*Auto Owners Insurance Company*
**Proposed Project for the
MSU Industrial Math Students**

Segment Data and Fit Loss Distributions to Claims Information

**Project Overview:** Enterprise Risk Management (ERM) and
loss reserve analysis relies upon a fundamental understanding of the claims
reporting process. In particular, predicting and modeling prospective frequency
and severity loss activity for the company is important. One of the key
considerations is identifying homogeneous data subsets while taking into account
data credibility. Characteristics to be considered are coverage, size of loss,
deductible, limit, state, company, etc.

The goal of this project is to use statistical techniques to identify homogeneous segments of data that provide the best loss distribution fit in to frequency, severity, and pure premium data, taking into consideration the characteristics previously listed.

**Project Description:**Data files containing exposure, number of claims, and incurred losses will
be provided along with associated data characteristics. The first step is to
group data into optimal segments to produce the best model fit. The second
step is to fit frequency, severity, and pure premium loss distributions to the
selected data segments. The third step is to simulate future losses using the
loss distributions generated in step 2. Statistical software is required to
determine the optimal data segments and loss distributions. Auto-Owners plans
to receive regular updates on the project’s progress.

**Benefit to Auto-Owners:** Interaction with Michigan State
Industrial Math Students would enable Auto-Owners to determine more optimal data
groupings for loss reserving and learn new techniques to fit loss distributions
to the data. These techniques can be used to enhance Auto-Owners ERM modeling
initiatives.