The five biggest auto insurers in Illinois have raised automobile insurance rates a whopping $527 million since January, an analysis by two consumer groups shows.
That follows about $1.1 billion in rate increases last year by the top 10 Illinois car insurers.
The analysis by the nonprofit Illinois Public Interest Research Group and Consumer Federation of America looked at auto insurance rate increases by the five largest companies in Illinois: State Farm, Allstate, Progressive, Geico and Country Financial, which together make up 62% of the Illinois market.
Now, state Rep. Will Guzzardi, D-Chicago, has introduced legislation to address those issues and crack down on insurers. Guzzardi’s bill would:
Require automobile insurers to get prior state approval for rate hikes.
Ban “excessive” insurance increases.
Prohibit using gender, marital status, age, occupation, schooling, home ownership, wealth, credit scores or a customer’s past insurance company relationships in setting car insurance rates.
It’s already illegal to use race, ethnicity and religion in setting rates. That would continue under Guzzardi’s proposal.
Author(s): Stephanie Zimmermann | Chicago Sun-Times
Narrow or prejudiced thinking is simple to write down and easy to copy and paste over and over. Descriptions such as “difficult” and “disruptive” can become hard to escape. Once so labeled, patients can experience “downstream effects,” said Dr. Hardeep Singh, an expert in misdiagnosis who works at the Michael E. DeBakey Veterans Affairs Medical Center in Houston. He estimates misdiagnosis affects 12 million patients a year.
Conveying bias can be as simple as a pair of quotation marks. One team of researchers found that Black patients, in particular, were quoted in their records more frequently than other patients when physicians were characterizing their symptoms or health issues. The quotation mark patterns detected by researchers could be a sign of disrespect, used to communicate irony or sarcasm to future clinical readers. Among the types of phrases the researchers spotlighted were colloquial language or statements made in Black or ethnic slang.
“Black patients may be subject to systematic bias in physicians’ perceptions of their credibility,” the authors of the paper wrote.
That’s just one study in an incoming tide focused on the variations in the language that clinicians use to describe patients of different races and genders. In many ways, the research is just catching up to what patients and doctors knew already, that discrimination can be conveyed and furthered by partial accounts.
Why is the insurance industry now facing increased scrutiny on certain underwriting methods?
Insurers increasingly are turning to nontraditional data sets, sources and scores. The methods used to obtain traditional data—that were at one time costly and time-consuming—can now be done quickly and cheaply.
As insurers continue to innovate their underwriting techniques, increased scrutiny should be expected. It is not unreasonable for consumer advocates to push for increased transparency and explainability when insurers employ these advanced methods.
What is the latest regulatory activity on this topic in the various states and at the NAIC?
Activity in the states has been minimal. In 2021, Colorado became the first (and so far, only) state to enact legislation requiring insurers to test their algorithms for bias. Legislation nearly identical to the Colorado law was introduced in Oklahoma and Rhode Island in 2022, and it is likely other states will consider similar legislation. Connecticut is finalizing guidance that would require insurers to attest that their use of data is nondiscriminatory. Other states have targeted specific factors, but most have adopted a wait-and-see approach.
The NAIC created a new high-level committee to focus on innovation and AI, but it has become clear that a national standard is not likely at this time.
Author(s): INTERVIEW BY STEPHEN ABROKWAH, Interview with Neil Sprackling, president of Swiss Re Life & Health America Inc.
Arlington, VA – Two newresearch reports designed to guide the insurance industry toward proactive, quantitative solutions to identify, measure and address potential racial bias in insurance pricing were published by the Casualty Actuarial Society (CAS) today.
“These two new reports in our CAS Research Series on Race and Insurance Pricing continue to provide additional insight into industry discussions on this topic,” said Victor Carter-Bey, DM, CAS chief executive officer. “We hope with this series to serve as a thought leader and role model for other insurance organizations and corporations in promoting fairness and progress.”
As the professional society of actuaries specializing in property and casualty insurance, the CAS is committed to diversity, equity and inclusion in actuarial work. To this end, the Society is releasing a series of four CAS Research Papers, which support the CAS’s Approach to Race and Insurance Pricing. This approach was adopted by the CAS Board of Directors in December 2020 and includes four key areas of focus and goals: basic and continuing education, research, leadership and influence, and collaboration. Each paper in the series addresses a different aspect of race and insurance pricing as viewed through the lens of property and casualty insurance.
Defining Discrimination in Insurance. This report examines terms that are being used in discussions around potential discrimination in insurance, including protected class, unfair discrimination, proxy discrimination, disparate impact, disparate treatment, and disproportionate impact. The paper provides historical and practical context for these terms and illustrates the inconsistencies in how different stakeholders define them. It also describes the potential impacts of these definitions on actuarial work.
Understanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Explored. The paper examines four commonly used rating factors to understand how the data underlying insurance pricing models may be impacted by racially biased policies and practices outside of insurance. The goal is to highlight the multi-dimensional impacts of systemic racial bias, as it may relate to insurance pricing. The four factors included in the report are: Credit-Based Insurance Score (CBIS), geographic location, homeownership and Motor Vehicle Records.
These four research reports are just one way the CAS supports evolving actuarial practices and strengthens the knowledge of its members. The papers demonstrate the Society’s recognition that actuaries—who are responsible for setting insurance rates—must be a voice in an ever-evolving dialogue. The CAS understands that this work is critical to maintaining the Society and its members’ public trust.
THE ACADEMY hosted a May 26 webinar, “What Is Unfair Discrimination in Insurance?” in which presenters explored the current regulatory infrastructure relating to unfair and unlawful discrimination in insurance and the challenges presented by the increased use of big data and artificial Intelligence (AI)-enabled systems.
Presenters were Daniel Schwarcz, an award-winning professor and scholar; former Illinois Director of Insurance Nat Shapo; and Brian Mullen, chairperson of the task force currently revising ASOP No. 12, Risk Classification ( for All Practice Areas). General Counsel and Director of Professionalism Brian Jackson moderated.
Mullen opened by providing background on ASOP No. 12. Schwarcz discussed prohibitions on “unfair discrimination”—which occurs when an insurer considers factors unrelated to actuarial risk—in rates and underwriting. He noted that machine learning AI tends to produce the same results as intentional proxy discrimination. As a result, insurance becomes less available and less affordable to individuals because of their race, sex, genetics, health, or income. He also discussed a proposed definition of proxy discrimination, practical tests for proxy discrimination, and the benefits of such a definition.
The goal of this paper is to equip actuaries to proactively participate in discussions and actions related to potential racial biases in insurance practices. This paper uses the following definition of racial bias: Racial bias refers to a system that is inherently skewed along racial lines. Racial bias can be intentional or unintentional and can be present in the inputs, design, implementation, interpretation or outcomes of any system. To support actuaries and the insurance industry in these efforts, this paper examines issues of racial bias that have impacted four areas of noninsurance financial services — mortgage lending, personal lending, commercial lending and the underlying credit-scoring systems — as well as the solutions that have been implemented in these sectors to address this bias. Actuaries are encouraged to combine this information on solutions and gaps in other industries with expertise in their practice areas to determine how, if at all, this information could be applied to identify potential racial biases impacting insurance or other industries in which actuaries work. Parallels can be drawn between the issues noted here in financial services and those being discussed within the insurance industry. While many states have long considered race to be a protected class which cannot be used for insurance business decisions, regulators and consumer groups have brought forth concerns about potential racial bias implicit in existing practices or apparent in insurance outcomes. State regulators are taking individual actions to address potential issues through prohibition of certain rating factors, and even some insurers are proactively calling for the industry to move away from using information thought to be correlated with race. However, this research suggests that government prohibition of specific practices may not be a silver-bullet solution. Actuaries can play a key role as the insurance industry develops approaches to test for, measure and address potential racial bias, and increase fairness and equality in insurance, while still maintaining riskbased pricing, company competitiveness and solvency.
Author(s): Members of the 2021 CAS Race and Insurance Research Task Force
Insurance rating characteristics have come under scrutiny by legislators and regulators in their efforts to identify and address racial bias in insurance practices. The goal of this paper is to equip actuaries with the information needed to proactively participate in industry discussions and actions related to racial bias and insurance rating factors. This paper uses the following definition of racial bias: Racial bias refers to a system that is inherently skewed along racial lines. Racial bias can be intentional or unintentional and can be present in the inputs, design, implementation, interpretation, or outcomes of any system. This paper will examine four commonly used rating factors in personal lines insurance — credit-based insurance score, geographic location, home ownership, and motor vehicle records — to understand how the data underlying insurance pricing models may be impacted by racially biased policies and practices outside of the system of insurance. Historical issues like redlining and racial segregation, as well as inconsistent enforcement of policies and practices contribute to this potential bias. These historical issues do not necessarily change the validity of the actuarial approach of evaluating statistical correlation of rating factors to insurance loss overall. Differences in the way individual insurers build rating models may produce very different end results for customers. More data and analyses are needed to understand if and to what extent these specific issues of racial bias impact insurance outcomes. Actuaries and other readers can combine this information with their own subject matter expertise to determine if and how this could impact the systems for which they are responsible, and what actions, if any, could be taken as a result.
