Loss Model 2 (ACT 653), Spring 2017.
This is the second part of a two-semester sequence covering the foundations of loss data analytics. This course discusses advanced topics in ratemaking and claims management for short-term insurance products. Specifically, this course examines aggregate loss models that allow insurers to price and manage claims from a pool of risks; it will introduces a set of quantitative tools, known as credibility, for insurers to perform experience rating; the course will also provide an overview on loss reserving techniques for estimating an insurer’s outstanding claims. For statistical inference in empirical applications using insurance data, this course investigates both frequentist and Bayesian approaches.
Loss Models 1 (ACT 652), Fall 2016.
This is the first part of a two-semester sequence covering the foundations of loss data analytics. Loss Models I focuses on tools needed for pricing short-term insurance products and managing insurance claims. Specifically, this course examines frequency and severity loss models, including the impact of coverage modifications. For empirical applications of these models to insurance data, the course also provides an overview of fundamental statistical approaches for inferring patterns from data.
Business Analytics II (BUS 365), Fall 2015.
This course extends the understanding and skill with data analysis techniques, including regression and time-series forecasting. It emphasizes application of these techniques to support organizational aims. Utilizes MS Excel and Access to manage and analyze data sets.
Business Analytics I (BUS 306), Fall 2017.
This course assists students in developing quantitative intuition through a practical application using Excel. Specifically, students learn how to produce summary statistics in both tabular and visual forms using data. They also learn the essentials of probability and apply it to decision problems where there is uncertainty. In addition, the course emphasizes hypothesis testing and regression analysis, with an introduction to simulation methods. Throughout the course, special attention is given to effective writing and presenting data analysis.
Actuarial Science Methods I: Exam P (Probability) Review (ACT 300), Spring 2018.
The goal of this course is to assist students develop a knowledge of fundamental mathematical tools for quantitatively assessing risk. Emphasize the applications of these tools to problems encountered in actuarial science.
Frees, E. & Okine, A. & Saridas, E. (2019). Dependence Modeling Loss Data Analytics