in force premium Answer: full term premium for policies that are in effect at a given point in time
reported losses Answer: paid losses + case reserves
estimated ultimate losses Answer: reported losses + ibnr reserve + ibner reserve
operating expense ratio formula ...
underwriting guidelines Answer: a set of company-specific criteria that can affect decisions of
whether to accept a risk or can alter aspects of the premium calculation
what do underwriting guidelines specify Answer: decision to accept risk, company placement, tier
placement, schedule rating credits/debits
criteria for exposure bases Answer: proportional to expected loss, practical (can be split up into
objective and verifiable), historical precedence,
extension of exposures method Answer: rerating every policy to restate the historical premium to
the
,amount that would be charged under the current rates
case incurred loss Answer: reported loss
classification ratemaking Answer: the process of grouping risks with similar loss potential and
charging different manual rates to
reflect differences in loss potential among the groups
first stage of classification ratemaking Answer: determining which risk criteria effectively segment
risks into groups with similar expected loss experience. usually different levels of a rating variable
second stage of classification ratemaking Answer: calculates the indicated rate differential relative
to the base level for each level being priced
adverse selection Answer: when failure to accurately price individual risks results in a company
having a disproportionally high amount of high risk policy holders
favorable selection Answer: opposite of adverse selection
categories of risk classification criteria Answer: statistical, operational, social, legal
important factors for statistical classification criteria Answer: statistical significance, homogeneity,
credibility
important factors for operational classification criteria Answer: objectivity, cost to administer,
verifiability, constancy
,important factors for social classification criteria Answer: affordability, causality, controllability,
privacy concerns
primary shortcoming of univariate rate classification methods Answer: they do not take into
account the effect of other rating variables
main benefit of multivariate rate classification methods Answer: they consider all rating variables
simultaneously
overall benefits of multivariate rate classification methods Answer: they consider all rating variables
simultaneously, they remove unsystematic effects in the data (noise), they produce model diagnostics
(additional information about the certainty
of results and the appropriateness of the model fitted), and they allow consideration of the interaction,
or
interdependency, between two or more rating variables
difference between glm and lm Answer: glm removes the restrictions of the assumptions that errors
are normally distributed with constant variance. Predictor variables do not have to relate in an additive
fashion
what does the link function of a glm do? Answer: defines the relationship between the expected
response variable and the linear combination of the predictor variables
in a glm the underlying random process typically follows a distribution from what family Answer:
the exponential family
glm analysis is typically performed on what type of data Answer: loss cost data
According to "The Practitioner's Guide to Generalized Linear Models" standard errors are: Answer:
an indicator of the speed with which the log-likelihood falls from the maximum given a change
, in parameter
common statistical diagnostics Answer: standard errors, measures of deviance,
a deviance Answer: a single figure measure of how much the fitted values differ from the
observations
factor analysis Answer: a data mining technique to reduce the number of parameter estimates in a
classification analysis. This can imply
a reduction in the number of variables or a reduction in the levels within a variable.
cluster analysis Answer: a data analysis tool that seeks to combine small groups of similar risks into
larger homogeneous categories or "clusters."
when is cluster analysis most commonly used Answer: when rating for geography
data mining techniques can enhance a ratemaking exercise by: Answer: whittling down a long list of
potential explanatory variables to a more manageable list, providing guidance in how to categorize
discrete variables, reducing the dimension of multi-level discrete variables, and reducing the dimension
of multi-level discrete variables
neural network Answer: gathers test data and invokes training algorithms designed to
automatically learn the structure of the data. This technique has been described as a recursion applied
to a
GLM.
challenges to territorial ratemaking Answer: geography tends to be highly correlated with other
rating factors, and the data in each individual territory may be sparse
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