Insurance Circular Letter No. 1 (2019)

January 18, 2019

TO:	All Insurers Authorized to Write Life Insurance in New York State
RE:	Use of External Consumer Data and Information Sources in Underwriting for Life Insurance

I. Summary

The purpose of this circular letter is to advise insurers authorized to write life insurance in New York of their statutory obligations regarding the use of external consumer data and information sources in underwriting for life insurance.

II. Discussion

Following reports of the emergence of unconventional sources or types of external data available to insurers, including within algorithms and predictive models, the New York State Department of Financial Services (“Department”) commenced an investigation of insurers’ underwriting guidelines and practices in New York related to the use of external data in underwriting for life insurance.

For purposes of this Circular Letter, external data includes any data or information sources not directly related to the medical condition of the applicant that is used – in whole or in part – to supplement traditional medical underwriting, as a proxy for traditional medical underwriting, or to establish “lifestyle indicators” that may contribute to an underwriting assessment of an applicant for life insurance coverage. For the purposes of this Circular Letter, external data sources do not include an MIB Group, Inc. member information exchange service, a motor vehicle report, or a criminal history search (see footnote 1).

The Department fully supports innovation and the use of technology to improve access to financial services. Indeed, insurers’ use of external data sources has the potential to benefit insurers and consumers alike by simplifying and expediting life insurance sales and underwriting processes. External data sources also have the potential to result in more accurate underwriting and pricing of life insurance. At the same time, however, the accuracy and reliability of external data sources can vary greatly, and many external data sources are companies that are not subject to regulatory oversight and consumer protections, which raises significant concerns about the potential negative impact on consumers, insurers and the life insurance marketplace in New York.

This circular letter addresses two particular areas of immediate concern with the use of external data sources, algorithms or predictive models that were identified during the Department’s investigation. First, the use of external data sources, algorithms, and predictive models has a significant potential negative impact on the availability and affordability of life insurance for protected classes of consumers. An insurer should not use an external data source, algorithm or predictive model for underwriting or rating purposes unless the insurer can establish that the data source does not use and is not based in any way on race, color, creed, national origin, status as a victim of domestic violence, past lawful travel, or sexual orientation in any manner, or any other protected class. Moreover, an insurer should also not use an external data source for underwriting or rating purposes unless the use of the external data source is not unfairly discriminatory and complies with all other requirements in the Insurance Law and Insurance Regulations. Second, the use of external data sources is often accompanied by a lack of transparency for consumers. Where an insurer is using external data sources or predictive models, the reason or reasons for any declination, limitation, rate differential or other adverse underwriting decision provided to the insured or potential insured should include details about all information upon which the insurer based such decision, including the specific source of the information upon which the insurer based its adverse underwriting decision.

It is important to note that this circular letter is not intended to provide an all-inclusive list of potential issues that could arise from the use of external data sources (including for both life and other kinds of insurance), nor is it intended to suggest that an insurer’s due diligence in assessing an external data source should be limited to the above two concerns.

A. Unlawful Discrimination

The N.Y. Insurance Law, Executive Law, General Business Law, and federal Civil Rights Act, protect against discrimination for certain classes of individuals. These laws govern the activities of insurers, including the ability of insurers to underwrite based on certain criteria. For example, Insurance Law Article 26 prohibits the use of race, color, creed, national origin, status as a victim of domestic violence, or past lawful travel in any manner, among other things, in underwriting. In addition, Insurance Law §§ 4224(a)(2) and (b)(2) prohibit insurers from refusing to insure or continuing to insure, limiting the amount, extent or kind of coverage, or charging a different rate for the same coverage solely because of the physical or mental disability, impairment or disease, or prior history of the disability or disease of an insured or potential insured except where the refusal, limitation or rate differential is permitted by law or regulation and is based on sound actuarial principles or is related to actual or reasonably anticipated experience. Insurers are responsible for complying with these anti-discrimination laws irrespective of whether they themselves are collecting data and directly underwriting consumers, or relying on external data sources, algorithms of external vendors or predictive models that are intended to be partial or full substitutes for direct underwriting. In short, an insurer may not use an external data source to collect or use information that the insurer would otherwise be prohibited from collecting or using directly.

Based on its investigation, the Department has determined that insurers’ use of external data sources in underwriting has the strong potential to mask the forms of discrimination prohibited by these laws. Many of these external data sources use geographical data (including community-level mortality, addiction or smoking data), homeownership data, credit information, educational attainment, licensures, civil judgments and court records, which all have the potential to reflect disguised and illegal race-based underwriting that violates Articles 26 and 42.

Other models and algorithms purport to make predictions about a consumer’s health status based on the consumer’s retail purchase history; social media, internet or mobile activity; geographic location tracking; the condition or type of an applicant’s electronic devices (and any systems or applications operating thereon); or based on how the consumer appears in a photograph. At the very least, the use of these models may either lack a sufficient rationale or actuarial basis and may also have a strong potential to have a disparate impact on the protected classes identified in New York and federal law.