Author(s): Members of the 2021 CAS Race and Insurance Research Task Force
This research paper is designed to introduce various terms used in defining discrimination by stakeholders in the insurance industry (regulators, consumer advocacy groups, actuaries and insurers, etc.). The paper defines protected class, unfair discrimination, proxy discrimination, disproportionate impact, disparate treatment and disparate impact. Stakeholders are not always consistent in their definitions of these terms, and these inconsistencies are highlighted and illustrated in this paper. It is essential to elucidate key elements and attributes of certain terms as well as conflicting approaches to defining discrimination in insurance in order to move the industry discussion forward. While this paper does not make a judgment on the appropriateness of the definitions put forth, nor does it promulgate what the definitions should be, readers will be empowered to understand the components of discrimination terms used in insurance, as well as be introduced to the potential implications for insurers. Actuaries who have a strong foundational knowledge of these terms are likely to play a key role in informing those who define and refine these terms for insurance purposes in the future. This paper is not a legal review, and thus discusses terms and concepts as they are used by insurance stakeholders, rather than what their ultimate legal definition will be. However, it is important for actuaries to understand the point of view of various stakeholders, and the potential impact it could have on actuarial work. As the regulatory and legislative landscape continues to shift, this brief should be considered a living document, that will periodically require update.
This research paper’s main objective is to inspire and generate discussions about algorithmic bias across all areas of insurance and to encourage actuaries to be involved. Evaluating financial risk involves the creation of functions that consider myriad characteristics of the insured. Companies utilize diverse statistical methods and techniques, from relatively simple regression to complex and opaque machine learning algorithms. It has been alleged that the predictions produced by these mathematical algorithms have discriminatory effects against certain groups of society, known as protected classes. The notion of discriminatory effects describes the disproportionately adverse effect algorithms and models could have on protected groups in society. As a result of the potential for discriminatory effects, the analytical processes followed by financial institutions for decision making have come under greater scrutiny by legislators, regulators, and consumer advocates. Interested parties want to know how to quantify such effects and potentially how to repair such systems if discriminatory effects have been detected.
This paper provides:
• A historical perspective of unfair discrimination in society and its impact on property and casualty insurance. • Specific examples of allegations of bias in insurance and how the various stakeholders, including regulators, legislators, consumer groups and insurance companies have reacted and responded to these allegations. • Some specific definitions of unfair discrimination and that are interpreted in the context of insurance predictive models. • A high-level description of some of the more common statistical metrics for bias detection that have been recently developed by the machine learning community, as well as a brief account of some machine learning algorithms that can help with mitigating bias in models.
This paper also presents a concrete example of an insurance pricing GLM model developed on anonymized French private passenger automobile data, which demonstrates how discriminatory effects can be measured and mitigated.
Author(s): Roosevelt Mosley, FCAS, and Radost Wenman, FCAS
The general assembly therefore declares that in order to ensure that all Colorado residents have fair and equitable access to insurance products, it is necessary to: (a) Prohibit: (I) Unfair discrimination based on race, color, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression in any insurance practice; and (II) The use of external consumer data and information sources, as well as algorithms and predictive models using external consumer data and information sources, which use has the result of unfairly discriminating based on race, color, national or ethnic origin, religion, sex, sexual orientation, disability, gender identity, or gender expression; and (b) After notice and rule-making by the commissioner of insurance, require insurers that use external consumer data and information sources, algorithms, and predictive models to control for, or otherwise demonstrate that such use does not result in, unfair discrimination.
In Israel, a vaccine passport was launched last week allowing those who are inoculated to go to hotels and gyms. Saudi Arabia now issues an app-based health passport for those inoculated, while Iceland’s government is doling out vaccine passports to facilitate foreign travel. Last month, President Biden issued executive orders asking government agencies to assess the feasibility of creating digital Covid-19 vaccination certificates.
Proponents of the plans say they will enable battered economies to reopen, even as vaccines are still being rolled out, allowing people to enjoy leisure activities and go to work safe in the knowledge they aren’t harming others or at risk themselves. It could also act as an incentive for people to get the shot.
The concept is potentially fraught with pitfalls. It could discriminate against minority communities, who are less likely to accept the vaccines, according to national surveys, or young people, who are less likely to be given priority to receive them.There are questions about the ethics of granting businesses access to peoples’ health records.
The Washington state Legislature, which has proposed legislation in the past to tackle issues such as data privacy and the use of facial recognition tech, is now reviewing a bill that would regulate the use of “automated decision systems” and AI technology within state government.
According to the bill, these systems use algorithms to analyze data to help make or support decisions that could result in discrimination against different groups or make decisions that could negatively impact constitutional or legal rights.
As a result, Senate Bill 5116 aims to regulate these systems to prevent discrimination and ban government agencies from using AI tech to profile individuals in public areas.