The Impact of AI & IoT in Insurance

However, today, data readiness and governance remain key challenges. Many insurers struggle with incomplete data, inconsistent governance, and outdated legacy systems—all of which are slowing down AI adoption.

To unlock AI’s full potential, insurers must modernize their data infrastructure, embrace open-source ecosystems, and invest in AI-driven analytics.

The Evolution of AI #

AI has evolved from basic automation to sophisticated machine learning (ML) and deep learning models, capable of predicting risks, personalizing policies, and automating claims assessments. These advancements will reshape underwriting, distribution, and claims management.

- Equisoft, LIMRA & UCT Data Readiness Study

AI Trends Shaping the Life Insurance Industry #

Surge of Data from Connected Devices #

According to IoT Analytics, the number of IoT-connected devices grew 13% in 2024, reaching 18.8 billion by year-end. This ongoing surge in connected devices is generating vast amounts of real-time behavioral data from:

• Wearable health monitors (heart rate, sleep patterns, activity levels).

• Smart home devices (security systems, fire and water damage sensors).

• Connected vehicles (telematics for real-time driving risk assessment).

This data explosion enables real-time underwriting and risk modeling, but insurers must improve their data integration capabilities to leverage it effectively. The proliferation of IoT and connected devices will enable insurers to understand their clients better, leading to more personalized pricing and faster risk assessment.

Open-Source and Data Ecosystems #

The future of insurance will be defined by collaborative data ecosystems, where public and private entities share insights to improve risk assessment, streamline operations, and enhance customer experiences. Open-source AI models and cross-industry data-sharing initiatives will accelerate innovation, allowing insurers to refine underwriting, fraud detection, and pricing strategies with broader, real-time data inputs.

By 2030, insurers will increasingly rely on shared data networks, enabling them to:

• Integrate diverse data sources from health providers, financial institutions, and consumer technology companies to develop more accurate risk models.

• Leverage AI-driven analytics trained on shared industry data, improving fraud detection, claims automation, and personalized policy pricing.

• Adopt dynamic, usage-based insurance (UBI) models, where policies continuously adjust based on real-time customer behavior rather than static risk profiles.

Open-source data protocols will serve as the foundation for AI-driven insurance, ensuring seamless, real-time data exchange across industries while maintaining regulatory compliance and data security. Insurers that embrace these open ecosystems will be positioned to respond faster to market changes, improve underwriting accuracy, and unlock new revenue opportunities.

Advances in Cognitive Technologies #

Cognitive technologies are advanced AI systems that simulate human intelligence—including learning, reasoning, problem-solving, and decision-making. These technologies, which include machine learning (ML), natural language processing (NLP), deep learning, and computer vision, enable insurers to analyze vast amounts of structured and unstructured data, automate decision-making, and improve customer interactions.

Unlike traditional rule-based AI, cognitive technologies continuously learn and improve over time, allowing insurers to transition from manual, time-consuming processes to automated, data-driven decision-making. In the life insurance industry, these innovations are reshaping underwriting, claims processing, and customer engagement, making insurance more personalized, efficient, and proactive.

How Cognitive Technologies are Transforming Life Insurance #

AI-Powered Underwriting & Risk Assessment #

Traditional underwriting involves extensive medical exam review, financial records analysis, and risk classification, often taking weeks to process. Cognitive AI is streamlining underwriting and reducing decision time by:

• Extracting insights from unstructured medical and financial data, including electronic health records (EHRs), prescription history, credit reports, and IoT-based health trackers.

• Replacing invasive medical exams for low-risk applicants with predictive analytics, allowing near-instant policy approvals.

• Continuously refining risk models using AI-driven insights from real-time health monitoring and lifestyle data, enabling dynamic pricing adjustments.

AI-Driven Claims Processing #

Claims processing has traditionally been manual and document-heavy, leading to delays and inefficiencies. AI-powered claims automation is addressing this by:

• Using NLP and machine learning to extract relevant data from medical reports, death certificates, and policy documents, significantly reducing manual processing time.