In light of the Department’s investigation and findings, the Department is providing the following principles that insurers should use as guidance in using external data sources in underwriting.

First, an insurer should not use an external data source, algorithm or predictive model in underwriting or rating unless the insurer has determined that the external tools or data sources do not collect or utilize prohibited criteria. An insurer may not simply rely on a vendor’s claim of non-discrimination or the proprietary nature of a third-party process as a justification for a failure to independently determine compliance with anti-discrimination laws. The burden remains with the insurer at all times.

Second, an insurer should not use an external data source, algorithm or predictive model in underwriting or rating unless the insurer can establish that the underwriting or rating guidelines are not unfairly discriminatory in violation of Articles 26 and 42. In evaluating whether an underwriting or rating guideline derived from external data sources or information is unfairly discriminatory, an insurer should consider the following questions:

(1) Is the underwriting or rating guideline that is derived, in whole or in part, from external data sources or information supported by generally accepted actuarial principles or actual or reasonably anticipated experience that justifies different results for similarly situated applicants?

(2) Is there a valid explanation or rationale for the differential treatment of similarly situated applicants reflected by the underwriting or rating guideline that is derived, in whole or in part, from external data sources or information?

Importantly, even if statistical data is interpreted to support an underwriting or rating guideline, there must still be a valid rationale or explanation supporting the differential treatment of otherwise like risks. The second part of this inquiry is particularly important where there is no demonstrable causal link between the classification and increased mortality and also where an underwriting or rating guideline has a disparate impact on protected classes.

Data, algorithms, and models that purport to predict health status based on a single or limited number of unconventional criteria also raise significant concerns about the validity of such models.

An insurer may establish guidelines and practices to assess an applicant’s health status and identify individuals at higher mortality risk if based on sound actuarial principles or if related to actual or reasonably anticipated experience. However, the data, algorithms, and predictive modeling used by the insurer must comport with the principles set forth above and all other relevant requirements in federal and New York law. An insurer may not rely on external data or external predictive algorithms or models unless the insurer has determined that the external data or predictive model is otherwise permitted by law or regulation and is based on both sound actuarial principles or experience and a valid explanation or rationale.

B. Consumer Disclosure/Transparency

Transparency is an important consideration in the use of external data sources to underwrite life insurance. Pursuant to Insurance Law § 4224(a)(2), insurers must notify the insured or potential insured of the right to receive the specific reason or reasons for a declination, limitation, rate differential or other adverse underwriting decision. An adverse underwriting decision would include the inability of an applicant to utilize an expedited, accelerated or algorithmic underwriting process in lieu of a traditional medical underwriting. Where an insurer is using external data sources or predictive models, the reason or reasons provided to the insured or potential insured must include details about all information upon which the insurer based any declination, limitation, rate differential or other adverse underwriting decision, including the specific source of the information upon which the insurer based its adverse underwriting decision. An insurer may not rely on the proprietary nature of a third-party vendor’s algorithmic processes to justify the lack of specificity related to an adverse underwriting action. Insurers must also provide notice to and obtain consent from consumers to access external data, where required by law or regulation. The failure to adequately disclose the material elements of an accelerated or algorithmic underwriting process, and the external data sources upon which it relies, to a consumer may constitute an unfair trade practice under Insurance Law Article 24.

III. Conclusion

The Department supports efforts to improve the effectiveness and timeliness of insurance underwriting decisions in order to provide consumers with increased access to financial services consistently with law. Accordingly, an insurer should not use external data sources, algorithms or predictive models in underwriting or rating unless the insurer has determined that the processes do not collect or utilize prohibited criteria and that the use of the external data sources, algorithms or predictive models are not unfairly discriminatory. The insurer must establish that the external data sources, algorithms or predictive models are based on sound actuarial principles with a valid explanation or rationale for any claimed correlation or causal connection. An insurer must also disclose to consumers the content and source of any external data upon which the insurer has based an adverse underwriting decision.

The Department reserves the right to audit and examine an insurer’s underwriting criteria, programs, algorithms, and models, including within the scope of regular market conduct examinations, and to take disciplinary action, including fines, revocation and suspension of license, and the withdrawal of product forms.

Please direct any questions regarding this circular letter to: Peter Dumar, Chief Insurance Attorney, Life Bureau, New York State Department of Financial Services, One Commerce Plaza, Albany, New York 12257 or by email at [email protected].

Sincerely

James Regalbuto
Deputy Superintendent - Life Insurance

^1. Criminal history only includes past convictions or pending criminal matters. It does not include prior arrests, pleas or imprisonment for which an individual was not convicted of any crime; or civil dispute history such as appearances in housing court, civil litigation, liens, bankruptcy, etc. See Executive Law § 296(16). Criminal history does include being sanctioned by the U.S. Government (or any agency thereof), or by any international organization in which the U.S. Government (or any agency thereof) is a member, for money laundering, terrorism, trafficking, etc.