I am a member of the Course 7 working group for the new Society of =
Actuaries examinations. We are looking for suggestions with regard to =
materials (texts or papers) for this exam and would appreciate hearing =
from you. Below I have listed a summary of the learning objectives for =
this course. They are provisional because they will change as we discover =
more source material. We are primarily interested in the modeling process =
and data management. Many texts we have found simply contain chapter =
after chapter of various models, with little information about the process =
itself. While material with an actuarial bent is preferred, it is not =
necessary.
It would be more efficient to e-mail your responses only to me (Stuart.=
Klugman@Drake.edu). I will post the collection, which will provide an =
opportunity for further comments. Thank you for your attention.
Course 7 provisional learning objectives
Overall objectives:
The student must be able to demonstrate the ability to use models to solve =
business problems and to communicate results. The primary emphasis of the =
course is on the modeling process, solving business problems and effective =
communication. Technical knowledge of a limited number of models will =
provide the context for meeting the primary objectives.
Specific objectives:
1. The context of modeling
What is a model, what is an actuarial model, the primary actuarial models =
used in survival analysis, credibility, risk theory, ruin theory, option =
pricing, and cash flow testing. The process, including the feedback loop =
plus common principals underlying modeling. Limitations of modeling plus =
sources of error.
2. Model design, selection and setup
Justifying model selection, setting assumptions and parameters, advantages =
and limitations of various models, effects of data quality and =
accessibility, effects of regulations, implicit versus explicit =
assumptions, and sensitivity analysis.
3. Input data selection and analysis
Know how to find data and the balance between quality, accessibility, =
credibility, and relevance. How to evaluate data quality and understand =
its impact.
4. Analysis of results
How to tell if results are reasonable, sensitivity of output to changes in =
input, recognition of the useful life of a model.
5. Communication
Clearly and accurately communicate the process and the results by =
understanding the nature of the audience, effects of standards of practice =
and regulation, use of appropriate format and media, and maintenance of =
internal documentation.
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Stuart Klugman, FSA
Principal Financial Group Professor of Actuarial Science
Drake University
2507 University Avenue
Des Moines, IA 50311 USA
ph: 515-271-4097
e-mail: Stuart.Klugman@drake.edu