Actuarial Analytics

Course Description

Predictive analytics is a key component of actuarial work. It helps improve solutions to traditional actuarial problems such as forecasting mortality, setting loss reserves, predicting policyholder behavior, and establishing classification ratemaking schemes. Actuaries also use these techniques for improving insurance operations through data-driven decision making. This course introduces students to statistical learning and linear models, with a focus on applying these tools to actuarial business decisions in an insurance or consulting environment. Topics covered include: types of modeling problems, methods of assessing model accuracy, exploratory data analysis, exponential family of distributions, parameter estimation, diagnostic tests of model fit and assumptions, model selection and interpretation, calculation of predicted values and confidence/prediction intervals in the context of ordinary least squares regression, k-nearest neighbors, and generalized linear models. In addition, the course aims to enhance students' programming skills.

Code

AS 3504

Credits

3

Subject

AS

Programs That Include This Course

• Bachelor's Bachelor of Business Administration in Actuarial Science
• Minor Minor in Actuarial Science