Apply regularization (ridge, lasso, elastic net) to control overfitting and perform variable selection.
  • Diagnose regression assumption violations using residual plots, leverage and influence statistics.
  • Extend linear models to generalized linear models (Poisson, gamma, Tweedie) for actuarial response variables.
30%64 3 MC
Time series
  • Identify stationarity and apply differencing/transformations to achieve it.
  • Fit and forecast ARIMA and exponential smoothing models using ACF/PACF diagnostics.
  • Evaluate time-series model fit via residual diagnostics and out-of-sample forecast error.
  • Apply time series decomposition to separate trend, seasonal and residual components.
15%32 1 MC
Principal components analysis
  • Compute principal components from a covariance/correlation matrix and interpret loadings and explained variance.
  • Apply PCA for dimension reduction prior to modeling and for visualization of high-dimensional data.
  • Select the number of components using scree plots and cumulative variance explained.
  • Recognize the effect of scaling/standardization on PCA results.
10%21 1 MC
Decision trees
  • Grow classification and regression trees using recursive binary splitting and impurity/variance-reduction criteria.
  • Apply pruning (cost-complexity) to control tree size and avoid overfitting.
  • Fit ensembles (bagging, random forests, boosting) and interpret variable importance measures.
  • Compare tree-based methods with GLMs on interpretability, accuracy and handling of nonlinearity/interactions.
20%43 1 MC · 1 written
Cluster analysis
  • Apply k-means and hierarchical clustering algorithms and interpret dendrograms.
  • Select an appropriate number of clusters using elbow, silhouette or gap-statistic methods.
  • Choose distance/dissimilarity measures appropriate to the data type.
  • Interpret cluster assignments for actuarial segmentation applications (e.g., risk classification).
10%21 1 MC

12 weeks · 96 planned hours for SOA SRM · recommended ≈250 hrs (154 short)

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