Fortunately, there is a solution to the dilemma just posed. It consists in adopting a pricing strategy that substantially alters the sensitivity of a firm’s total economic value to changes in interest rates. In the example give earlier, where a = 15% and b = 0, the duration of the firm’s franchise value and total economic value are 17.62 and 6.70, respectively. But suppose we alter the firm’s pricing policy by changing these parameters to a = 10% and b = 1. In this case the target return on surplus remains at 15% (given that the risk-free yield remains at 5%), but the durations change from 17.62 to 7.62 for franchise value, and from 6.70 to 3.27 for total economic value. The key insight here is that a firm’s pricing strategy can significantly affect the duration of its franchise value and, consequently, the duration of its total economic value.
This insight suggests a more systematic approach to managing the duration of total economic value: find a combination of the strategy parameters a and b such that the return on surplus and the duration of total economic value are both acceptable. This can be done either by systematic numerical search or by constrained optimization procedures. For example, if the firm in our example wanted a target return on equity of 15% but a total economic value with a duration of zero, it should implement a pricing strategy with the parameters a = 6.2% and b = 1.763 to achieve those objectives. The consequences of this and the two previously mentioned pricing strategies are shown in Figure 3 for the three different pricing strategies just described.
Author(s): William H. Panning
Publication Date: 2006
Publication Site: Casualty Actuarial Society (for exams)
Unfair Discrimination without Disproportionate Impact. As previously defined, unfair discrimination occurs when rating variables that have no relationship to expected loss are used. A hypothetical example could be if an insurer decided to use rating factors that charged those with red cars higher rates, even if the data did not show this. In this case, there would be no disproportionate impact, assuming protected classes do not own a large majority of red cars. Disparate Treatment. Disparate treatment and unfair discrimination are not directly related if we use the Fair Trade Act definition of unfair discrimination. However, in states where rating on protected class is defined to be unfair discrimination, disparate treatment would be a subset of unfair discrimination. In such cases, an insurer would explicitly use protected class to charge higher rates, with the intention of prejudicing against that class. Intentional Proxy Discrimination. If proxy discrimination is defined to require intent, it would be a subset of disparate treatment, whereby an insurer would deliberately substitute a facially neutral variable for protected class for the purpose of discrimination. Redlining is an example of this type of discrimination, given the use of location characteristics as proxies for race and social class. Disproportionate Impact. Disproportionate impact focuses on effect on protected class, even if there is a relationship to expected loss. An example of this is the one mentioned in the AAA study, whereby a rating plan that uses age could disproportionately impact a minority group if those in that minority group tend to have higher risk ages. This disproportionate impact is not necessarily the same as proxy discrimination, since it is likely that even after controlling for minority status, age would have a relationship to expected costs.
Unintentional Proxy Discrimination. If proxy discrimination is defined to be unintentional, the focus is more on disproportionate outcomes and the variables used to substitute for protected class. Several variables are being investigated by regulators to potentially be proxy discrimination and include criminal history for auto insurance rating. In order to prove proxy discrimination, an analysis would have to be performed to understand the extent to which criminal history proxies for minority status, and whether its predictive power would decrease when controlling for protected class. It is important to note once again that terms like “unintentional proxy discrimination” may be subsumed by “disparate impact,” but they are included in this paper to show how various stakeholders use the term differently. Disparate Impact. Disparate impact is unintentional discrimination, where there is disproportionate impact, but also other legal requirements, such as the existence of alternatives. To date, no disparate impact lawsuits against insurance companies have been won. An example of potential disparate impact (although it was not litigated as a lawsuit) is from health care. Optum used an algorithm to identify and allocate additional care to patients with complex healthcare needs. The algorithm was designed to create a risk score for each patient during the enrollment period. Patients above the 97th percentile were automatically enrolled in the program and thus allocated additional care. Upon an independent peer review of the model, researchers found that the model was in fact allocating artificially lower scores to Black patients, even though the model did not use race. The reason behind this was the model’s use of prior healthcare costs as an input. Black patients typically spend less than white patients on health care, which artificially allocated better health to Black patients.18 Unfair Discrimination and Disproportionate Impact. In this case, an insurer would use a variable that both has no relationship to expected loss, but also has an outsized effect on protected classes. An example of this could be the same red car case above, but where protected classes also owned almost all the red cars. In this case, higher rates would create a disproportionate effect on protected classes, while also having no relationship to expected loss.
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.
In a groundbreaking TED-style talk, Dominic Lee, ACAS takes the audience on a multisensory journey beyond the boundaries of traditional insurance. He presents a framework for the actuarial profession to step into the future and claim its rightful place as a dominant force in the world of risk: Reimagine, Embrace and Explore.
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.