• Applying fraud detection algorithms to cross-check claim data with historical records, identifying anomalies and reducing fraudulent payouts.

• Automating claims triage, allowing straightforward claims to be approved instantly, while more complex cases are flagged for human review.

Enhancing Customer Engagement with AI Chatbots & Digital Assistants #

Life insurance policies can be complex, requiring extensive guidance and support. AI-powered chatbots and AI agents are transforming policyholder interactions by:

• Providing 24/7 customer support, answering policy inquiries, claims status updates, and premium adjustments.

• Recommending personalized policies by analyzing customer demographics, financial goals, and lifestyle risks.

• Enhancing policyholder retention by proactively reminding customers about renewals, beneficiary updates, and available discounts.

What are AI agents? #

An AI agent in life insurance is a more advanced application of artificial intelligence compared to basic chatbots. They represent a new stage of AI capability for both the public and for insurers.

While the terms "chatbot" and "agent" are sometimes used interchangeably, AI agents typically offer more sophisticated capabilities:

AI agents in life insurance can:

• Handle end-to-end processes autonomously, such as completing a full application or claim

• Make complex decisions using underwriting rules and risk assessment algorithms

• Proactively engage with customers based on life events or policy milestones

Personalized Risk Prevention & Wellness Incentives #

AI is not only improving claims and underwriting but also preventing risks before they occur. Insurers can:

• Integrate AI with wearables and health-tracking devices, encouraging policyholders to adopt healthier lifestyles in exchange for premium discounts.

• Analyze customer speech and behavioral patterns to detect early signs of potential health risks and suggest proactive interventions.

• Use predictive analytics to prevent policy lapses, identifying customers at risk of cancellation and offering retention incentives.

Challenges with Implementing AI for Life Insurers #

While AI is reshaping life insurance by improving risk assessment, underwriting, and customer interactions, regulatory, technological, and organizational barriers remain. Addressing these challenges is crucial for insurers to fully leverage AI’s potential while maintaining compliance, trust, and security.

Regulatory Compliance and Ethical Data Usage #

AI-driven life insurance models must comply with strict global regulations such as GDPR (Europe), CCPA (California), and industry-specific insurance laws. Ensuring responsible AI usage involves:

• Addressing AI bias and fairness – AI models trained on historical data may unintentionally reinforce biases, leading to discriminatory pricing or coverage decisions. Insurers must implement AI fairness auditing to detect and mitigate bias. The Equisoft, LIMRA & UCT Data Readiness Study, “Assessing Data Readiness for AI in the Life Insurance Industry” highlights concerns that bias in AI models could impact policy pricing and risk assessment, leading to regulatory scrutiny and consumer distrust.

• Ensuring transparency and explainability – Regulators will increasingly require clear, interpretable AI models that can justify underwriting and claims decisions. This is essential for customer trust and compliance.

• Strengthening data privacy protections – AI-driven underwriting and fraud detection depend on large volumes of sensitive customer data (medical records, financial history, IoT data). Without robust cybersecurity and consent management, insurers risk compliance violations and reputational damage.

According to KPMG’s 2023 Insurance CEO Outlook, 52% of insurance CEOs cite ethical concerns and lack of AI regulation as major hurdles, with 72% supporting AI regulations on par with climate commitment policies. This highlights growing industry awareness of AI’s risks and the need for governance frameworks to protect consumers while enabling innovation.

Data Readiness #

The Equisoft, LIMRA & UCT Data Readiness Study found that data quality, integration, and governance are the primary obstacles preventing insurers from successfully adopting AI. The study revealed:

• Fragmented data systems – Many life insurers still rely on legacy infrastructure with siloed databases, making it difficult to enable real-time decision-making and AI-driven insights.

• Inconsistent data governance – Globally varying regulations create compliance challenges, requiring insurers to standardize data security, access controls, and governance policies.

• Limited cross-industry data sharing – AI thrives on diverse, large datasets, yet insurers have been slow to adopt open-source AI models or participate in data-sharing ecosystems, limiting AI’s predictive power.

