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Cl 2019 18

cl 2019 18

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Cl 2019 18 Video

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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.

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.

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 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.

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.

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 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:.

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.

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.

Transparency is an important consideration in the use of external data sources to underwrite life insurance. 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.

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 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.

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Visit Everton Club Page. Saturday 02 February BHA Visit Watford Club Page. Saturday 02 February CAR Visit Bournemouth Club Page. Visit Leicester City Club Page.

Visit Newcastle United Club Page. Saturday 02 February CRY Visit Crystal Palace Club Page. Saturday 02 February BUR Visit Southampton Club Page.

Visit Burnley Club Page. Visit Cardiff City Club Page. Visit Fulham Club Page. Visit Huddersfield Town Club Page. Division 1 This table charts the Premier League teams Position.

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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.

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 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.

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.

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 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:.

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.

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.

Transparency is an important consideration in the use of external data sources to underwrite life insurance. 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.

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 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.

Visit Watford Club Page. Saturday 02 February CAR Visit Bournemouth Club Page. Visit Leicester City Club Page. Visit Newcastle United Club Page.

Saturday 02 February CRY Visit Crystal Palace Club Page. Saturday 02 February BUR Visit Southampton Club Page.

Visit Burnley Club Page. Visit Cardiff City Club Page. Visit Fulham Club Page. Visit Huddersfield Town Club Page. Division 1 This table charts the Premier League teams Position.

Recent Result - Friday 6 May Recent Result - Monday 11 April Visit Sunderland Club Page. Recent Result - Monday 2 May Recent Result - Monday 9 May Recent Result - Thursday 12 May Recent Result - Monday 25 April Recent Result - Tuesday 19 April Visit Reading Club Page.

Visit Middlesbrough Club Page. Visit Norwich City Club Page. Division 2 This table charts the Premier League teams Position.

Visit Derby County Club Page. Recent Result - Monday 18 April Visit Swansea City Club Page. Visit Aston Villa Club Page.

2 Responses

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