Big Data, Big Discussions

Link: https://theactuarymagazine.org/big-data-big-discussions/

Excerpt:

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.

Publication Date: March 2022

Publication Site: The Actuary

CAS Releases Two Additional Papers in Race and Insurance Pricing Series

Link: https://www.casact.org/article/cas-releases-two-additional-papers-race-and-insurance-pricing-series

Excerpt:

Arlington, VA – Two new research 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.

Two of the four reports in the CAS Research Paper Series on Race and Insurance PricingUnderstanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Explored and Defining Discrimination in Insurance, are being released today. Here is a more detailed description of the two reports published today:

Defining Discrimination in InsuranceThis 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 ExploredThe 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.

The other two reports, Methods for Quantifying Discriminatory Effects on Protected Classes in Insuranceand Approaches to Address Racial Bias in Financial Services: Lessons for the Insurance Industry, were released March 10, 2022 during a virtual briefing.  

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.

Publication Date: 31 Mar 2022

Publication Site: CAS

Professionalism Webinar Examines Unfair
Discrimination in Insurance

Link: https://www.actuary.org/sites/default/files/2022-05/Actuarial-Update-May-2022.pdf

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Excerpt:

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.

Publication Date: May 2022

Publication Site: American Academy of Actuaries

APPROACHES TO ADDRESS RACIAL BIAS IN FINANCIAL SERVICES: LESSONS FOR THE INSURANCE INDUSTRY

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Approaches-to-Address-Racial-Bias_0.pdf?utm_source=Landing+Page&utm_medium=Website&utm_campaign=RIP+Series

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Excerpt:

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

Publication Date: March 2022

Publication Site: CAS

UNDERSTANDING POTENTIAL INFLUENCES OF RACIAL BIAS ON P&C INSURANCE: FOUR RATING FACTORS EXPLORED

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Understanding_Potential_Influences.pdf?utm_source=III&utm_medium=Issue+Brief&utm_campaign=RIP

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Excerpt:

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

Publication Date: March 2022

Publication Site: CAS

DEFINING DISCRIMINATION IN INSURANCE

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Defining_Discrimination_In_Insurance.pdf?utm_source=III&utm_medium=Issue+Brief&utm_campaign=RIP+Series

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Excerpt:

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.

Author(s): Kudakwashe F. Chibanda, FCAS

Publication Date: March 2022

Publication Site: CAS

METHODS FOR QUANTIFYING DISCRIMINATORY EFFECTS ON PROTECTED CLASSES IN INSURANCE

Link: https://www.casact.org/sites/default/files/2022-03/Research-Paper_Methods-for-Quantifying-Discriminatory-Effects.pdf

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Excerpt:

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

Publication Date: March 2022

Publication Site: CAS

Restrict Insurers’ Use Of External Consumer Data, Colorado Senate Bill 21-169

Link: https://leg.colorado.gov/sites/default/files/2021a_169_signed.pdf

Link: https://leg.colorado.gov/bills/sb21-169

Excerpt:

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.

Publication Date: 6 July 2021

Publication Site: Colorado Legislature

Covid-19 Vaccine ‘Passports’ Raise Ethics Concerns, Logistical Hurdles

Link: https://www.wsj.com/articles/covid-19-vaccine-passports-raise-ethics-concerns-logistical-hurdles-11614335403

Excerpt:

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.

Author(s): Max Colchester, Felicia Schwartz

Publication Date: 26 February 2021

Publication Site: Wall Street Journal

Lawmaker Proposes to Ban AI and Its Discriminatory Impact

Link: https://www.governing.com/security/Lawmaker-Proposes-to-Ban-AI-and-Its-Discriminatory-Impact.html

Excerpt:

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.

Author(s): KATYA MARURI, GOVERNMENT TECHNOLOGY

Publication Date: 26 February 2021

Publication Site: Governing