The Insurance coverage Business Must Align on Finest Practices

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This post is part of a series sponsored by TransUnion.

The attention of society and regulators has used fairness and equity as objectives to evaluate the results of existing processes such as insurance auditing. For example, a new law in Colorado, which will come into effect in early 2023, requires insurers to provide analytical evidence that their operational processes, which use consumer data and predictive models, do not unfairly discriminate against certain consumer groups. Credit-based insurance scores (hereinafter referred to as insurance risk scores) are an example of the inputs used in these operational processes

Insurance risk assessments have become essential for insurers as they seek to quickly and accurately draw policies and attract new business. But the relationship between credit report and insurance risk assessment is technical and complex. Most consumers are simply not aware that insurance risk assessments are used in purchasing insurance, and if incomplete information is given about it, they may be suspicious of their use.

This reality underscores two dimensions of fairness – the fairness of results and the consumers’ perception of fairness to these practices. These fairness issues are important and insurance companies need to demonstrate that their practices will not produce unfair results and appear fair to consumers.

Fairness tests – the need to be guided by best practices

Actuarial and predictive modeling are decades old and mature. The insurance industry has become very good at building empirically sound, demonstrably strong and stable models. In the insurance industry, however, fairness testing research and practice is still in its infancy, but is more robust in the academic field.

Much of the current focus is on race, ethnicity, and income; However, it is illegal for insurance companies and consumer credit reporting agencies to collect or store information about race and ethnicity, making it very difficult to analyze fairness and equity along these axes. Industry needs to evaluate options for capturing or estimating these characteristics.

Next, there needs to be a uniform definition of fair. From a data science and predictive modeling perspective, a fair outcome is an outcome where the predicted outcome matches actual outcomes based on a statistical measure of significance. On the other hand, some would say that fair means treating results equally across the population. As the industry works to define fair, both the variance in actual results and the population profile should be considered – a behavior-adjusted fair result.

Consumer perception of fairness

When it comes to consumer perception of fairness, Dr. Barbara Kiviat of Stanford University one of the most important academic researchers examining societal attitudes towards creditworthiness. In particular, she has worked out the concept of kinship in the use of credit ratings: Consumers either reject or reject the application of credit ratings to areas of their lives if they don’t see a clear link between the two. And many consumers and lawmakers right now do not see credit as something logically linked to insurance, leading them to view insurance risk assessments as unfair.

Dr. However, Kiviat points out, “When logically disjointed, morally heterogeneous data doesn’t seem so bad, if its use promises to expand the market to previously excluded people.” see, will they appreciate their role in expanding the market?

Another important result of Dr. Kiviat is that consumers are more likely to perceive a credit-based assessment as appropriate if they know it is not misleading risk. As TransUnion has shown with the precautions surrounding the CARES Act, insurance risk assessments can be adjusted in such a way that factors that are beyond the control of the consumer are not taken into account and yet remain stable and predictable.

An opportunity to raise awareness and educate consumers

Based on Dr. Kiviat’s research needs someone to have a clear theory of causality explaining why and how the rating system works in order for someone to stop using consumer data, such as: B. Insurance risk assessments. Insurers have an opportunity to convey a clearer understanding by taking a number of steps to raise awareness and educate consumers about the use of credit information in insurance, including:

  • How and why credit information is used
  • The benefits and opportunities it offers consumers
  • The protection and rights granted to consumers in the current proceeding

What could an awareness campaign about insurance risk scores look like in practice? TransUnion expressly recommends insurers:

  • Give consumers an explanation of what insurance risk ratings are, how they differ from financial credit ratings, and how insurers use them in combination with other variables to draw policies.
  • Explain to consumers why insurance risk assessments are used in underwriting, with an emphasis on the benefits to consumers.
  • Provide consumers with information about the protections and rules for assessing insurance risk, including the rights consumers have to access, challenge, and direct the use of their personal credit information.
  • Describe to consumers the credit behavior that can lead to an improvement in their scores. By providing consumers with this information, you can enable them to control and manage their personal credit history, which can result in better financial inclusion and lower costs.

Ultimately, insurers must represent their interests vis-à-vis local and national legislators. Teams working with insurance risk assessment products should work hand-in-hand with corporate government teams to identify potential trouble spots. Now is a good time to make your Government Relations colleagues aware of this issue and to make sure they are committed to your business.

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