Difference between revisions of "McCarty.Credit"
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:: <span style="color: red;">'''E'''</span>rrors ''(50% of credit reports have errors)'' | :: <span style="color: red;">'''E'''</span>rrors ''(50% of credit reports have errors)'' | ||
:: <span style="color: red;">'''E'''</span>conomic downturns ''(may have a disparate impact on vulnerable populations)'' | :: <span style="color: red;">'''E'''</span>conomic downturns ''(may have a disparate impact on vulnerable populations)'' | ||
− | :: <span style="color: red;">'''D'''</span> | + | :: <span style="color: red;">'''D'''</span>iscriminatory ''(credit scoring may lead to rates that are unfairly discriminatory)'' |
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Revision as of 18:46, 26 November 2018
Representing the NAIC, McCarty presented his views on the impact of credit-based insurance scoring on the availability and affordability of insurance. This reading is closely related to Kucera.Credit.
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Based on past exams, the main things you need to know (in rough order of importance) are:
- arguments against use of credit scores (see Kucera.Credit for arguments for use of credit scores)
- disproportionate impact of credit scoring on certain classes of insureds
reference part (a) part (b) part (c) part (d) part (e) (part f) E (2017.Fall #1) see Kucera.Credit arguments against
- credit scoressee Kucera.Credit E (2016.Spring #1) disparate impact:
- gendercredit score vs frequency
- explainE (2015.Fall #1) disparate impact:
- agedisparate impact:
- other than agemetrics / drivers:
- underlying credit scoresE (2014.Fall #2) definition:
- cost-based ratesequitable rates:
- 2 conditionstexting
- is it equitableBloom's Taxonomy:
- financial stabilitysee NAIC.IRIS Bloom's Taxonomy:
- proxy variablesE (2014.Spring #5) see Kucera.Credit arguments against
- credit scoressee Kucera.Credit
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In Plain English!
This paper is easy - good bedtime reading. (You only have to know the first 11 pages.)
Question: identify arguments against the use of credit-scoring [Hint: FEED]
- Frequency (credit score is correlated only with frequency, not with severity of claims)
- Errors (50% of credit reports have errors)
- Economic downturns (may have a disparate impact on vulnerable populations)
- Discriminatory (credit scoring may lead to rates that are unfairly discriminatory)
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Memorize:
Conceptual:
Calculational: