Federal Advisory Committee on Insurance 

Subcommittee on Availability of Insurance Products 

Background for Discussion on November 8, 2019 Conference Call 

Subcommittee Topics/Issues 

During the subcommittee’s August 2019, the group identified three issues for future discussion 

and potential recommendations to FACI:1 

1. Long-Term Care Insurance 

2. Disparate Impact 

3. Insurance-Specific Principles for Artificial Intelligence and Digital Rights/Data Privacy 

As discussed below, the co-chairs of the subcommittee on availability have decided to discuss 

issues 2 and 3 on the November 8, 2019 call. 

Long Term Care Insurance 

A number of state and federal activities related to long-term care insurance are on-going. 

The NAIC has established an Executive Committee Task Force to address a number of 

regulatory and consumer protection issues related to LTCI.2

 That work is ongoing, but no 

proposals or details of the Task Force’s work have been published. In addition, there is a federal 

interagency task force convened by the Treasury Department to examine issues and problems 

with the LTCI market. The Task Force is expected to produce a report this year, but it has not 

yet been published. 

Given the major pending activities of the NAIC and the federal interagency task force on 

LTCI, the subcommittee on availability will wait to further discuss LTCI until work products of 

the NAIC and the interagency Task Force are available. 

Disparate Impact 

Disparate impact refers to policies, practices and outcomes that have the effect of 

discriminating against protected classes. Disparate impact refers to a different type of unfair 

discrimination from disparate treatment. Disparate treatment means discriminating directly on 

the basis of prohibited characteristics, while disparate impact, also known as disparate effect, 

refers to discrimination based on practices that have the effect of discriminating on the basis of 

prohibited characteristics. 

1

 The draft minutes of the August 019 call are attached. 

2 A recent update on the NAIC LTC TF is attached. 
 

 

 

FACI Subcommittee on Availability Background for November 8, 2018 Call 

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While a controversial issue in financial services regulation, disparate impact has been 

recognized as a form of prohibited unfair discrimination by numerous courts, including the U.S. 

Supreme Court in a 2015 decision. Justice Kennedy wrote 

Recognition of disparate-impact claims is also consistent with the central purpose of the 

FHA, which, like Title VII and the ADEA, was enacted to eradicate discriminatory 

practices within a sector of the Nation’s economy. Suits targeting unlawful zoning laws 

and other housing restrictions that unfairly exclude minorities from certain 

neighborhoods without sufficient justification are at the heartland of disparate-impact 

liability. See, e.g., Huntington v. Huntington Branch, NAACP, 488 U. S. 15, 16–18. 

Recognition of disparate-impact liability under the FHA plays an important role in 

uncovering discriminatory intent: it permits plaintiffs to counteract unconscious 

prejudices and disguised animus that escape easy classification as disparate treatment. 

The Court holds that disparate-impact claims are cognizable under the Fair Housing Act 

upon considering its results-oriented language, the Court’s interpretation of similar 

language in Title VII and the ADEA, Congress’ ratification of disparate-impact claims in 

1988 against the backdrop of the unanimous view of nine Courts of Appeals, and the 

statutory purpose. 

The Supreme Court case dealt with a disparate impact claim under the federal Fair 

Housing Act which prohibits discrimination in housing on the basis on the basis of race, color, 

religion, sex, familial status, or national origin. In addition to recognizing disparate impact as a 

type of unfair discrimination covered by the FHA, courts have also recognized that unfair 

discrimination in home insurance – whether disparate treatment or disparate impact – is also 

covered by the FHA. 

Financial institutions have long sought to eliminate disparate impact as a form of unfair 

discrimination. Insurers have argued that disparate impact is particularly unsuited to insurance, 

that recognizing disparate impact as unfair discrimination would undermine risk-based pricing 

and the state regulatory system and would lead to massive amounts of litigation. 

For purposes of subcommittee discussion, the following are some arguments for the statebased insurance regulatory system to recognize disparate impact as unfair discrimination. 

1. If discriminating intentionally on the basis of prohibited classes is prohibited – e.g., 

insurers are prohibited from using race, religion or national origin as underwriting, 

tier placement or rating factors – why would practices that have the same effect be 

permitted? 
FACI Subcommittee on Availability Background for November 8, 2018 Call 

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Example of Disparate Impact in insurance: In the 1990’s, fair housing groups brought a 

disparate impact challenge against insurers’ use of age and value of the home for underwriting. 

The groups argued that these underwriting guidelines discriminated against minority 

communities because these communities’ housing was characterized by low value and old age. 

The challenges were largely successful and, in response, insurers developed more detailed 

underwriting based on, for example, age and type of electrical system and age and condition of 

the roof. 

2. In an era of big data analytics, the potential for proxy discrimination has grown 

dramatically. 

Barocas and Selbst: Big Data’s Disparate Impact 

Advocates of algorithmic techniques like data mining argue that they eliminate human 

biases from the decision-making process. But an algorithm is only as good as the data it 

works with. Data mining can inherit the prejudices of prior decision-makers or reflect the 

widespread biases that persist in society at large. Often, the “patterns” it discovers are 

simply preexisting societal patterns of inequality and exclusion. Unthinking reliance on 

data mining can deny members of vulnerable groups full participation in society. 

TransUnion Criminal History Scores 

“TransUnion recently evaluated the predictive power of court record violation data 

(including criminal and traffic violations) 

“Also, as court records are created when the initial citation is issued, they provide insight 

into violations beyond those that ultimately end up on the MVR—such as violation 

dismissals, violation downgrades, and pre-adjudicated or open tickets.” 

What is the likelihood that TU Criminal History Scores have a disparate impact against AfricanAmericans? Consider policing records in Ferguson, Missouri. 

US DOJ Investigation of the Ferguson Police Department 

Ferguson’s approach to law enforcement both reflects and reinforces racial bias, 

including stereotyping. The harms of Ferguson’s police and court practices are borne 

disproportionately by African Americans, and there is evidence that this is due in part to 

intentional discrimination on the basis of race. 

Ferguson’s law enforcement practices overwhelmingly impact African Americans. Data 

collected by the Ferguson Police Department from 2012 to 2014 shows that African 

Americans account for 85% of vehicle stops, 90% of citations, and 93% of arrests made 

by FPD officers, despite comprising only 67% of Ferguson’s population. 
 

b0 + b1X1 + b2X2 + b3X3 + e = y 

X1, X2 + X3 

 

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FPD appears to bring certain offenses almost exclusively against African Americans. For 

example, from 2011 to 2013, African Americans accounted for 95% of Manner of 

Walking in Roadway charges, and 94% of all Failure to Comply charges. 

Our investigation indicates that this disproportionate burden on African Americans 

cannot be explained by any difference in the rate at which people of different races 

violate the law. Rather, our investigation has revealed that these disparities occur, at least 

in part, because of unlawful bias against and stereotypes about African Americans 

3. Disparate Impact is Particularly Suited to Insurance: Disparate Impact Analysis is 

Consistent with State Regulatory Requirements Regarding Unfair Discrimination and 

with Actuarial Justification Used by Insurers? 

State insurance laws and regulation typically require prohibit rates and other practices 

that are unfairly discriminatory. For pricing (underwriting, tier placement, rating factors), unfair 

discrimination is generally understood as a statistical or actuarial measure – consumers of similar 

risk and hazard are treated differently. 

Actuarial justification is a statistical test – that a particular characteristic of the consumer, 

vehicle, property or environment is correlated with a particular outcome, like pure premium 

(average claim cost). The same statistical test can be used to evaluate and minimize disparate 

impact. Stated differently – if a particular correlation and statistical significance is used to 

justify, say, insurance credit scoring, those same standards of correlation and statistical 

significance are reasonable evidence of disparate impact and unfair discrimination on the basis of 

prohibited factors. 

In addition, the ability of insurers to identify and minimize disparate impact can be easily 

built into the development of pricing, marketing or claim settlement models by including 

consideration of prohibited characteristics as control variables in the development of the model 

and then omitting these prohibited characteristics when the model is deployed. 

Consider the simple model 

Say that are miles driven, driving record and credit score and we are trying to 

predict y – the frequency of an auto claim. 

Let’s assume that all three Xs are statistically significant predictors of the likelihood of a 

claim and the b values are how much each X contributes to the explanation of claim. 

b0 is the “intercept” – a base amount and e is the error term – the portion of the 

explanation of the claim not provided by the independent variables. 
 

 

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Now, let’s add a control variable for race: 

b0 + b1X1 + b2X2 + b3X3 + b4R1+ e = y 

R1 is a control variable – by including race in the model development, the correlation of 

the Xs to race is statistically removed and the new b values are now the contribution of the Xs, 

independent of their correlation to race, to explaining the likelihood of a claim 

When the model is deployed, the variable for race is removed – the Xs remain, but the b 

values now minimize disparate impact. 

Recognizing disparate impact as unfair discrimination in insurance is both reasonable and 

beneficial: 

 Minimizes Disparate Impact – Stop the Cycle of Perpetuating Historical Discrimination. 

Promotes Availability and Affordability for Underserved Groups 

Improves Cost-Based Insurance Pricing Models 

Improve Price Signals to Insureds for Loss Mitigation Investments 

Help Identify Biases in Data and Modelers / Improve Data Insights 

 Improve Consumer Confidence of Fair Treatment by Insurers 

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For Subcommittee Discussion: 

 What are subcommittee members’ views of the arguments for recognizing disparate 

impact as unfair discrimination? 

What are the arguments against recognizing disparate impact as unfair discrimination and 

who might the subcommittee ask to present those views? 

How might disparate impact affect the current state-based regulatory system regarding 

unfair discrimination? 

What role should FIO play in the disparate impact debate and what can the subcommittee 

do to support FIO? 

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Insurance-Specific Principles for Artificial Intelligence and Digital Rights/Data Privacy 

There has been growing recognition by governments and regulatory agencies around the 

world that big data analytics (BDA) and artificial intelligence (AI) have the potential for great 

consumer and societal benefits, but AI and BDA practices also raise concern about consumers’ 

digital rights and the fair treatment of consumers. In response, a number of jurisdictions have 

developed or are in the process of developing principles for ethical AI as well as regulations and 

laws to ensure consumers’ digital rights and data privacy. Regarding principles for AI, the 

NAIC has created a new AI working group to develop such principles and has requested 

comments on the principles developed by the Organization for Economic Cooperation and 

Development (OECD) as a starting point for the NAIC’s work.3

 The OECD’s AI principles are: 

 The AI WG web page and materials are found at https://content.naic.org/cmte_ex_ai_wg.htm 3
 

Loss Prevention / Mitigation / Sustainability / Resilience: Enhance, not undermine the 

loss prevention potential of insurance 

Availability / Affordability – Address the protection gap for low- and moderate-income 

consumers and small businesses. Most important tool for individual, business and 

community recovery and resilience. 

Digital Rights – consumer ownership and consent to identified uses, protection of 

consumer data, contestability, disclosure of and remediation following data breaches 

Regulatory and Legal Compliance – compliance with the letter of and the intent of the 

law 

FACI Subcommittee on Availability Background for November 8, 2018 Call 

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 AI should benefit people and the planet by driving inclusive growth, sustainable 

development and well-being. 

 AI systems should be designed in a way that respects the rule of law, human rights, 

democratic values and diversity, and they should include appropriate safeguards – for 

example, enabling human intervention where necessary – to ensure a fair and just society. 

 There should be transparency and responsible disclosure around AI systems to ensure that 

people understand AI-based outcomes and can challenge them. 

AI systems must function in a robust, secure and safe way throughout their life cycles and 

potential risks should be continually assessed and managed. 

Organisations and individuals developing, deploying or operating AI systems should be 

held accountable for their proper functioning in line with the above principles. 

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In addition, there has been considerable activity in the U.S. and internationally regarding 

digital rights and data privacy, including the European Union General Data Protection 

Regulation and the California Consumer Privacy Act. 

For purposes of discussion by the subcommittee, are there insurance-specific issues with 

either principles for AI or digital rights / data privacy and should there be insurance-specific 

considerations? As an example, the Center for Economic Justice has proposed the following: 

 Cost-Based Pricing: Protect insurer financial condition, provide proper investment risk / 

mitigation benefit price signals, fair treatment of consumers entering into contracts of 

adhesion 

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Risk Pooling: Protect risk diversification, availability and affordability of insurance 

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Fair Competition – Antitrust enforcement for emerging types of collective pricing and 

claim settlement practices facilitated by big data algorithms 

Fair Competition – Empower consumers by more symmetric sharing of information 

between insurers and consumers 

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Transparency, Explainability and Accountability – ethical and accountable algorithms 

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