ASA study materials
ASA study resources

ASA study guide

ASA · Work through your exam, one topic at a time.

The Associate of the Society of Actuaries (ASA) is a professional designation awarded by the SOA to candidates who complete the ASA pathway and have their Application for Admission as an Associate approved by the SOA Board of Directors. It is designed for people building an actuarial career who want a recognized credential before, or as a step toward, the FSA designation. The ASA study guide has 12 sections and about 3,566 words.

01

VEE Mathematical Statistics

Mathematical statistics questions test whether you can move between a population model, a sample, and an inferential statement. Know random variables and their distributions, joint and conditional distributions, independence, expectation, variance, covariance, and moment generating functions.

265 words, about 1 min
02

VEE Economics

Economics questions emphasize incentives, constraints, and equilibrium. Understand demand and supply, elasticity, consumer and producer surplus, opportunity cost, marginal analysis, market structures, externalities, and public goods.

258 words, about 1 min
03

VEE Accounting and Finance

Accounting questions test the links among the balance sheet, income statement, and statement of cash flows. Know accrual accounting, revenue and expense recognition, depreciation, working capital, retained earnings, and the difference between book value and market value.

263 words, about 1 min
04

Probability (P) Exam

The Probability exam rewards precise use of axioms and conditioning. Know set operations, independence, mutually exclusive events, conditional probability, Bayes theorem, and the law of total probability.

247 words, about 1 min
05

Financial Mathematics (FM) Exam

Financial Mathematics questions center on dated cash flows. Master accumulation and discount factors, effective and nominal rates, force of interest, annuities, perpetuities, loans, bonds, and yield rates.

259 words, about 1 min
06

Pre-Actuarial Foundations Module

The Pre-Actuarial Foundations Module introduces how actuaries define problems, choose assumptions, build models, and communicate results. Understand the control cycle: define the problem, identify risks, gather and validate data, select assumptions, build and test a model, interpret results, and monitor experience.

246 words, about 1 min
07

Fundamentals of Actuarial Mathematics (FAM) Exam

Fundamentals of Actuarial Mathematics builds from survival models to insurance and annuity values. Know the survival function, force of mortality, life tables, curtate and complete future lifetimes, and select and ultimate mortality.

265 words, about 1 min
08

ALTAM or ASTAM Exam

The ALTAM or ASTAM exam deepens the corresponding track. For ALTAM, focus on multiple decrements, pension and insurance cash flows, premium reserves, gross premiums, expense loads, and profit testing.

249 words, about 1 min
09

Statistics for Risk Modeling (SRM) Exam

Statistics for Risk Modeling emphasizes applying statistical models to insurance and risk problems. Know simple and multiple linear regression, least squares, coefficient interpretation, inference, residual diagnostics, and model selection.

250 words, about 1 min
10

Actuarial Science Foundations Module

The Actuarial Science Foundations Module connects probability, finance, and business context. Understand expected value, present value, risk pooling, diversification, and the difference between risk and uncertainty.

257 words, about 1 min
11

Predictive Analytics (PA) Exam

The Predictive Analytics exam tests the full modeling pipeline: problem framing, data cleaning, feature engineering, model selection, validation, interpretation, and communication. Know regression, GLMs, regularization, trees, random forests, boosting, and neural networks at a conceptual level.

248 words, about 1 min
12

Advanced Topics in Predictive Analytics (ATPA) Assessment

The Advanced Topics in Predictive Analytics assessment expects you to reason about models in a business and governance context. Understand ensemble methods, model stacking, feature selection, hyperparameter tuning, interpretability tools, and monitoring after deployment.

759 words, about 4 min