SOA ATPAAdvanced Topics in Predictive Analytics

Associateship (ASA)

Multi-hour assessment·Project-based·≈120 study hours

Syllabus learning objectives

Paraphrased from the SOA syllabus so the study-plan builder and practice sets track the topics you will actually be examined on. Weights are the official topic ranges.

Unsupervised learning & feature engineering

30%

Apply clustering and dimension-reduction techniques as a preprocessing step in a predictive analytics workflow.
Engineer features from raw, unstructured or high-cardinality data suitable for advanced models.
Detect anomalies/outliers using unsupervised techniques and assess their effect on downstream models.
Evaluate the tradeoff between automated feature engineering and interpretability.

Time-series & forecasting models

30%

Fit advanced time-series models (state-space, ARIMAX, hierarchical/panel forecasting) to actuarial data with covariates.
Assess forecast accuracy and uncertainty using backtesting and prediction intervals.
Apply forecasting reconciliation techniques across hierarchical or grouped time series.
Identify structural breaks and regime changes and adjust models accordingly.

Model governance, fairness & communication

40%

Apply model governance frameworks, including documentation, validation and ongoing monitoring standards.
Assess a predictive model for disparate impact and unfair discrimination against protected classes.
Recommend model risk mitigation strategies (bias testing, explainability tools, monitoring dashboards).
Communicate model risk, limitations and governance findings to technical and executive stakeholders.

Overview

ATPA is an assessment (not a traditional exam) requiring an analysis project on advanced topics: clustering, dimensionality reduction, forecasting, and responsible-AI/model governance.

Duration: Multi-hour assessment
Questions: Project-based
Style: Computer-based with software
Passing: Pass/fail

Syllabus map

Unsupervised learning & feature engineering

30%

GLMs and Predictive Modeling in Pricing

Time-series & forecasting models

30%

Forecasting Premium, Loss, and Profitability

Model governance, fairness & communication

40%

Actuarial Communication and ASOP 41 Actuarial Data Quality and ASOP 23

Key formulas

Silhouette $\;s_i=\dfrac{b_i-a_i}{\max(a_i,b_i)}$ ; ARIMA $(p,d,q)$ : $\phi(B)(1-B)^dy_t=\theta(B)\varepsilon_t$ .

Study strategy

Practice writing model documentation that satisfies ASOP 56 (modeling).
Be fluent in one toolset (R tidyverse or Python scikit-learn) end to end.

Common traps

Using k-means on unscaled features.
Fitting ARIMA to non-stationary series without differencing.

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