Difference between revisions of "McCarty.Credit"

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(In Plain English!)
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==In Plain English!==
 
==In Plain English!==
  
This paper is easy to read. If you have time, glance it over as you are only responsible for the first 11 pages.
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This paper is easy - good bedtime reading. ''(You only have to know the first 11 pages.)''
  
Basically, McCarthy is concerned that use of credit-based insurance scores disparately impacts certain classes of people thus has a discriminatory effect. He argues that even if a tool may show mathematical correlations with insurance claims, this does not necessarily make it fair and a valid criterion for insurance purposes.
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:{| class="wikitable"
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|-
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| '''Question''': identify arguments against the use of credit-scoring [Hint: <span style="color: red;">'''FEED'''</span>
  
* list 4 cons with memory trick
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: <span style="color: red;">'''F'''</span>requency ''(credit score is correlated only with <u>frequency</u>, not with severity of claims)''
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: <span style="color: red;">'''E'''</span>rros ''(50% of credit reports have errors)''
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: <span style="color: red;">'''E'''</span>conomic downturns ''(may have a disparate impact on vulnerable populations)''
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: <span style="color: red;">'''D'''</span>discriminatory ''(credit scoring may lead to rates that are unfairly discriminatory)''
  
 
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Revision as of 18:45, 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 scores
see Kucera.Credit
E (2016.Spring #1) disparate impact:
- gender
credit score vs frequency
- explain
E (2015.Fall #1) disparate impact:
- age
disparate impact:
- other than age
metrics / drivers:
- underlying credit scores
E (2014.Fall #2) definition:
- cost-based rates
equitable rates:
- 2 conditions
texting
- is it equitable
Bloom's Taxonomy:
- financial stability
see NAIC.IRIS Bloom's Taxonomy:
- proxy variables
E (2014.Spring #5) see Kucera.Credit arguments against
- credit scores
see 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)
Erros (50% of credit reports have errors)
Economic downturns (may have a disparate impact on vulnerable populations)
Ddiscriminatory (credit scoring may lead to rates that are unfairly discriminatory)

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BattleCodes

Memorize:


Conceptual:


Calculational:

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