McCarty.Credit

Reading: “Testimony of Kevin M. McCarty, Florida Insurance Commissioner, Florida Office of Insurance Regulation and Representing the National Association of Insurance Commissioners, Regarding: ‘The Impact of Credit-Based Insurance Scoring on the Availability and Affordability of Insurance,’ May 21, 2008,” Subcommittee on Oversight and Investigations of the House Committee on Financial Services, excluding Appendices 1 and 2.

Author: McCarty, K.M.

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BA Quick-Summary: Credit-Based Insurance Scores 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.

BattleTable

Based on past exams, the main things you need to know (in rough order of importance) are:

• disparate impact of credit scoring on certain classes of insureds
• arguments against use of credit scores (see Kucera.Credit for arguments for use of credit scores)

reference	part (a)	part (b)	part (c)	part (d)	part (e)	(part f)
E (2018.Fall #3)	Porter.2-Devlpt	Porter.2-Devlpt	arguments against - credit scores
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

Full BattleQuiz

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In Plain English!

This paper is easy - good bedtime reading. (You only have to know the first 11 pages.) It overlaps with Kucera.Credit but focuses more on potential problems with using credit-scoring in ratemaking.

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, sometimes due to identity theft)

Economic downturns (downturns in economy may have a disparate credit impact on vulnerable populations)

Discriminatory (credit scoring may lead to rates that are unfairly discriminatory - see below for details)

mini BattleQuiz 1

Question: identify examples where credit scoring can be unfairly discriminatory

age: young people don't have a long credit history, elderly people use credit less often (both may result in a lower credit score)

poor families: may use cash versus credit (you need to use credit to get a good credit score)

recent immigrants: may not have access to credit (you need to use credit to get a good credit score)

moral/religious beliefs: certain belief systems may discourage use of credit (you need to use credit to get a good credit score)

mini BattleQuiz 2

There are a few miscellaneous items from old exams that are covered in the mini BattleQuiz. Make sure you look at the following problem because it ties together concepts from a few different readings: (It's also included in the mini BattleQuiz)

E (2014.Fall #2)

mini BattleQuiz 3

Full BattleQuiz