SOA Exam SRM Guide
SRM is the SOA statistics bridge: regression, risk modeling, statistical learning ideas, and the conceptual base for PA.
What Exam SRM Entails
- Statistics for risk modeling, including regression, time series, and simulation ideas.
- Preparation for predictive analytics work and PA-style model communication.
- Core practice: choosing models, interpreting assumptions, and connecting statistical output to risk decisions.
SOA Exam SRM
Official syllabus readings and topic weights are mapped; sample questions are topic-tagging inputs only.
What the official PDFs establish
- Format
- 3.5-hour, 35-question multiple-choice CBT exam.
- Relationship to PA
- SRM is a prerequisite for PA and assumes calculus, probability, and mathematical statistics background.
- R note
- Candidates are not required to learn R, but R output may appear on the exam.
Topic and domain coverage
| Topic | Weight | Source |
|---|---|---|
| Basics of Statistical Learning | 5-10% | Source: Exam SRM syllabus, p. 2 |
| Linear Models | 40-50% | Source: Exam SRM syllabus, p. 2 |
| Time Series Models | 10-15% | Source: Exam SRM syllabus, p. 3 |
| Decision Trees | 20-25% | Source: Exam SRM syllabus, p. 3 |
| Unsupervised Learning Techniques | 10-15% | Source: Exam SRM syllabus, p. 3 |
Chapter and reading intelligence
- Frees Regression Modeling
Chapters 1-3, 5-9, and 11-13 are mapped in the syllabus with section-level exclusions.
- ISLR, second edition
Syllabus readings cover statistical learning, linear regression, resampling, model selection, trees, and unsupervised learning sections.
Official files used by the map
- Official syllabussyllabus
Primary source for format, topic weights, and readings.
- Sample questionssample-questions
Use for topic tagging and practice design; do not republish question text.
Source: Exam SRM Sample Questions
Quick Answer
SRM is pre-fellowship, not an FSA exam. It belongs in the ASA-side statistics and predictive analytics sequence with PA and ATPA.
Official Format And What The Exam Covers
The September 2026 syllabus maps SRM as a 3.5-hour, 35-question multiple-choice CBT exam. The official overview frames it around regression, time series, principal components analysis, decision trees, cluster analysis, and model selection or validation.
The syllabus also makes three practical points candidates should remember: SRM is a prerequisite for PA, calculus and Probability (P) background are assumed, and candidates are not required to learn R even though R output may appear on the exam.
Where The Weight Really Sits
Linear models dominate the syllabus. If a candidate treats SRM as a generic machine-learning survey, they will usually underprepare for the regression-heavy center of the exam.
The rest of the syllabus matters because it rounds out actuarial modeling judgment: time series for temporal structure, decision trees for nonlinear segmentation, and unsupervised learning for dimension reduction or clustering ideas.
- Basics of statistical learning: 5-10%.
- Linear models: 40-50%.
- Time series models: 10-15%.
- Decision trees: 20-25%.
- Unsupervised learning techniques: 10-15%.
What It Connects To
SRM connects Exam P probability to regression, model selection, likelihood-based thinking, and practical risk modeling. It is also the cleanest conceptual preparation for PA because it teaches what a model is doing before PA asks you to explain and defend that model in a business context.
For ActuaryPath, SRM is also one of the best bridges into broader statistics and ML language: generalized linear models, AIC or BIC, principal components, and decision trees are all recognizable outside the actuarial exam world.
SOA vs CAS Context
SRM is not the same as CAS MAS-I or MAS-II, but there is conceptual overlap around probability, statistics, regression, and model interpretation. The useful comparison is not which exam is "harder" in the abstract, but how the downstream signal differs between the ASA predictive-analytics sequence and the CAS modern-statistics sequence.
Common Candidate Mistakes
A common SRM mistake is overfitting your study plan to vocabulary and underweighting interpretation. The exam rewards being able to choose a model, read assumptions, interpret diagnostics, and explain why a procedure is or is not appropriate in context.
Another common mistake is treating SRM as if coding fluency is the tested skill. The exam is about statistical reasoning first. Software output is useful only if you can interpret what it means.
Original exam practice
3 questions built from syllabus outcomes and released-exam patterns. The prompts and answers are original, so they train the skill without copying official exam text.
Exam SRM Model Selection Drill
Original SRM checks for bias-variance, GLM interpretation, AIC and BIC, and tree-model judgment.
- Question 1/Written Answer
Bias-variance tradeoff
A model has low training error but much higher validation error. Name the likely issue and one reasonable response.
Solution and grading points
The likely issue is overfitting. A reasonable response is to reduce model complexity, use regularization, prune the tree, or choose the tuning parameter using cross-validation.
- Identifies overfitting.
- Uses validation error rather than training error as the warning sign.
- Gives a response that reduces complexity or selects it with validation.
- Question 2/Flashcard
GLM link function
In a GLM, what does the link function connect?
Solution and grading points
The link function connects the conditional mean of the response to the linear predictor. For example, a log link models log(E[Y | X]) as a linear function of predictors.
- Names the conditional mean.
- Names the linear predictor.
- Does not describe the link as transforming the raw response value only.
- Question 3/Written Answer
AIC versus BIC
Two ordinary linear models fit the same dataset. Model A has lower AIC, while Model B has lower BIC. What does that disagreement usually signal?
Solution and grading points
It usually signals a tradeoff between fit and complexity. BIC penalizes model size more strongly than AIC for larger samples, so the BIC-preferred model is often simpler.
- States that both criteria balance fit and complexity.
- Identifies BIC as the stronger complexity penalty in larger samples.
- Avoids claiming that one criterion is always correct.