ACTUARIAL STATISTICS FOR RISK MODELING
Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.
Topics
- linear and nonlinear models
- diagnostics and assessment of predictive models
- variable and model selection
- and non-supervised learning techniques
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: ( GEN BUS 317 , ECON 410 , STAT/MATH 310 , STAT 312 , 333 , or 340 ), graduate/professional standing, or declared in Capstone Certificate in Actuarial Science Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Articulate key assumptions pertinent to various statistical methods for risk modeling in insurance. Audience: Both Grad & Undergrad 2. Evaluate actuarial models for risk analysis using graphical procedures, hypothesis tests, and score-based approaches. Audience: Both Grad & Undergrad 3. Demonstrate an understanding of the bias-variance trade-off within the actuarial and risk modeling context and its impact on model selection. Audience: Both Grad & Undergrad 4. Employ resampling techniques for validating models used in risk assessment and management. Audience: Both Grad & Undergrad 5. Explain and interpret statistical package outputs to risk managers and other stakeholders. Audience: Both Grad & Undergrad 6. Apply supervised and unsupervised learning methods to address challenges in insurance applications. Audience: Graduate Learning Outcomes: 1. Articulate key assumptions pertinent to various statistical methods for risk modeling in insurance. Audience: Both Grad & Undergrad 2. Evaluate actuarial models for risk analysis using graphical procedures, hypothesis tests, and score-based approaches. Audience: Both Grad & Undergrad 3. Demonstrate an understanding of the bias-variance trade-off within the actuarial and risk modeling context and its impact on model selection. Audience: Both Grad & Undergrad 4. Employ resampling techniques for validating models used in risk assessment and management. Audience: Both Grad & Undergrad 5. Explain and interpret statistical package outputs to risk managers and other stakeholders. Audience: Both Grad & Undergrad 6. Apply supervised and unsupervised learning methods to address challenges in insurance applications. Audience: Graduate
Sources
- Course catalogue ↗2026-2027