A reporting-period timeline of the company’s AI and machine-learning disclosures, grounded in official investor materials and exact source excerpts.
Products and use casesPartners and vendorsRisk and governance
Use-case evidence
AI use cases in official disclosures
Each row is a distinct, evidence-linked use case or initiative identified in official results and investor materials. Expand a row’s evidence to inspect the exact source wording.
6 verified use cases
Newest disclosure first
Products and use casesContinuing
Ethos deploys generative and agentic AI across customer and back-office operations
Ethos utilizes artificial intelligence, machine learning, generative AI, and agentic AI to collect, aggregate, analyze, or generate data. These technologies are applied across customer-facing, operational, and back-office functions, specifically including customer support, lead targeting, agent fraud detection, and developer tooling.
·Quarterly report·SEC EDGAR
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“We utilize artificial intelligence, machine learning, and similar tools and technologies, including generative AI and agentic AI (collectively, “AI”) that collect, aggregate, analyze or generate data or other materials or content in connection with our business, including customer-facing, operational and back-office functions, such as customer support, lead targeting, agent fraud detection, and development tooling.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Ethos performs due diligence on third-party AI systems despite lack of direct control
The company relies on AI technologies developed and maintained by third parties, which may perform unexpectedly in complex enterprise environments. While Ethos conducts due diligence and imposes contractual information security requirements, it cannot directly control how these third-party systems are developed, trained, or maintained.
·Quarterly report·SEC EDGAR
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“Though we perform due diligence into third-party providers of AI technologies and, where appropriate, impose contractual requirements regarding their information security practices, we are not able to control the way third-party products or services are developed, trained or maintained.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Threat actors leverage advanced AI to launch highly automated cyber attacks
Ethos faces heightened security threats as malicious actors utilize advancing AI technologies to launch automated, targeted, and coordinated cyber attacks. These include social-engineering tactics such as deep fakes, which are increasingly difficult to identify as fake.
·Quarterly report·SEC EDGAR
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“Additionally, as AI technologies continue to advance, threat actors can leverage these technologies to develop more sophisticated attack methods that are increasingly automated, targeted, coordinated, and which may evolve faster than traditional security measures can adapt, making them more difficult to defend against.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Growing regulatory scrutiny on AI use in the insurance industry
State insurance regulators and the National Association of Insurance Commissioners are increasingly targeting the use of AI in offering and underwriting products. For instance, the Colorado Department of Insurance requires life insurers to implement frameworks to prevent unfair discrimination from predictive algorithms, and over 20 states have adopted bulletins describing AI governance expectations.
·Annual report·SEC EDGAR
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“These regulators are also increasingly focused on the use of AI in offering and underwriting consumer products.”
Machine learning deployed for purchase prediction and operational intelligence
Ethos integrates machine learning models to identify historical consumer cohort patterns, predict purchasing behavior, and forecast underwriting outcomes early in the funnel. The company also deploys machine learning for key operational tasks including fraud detection, agent quality scoring, marketing optimization, and policy audit prioritization.
·Annual report·SEC EDGAR
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“Our data engine and machine learning models identify patterns from historical consumer cohorts and use that data to predict purchase behavior, consumer lifetime values, and underwriting outcomes early in the funnel, improving our targeting while offering consumers an experience that is personalized to their needs.”
Context: Ethos Annual Report, Section: Distribution
AI technologies amplify cybersecurity and operational threat landscape
Ethos is exposed to evolving cybersecurity threats, noting that threat actors can leverage advancing AI technologies to execute more automated, targeted, and coordinated attacks. Additionally, AI systems are susceptible to cybersecurity threats due to the high volume of data they process.
·Annual report·SEC EDGAR
Read exact evidence
“Additionally, as AI technologies continue to advance, threat actors can leverage these technologies to develop more sophisticated attack methods that are increasingly automated, targeted, coordinated, and which may evolve faster than traditional security measures can adapt, making them more difficult to defend against.”
Ethos deploys generative and agentic AI across customer and back-office operations
Ethos utilizes artificial intelligence, machine learning, generative AI, and agentic AI to collect, aggregate, analyze, or generate data. These technologies are applied across customer-facing, operational, and back-office functions, specifically including customer support, lead targeting, agent fraud detection, and developer tooling.
Read exact evidence
“We utilize artificial intelligence, machine learning, and similar tools and technologies, including generative AI and agentic AI (collectively, “AI”) that collect, aggregate, analyze or generate data or other materials or content in connection with our business, including customer-facing, operational and back-office functions, such as customer support, lead targeting, agent fraud detection, and development tooling.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Ethos performs due diligence on third-party AI systems despite lack of direct control
The company relies on AI technologies developed and maintained by third parties, which may perform unexpectedly in complex enterprise environments. While Ethos conducts due diligence and imposes contractual information security requirements, it cannot directly control how these third-party systems are developed, trained, or maintained.
Read exact evidence
“Though we perform due diligence into third-party providers of AI technologies and, where appropriate, impose contractual requirements regarding their information security practices, we are not able to control the way third-party products or services are developed, trained or maintained.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Threat actors leverage advanced AI to launch highly automated cyber attacks
Ethos faces heightened security threats as malicious actors utilize advancing AI technologies to launch automated, targeted, and coordinated cyber attacks. These include social-engineering tactics such as deep fakes, which are increasingly difficult to identify as fake.
Read exact evidence
“Additionally, as AI technologies continue to advance, threat actors can leverage these technologies to develop more sophisticated attack methods that are increasingly automated, targeted, coordinated, and which may evolve faster than traditional security measures can adapt, making them more difficult to defend against.”
Context: Section: Risks Related to Intellectual Property, Artificial Intelligence, Data Privacy, and Security
Growing regulatory scrutiny on AI use in the insurance industry
State insurance regulators and the National Association of Insurance Commissioners are increasingly targeting the use of AI in offering and underwriting products. For instance, the Colorado Department of Insurance requires life insurers to implement frameworks to prevent unfair discrimination from predictive algorithms, and over 20 states have adopted bulletins describing AI governance expectations.
Read exact evidence
“These regulators are also increasingly focused on the use of AI in offering and underwriting consumer products.”
Machine learning deployed for purchase prediction and operational intelligence
Ethos integrates machine learning models to identify historical consumer cohort patterns, predict purchasing behavior, and forecast underwriting outcomes early in the funnel. The company also deploys machine learning for key operational tasks including fraud detection, agent quality scoring, marketing optimization, and policy audit prioritization.
Read exact evidence
“Our data engine and machine learning models identify patterns from historical consumer cohorts and use that data to predict purchase behavior, consumer lifetime values, and underwriting outcomes early in the funnel, improving our targeting while offering consumers an experience that is personalized to their needs.”
Context: Ethos Annual Report, Section: Distribution
AI technologies amplify cybersecurity and operational threat landscape
Ethos is exposed to evolving cybersecurity threats, noting that threat actors can leverage advancing AI technologies to execute more automated, targeted, and coordinated attacks. Additionally, AI systems are susceptible to cybersecurity threats due to the high volume of data they process.
Read exact evidence
“Additionally, as AI technologies continue to advance, threat actors can leverage these technologies to develop more sophisticated attack methods that are increasingly automated, targeted, coordinated, and which may evolve faster than traditional security measures can adapt, making them more difficult to defend against.”