Fraud detection, risk assessment and pricing lead AI use in insurance

The review identifies risk assessment and pricing as the top priorities for both academia and industry. In automotive insurance, AI-driven telematics and usage-based insurance (UBI) systems are redefining premium calculations by analyzing driver behavior, road conditions, and real-time GPS data. Advanced machine learning models, including neural networks and convolutional architectures, are used to detect risky behaviors and automate vehicle damage assessments with accuracy surpassing 90 percent.

Artificial intelligence is transforming the insurance industry across its three dominant sectors, automotive, health, and property, but researchers warn that gaps between academic innovation and industry implementation could hinder progress if left unaddressed. A new study titled “AI Revolution in Insurance: Bridging Research and Reality”, published in Frontiers in Artificial Intelligence, systematically evaluates AI's role in risk assessment, claims processing, fraud detection, and customer personalization.

Using PRISMA review guidelines, the study analyzes 65 academic papers and 33 industry cases to compare research outcomes with field deployment across global insurance markets. Conducted by researchers at the Luxembourg Institute of Science and Technology and the University of Limerick, the review highlights that while AI models offer unprecedented improvements in underwriting accuracy, operational efficiency, and customer engagement, implementation remains uneven. Disparities persist between sectors, with automotive insurance leading in automation and health insurance in predictive analytics. Property insurance, however, trails behind in key innovation areas like policyholder behavior modeling and AI-driven claims management.

What are the most promising AI applications in insurance?

The review identifies risk assessment and pricing as the top priorities for both academia and industry. In automotive insurance, AI-driven telematics and usage-based insurance (UBI) systems are redefining premium calculations by analyzing driver behavior, road conditions, and real-time GPS data. Advanced machine learning models, including neural networks and convolutional architectures, are used to detect risky behaviors and automate vehicle damage assessments with accuracy surpassing 90 percent.

In the health insurance sector, AI is most effective in cost prediction and fraud detection. Deep neural networks trained on demographic, biometric, and lifestyle data are now capable of forecasting insurance premiums with high precision. Ensemble learning models, including XGBoost and Random Forest, support explainability through tools like SHAP and ICE plots, ensuring both regulatory compliance and user trust. Blockchain-enhanced AI systems also play a growing role in combating insurance fraud.

Property insurance applications range from climate risk modeling using satellite imagery to automated blueprint analysis and home valuation. However, the review finds limited academic focus on customer behavior analysis and claims automation in this sector, despite commercial systems already operational. AI’s potential to improve pricing frameworks, assess structural vulnerabilities, and enable proactive claims handling remains underleveraged by scholars.

Which insurance companies are leading AI integration?

The review provides a detailed survey of commercial AI adoption, revealing a more balanced landscape than academic literature suggests. Health insurance leads in implementation counts, with 23 industry deployments, followed by property and automotive at 20 and 19 respectively. Companies like Arity, Gradient AI, and Metromile dominate automotive innovations through UBI and real-time behavioral analytics. Tractable and Snapsheet lead in claims automation using computer vision and machine learning.

In property insurance, CAPE Analytics and Lemonade have pioneered AI-powered risk assessment and automated underwriting. Firms such as Bold Penguin and Slice use AI to match customers with tailored insurance products across the property domain. Health insurers and benefits providers, including SprouttInsurance and Nayya, use AI to predict costs and recommend personalized plans.

Cross-sector solutions are also emerging. Companies like H2O.ai, Duck Creek, and WorkFusion offer scalable platforms for risk modeling, fraud detection, and intelligent document processing that span multiple insurance types. These domain-agnostic tools are particularly well-positioned to serve emerging needs in mixed insurance portfolios and embedded insurance ecosystems.

Why are ethical, regulatory and technical barriers slowing full adoption?

Despite AI’s broad appeal, significant roadblocks remain. The review outlines six key challenges insurers must overcome: data quality, ethical governance, regulatory compliance, explainability, continuous adaptation, and societal acceptance. Many AI systems rely on sensitive personal or health data, making strong data governance frameworks essential, especially in jurisdictions governed by HIPAA, GDPR, and the European AI Act.

Ethical concerns persist around algorithmic bias, especially in high-risk decisions involving coverage denial or premium increases. The study points to LIST’s ethical AI leaderboard, which evaluates large language models on fairness across categories such as ageism, racism, and political bias. Explainability also remains a technical hurdle, with many AI systems operating as black boxes. Techniques such as Shapley values and TabNet architecture are promising tools to demystify decision-making processes and improve stakeholder trust.

Regulatory oversight is intensifying. The European Union’s AI Act categorizes AI applications by risk, placing stricter demands on high-impact insurance functions. These include dynamic pricing and automated claims decisions, both of which require transparency, human-in-the-loop safeguards, and real-time auditability. Failure to meet these requirements can stall deployments or invite legal scrutiny.

The study also emphasizes the importance of model retraining and adaptation. AI models must be continuously updated to reflect evolving risk environments, such as climate change for property insurance or pandemic-related fluctuations in health claims. Retrieval-Augmented Generation (RAG) is one emerging solution that helps mitigate LLM hallucinations by combining real-time document retrieval with generation capabilities.

Lastly, societal and cultural acceptance remains a subtle but critical barrier. The human element in insurance, trust, empathy, and clear communication, cannot be fully replaced by automation. Researchers argue for co-creation models where policyholders, regulators, and insurers jointly shape AI design, fostering sustainable innovation and ethical governance.

How can research catch up with practice and guide future development?

A striking gap identified by the review lies in academic underrepresentation of certain high-impact domains. Property insurance, despite being rich in AI implementation, is underexplored in peer-reviewed literature. Claims automation in both property and health domains also lacks sufficient scholarly attention. Fraud detection, although widely deployed in industry, is only lightly addressed in research.

The review calls for targeted academic inquiry into these gaps and recommends deeper collaboration between insurers and scholars. It urges a shift from theoretical AI models toward real-world validations with robust performance metrics, ethical audits, and economic cost-benefit analyses. Researchers are also encouraged to engage with regulatory bodies to co-develop standards for model auditability, bias mitigation, and explainable decision-making.

The future of the insurance sector's transformation hinges not just on technological progress but on its alignment with ethical imperatives, regulatory expectations, and public trust.