According to the report, data readiness is the single biggest barrier to AI adoption. Poor data quality, integration issues, and inconsistent governance prevent insurers from fully leveraging AI’s potential.

Legacy Systems and Organizational Change Management #

Many insurers operate on outdated IT systems that are incompatible with AI-driven solutions. The transition to AI requires:

• Migrating from legacy systems to cloud-based infrastructures – AI models demand high-speed data processing and real-time analytics, which on-premise legacy systems cannot support.

• Upskilling employees for AI adoption – AI adoption requires workforce transformation. Insurance professionals need training in AI-driven analytics, automation tools, and regulatory compliance to work alongside AI-powered assistants and predictive models.

• Balancing automation with human expertise – AI in underwriting and claims processing is shifting from human-in-the-loop models to fully automated systems. However, insurers must ensure appropriate human oversight to maintain trust and regulatory compliance.

How to Improve Data Readiness for AI #

To successfully integrate AI, insurers must invest in data maturity, align AI initiatives with business goals, and modernize their technology infrastructure. The Equisoft, LIMRA & UCT Data Readiness Study found that 78% of insurers cite data quality, integration, and governance as the top barriers to AI adoption.

Addressing these challenges requires a multi-faceted approach that includes organizational culture, data strategy, and technological modernization.

Foster a Data-Driven Culture and Hire the Right Talent #

A strong data culture is the foundation for AI success. Insurers must promote data literacy across all departments, ensuring that employees—from underwriting to claims management—understand how to interpret, utilize, and protect AI-driven insights. Hiring AI specialists, data scientists, and governance experts is essential to developing robust data models, ensuring compliance, and reducing AI bias.

However, only 38% of insurers feel they have the right talent in place to manage AI-driven initiatives. To bridge this gap, insurers should prioritize AI training programs and foster a culture of innovation that encourages cross-departmental collaboration on AI projects.

Achieve Organizational Alignment for AI Success #

Effective AI depends on high-quality, real-time data, yet many insurers still rely on fragmented, legacy systems that make AI integration difficult. To improve data readiness, insurers must:

• Automate data cleansing and validation to ensure accuracy and eliminate inconsistencies.

• Enhance real-time data integration through standardized metadata management.

• Invest in predictive and prescriptive analytics to transform raw data into actionable insights for underwriting, risk modeling, and customer engagement.

The research also indicates that only 50% of insurers have effective metadata management systems in place, limiting their ability to harness AI-driven insights. Strengthening data governance frameworks will be key to maximizing AI’s potential.

Align AI with Business Goals and ROI #

AI initiatives should not exist in isolation—they must be closely linked to business strategy and financial objectives. Insurers should:

• Ensure AI supports key business objectives, such as improving customer experience, increasing efficiency, or reducing fraud.

• Develop a scalable AI roadmap that allows for phased implementation.

• Identify high-impact AI use cases with measurable ROI to justify continued investment.

Without a clear AI roadmap and ROI framework, insurers risk scattered AI adoption with limited business impact. Insurers that align AI investments with measurable business outcomes will be better positioned to drive long-term success.

Upgrade Tech Infrastructure for AI Scalability #

Legacy systems remain a significant roadblock to AI adoption, with 78% of insurers still operating in hybrid IT environments that limit real-time processing. To modernize IT infrastructure, insurers must:

• Migrate to cloud-based platforms to improve scalability and processing power.

• Implement API-driven data exchange to facilitate real-time decision-making.

• Strengthen cybersecurity and compliance frameworks to protect sensitive AI-driven data from breaches and unauthorized access.

The Equisoft study found that insurers that invest in cloud and API-driven infrastructure see faster AI implementation and improved operational efficiency. Upgrading core systems to support real-time AI analytics will be essential for future competitiveness.

Final Thoughts #

AI has the power to transform life insurance, but without data readiness, strategic alignment, and modern IT infrastructure, insurers will struggle to maximize its benefits. By investing in a data-driven culture, improving data quality, aligning AI with business goals, and modernizing technology infrastructure, insurers can unlock AI’s full potential—delivering faster claims processing, more accurate underwriting, and a better customer experience.