Article

Artificial Intelligence (AI) is fundamentally reshaping the insurance sector, delivering unprecedented accuracy, efficiency, and personalization across underwriting, actuarial pricing, and risk evaluation. While AI’s use continues to expand and evolve, there are many considerations that need to be made to ensure fairness, transparency, and confidentiality. This article explores how AI can be used in underwriting, pricing, and risk evaluation along with its limitations and potential concerns.

AI in Underwriting

AI transforms underwriting by expediting data collection, improving the identification of risk, and automating routine tasks. Instead of relying solely on traditional data like demographics or static health histories, AI integrates diverse sources—including real-time feeds from IoT devices, social media, telematics, and public records. For example:

• AI-powered application review automates data collection and highlights complex cases for human oversight, reducing underwriting processing times for standard policies from days to minutes while increasing accuracy to over 99%.
• Machine learning models can scan historical claims, environmental data, and behavioral patterns to generate dynamic, individualized risk scores, allowing policies to adjust as circumstances change.
• Insurance companies leverage AI to prefill forms, assess driving habits or home sensor data for property and auto insurance, and detect anomalies that might suggest fraud.
• The integration of generative AI, such as Allianz’s BRIAN system, is helping underwriters analyze large, intricate data sets for faster and fairer decision-making.

AI in Actuarial Pricing

Traditionally, actuaries use statistical techniques such as Generalized Linear Models (GLMs). Today, machine learning and more advanced AI techniques—like Random Forests or Gradient Boosting Machines—deliver higher predictive performance and model complex, nonlinear patterns more effectively:

• AI-powered pricing models scan much broader risk factors, such as claims heatmaps, live location data, or even short-term weather trends, resulting in more granular premium settings.
• Automated actuarial models can dynamically adjust prices for different policyholder categories, factoring in real-time exposure, inflation, and behavioral variables, thus reducing the occurrence and financial impact of claims.
• AI tools support no-claims bonus systems and encourage risk-reducing behavior, offering dynamic, individualized discounts based on ongoing risk assessments.

AI in Evaluating Risk

AI excels at aggregating and analyzing vast, complex datasets to create detailed, real-time risk profiles:

• AI agents synthesize structured and unstructured data (claims records, social feeds, IoT metrics) to identify subtle relationships unseen by humans, enabling precise risk segmentation.
• Predictive modeling identifies trends in claim frequency, loss events, and customer behaviors, making risk evaluation highly personalized—shifting away from broad categorization to individualized scores based on actual activity.
• In practice, AI reduces the time required to assess risk by up to 90%, and companies employing AI-driven risk assessment tools have seen up to a 25% increase in accuracy.
• Real-time risk evaluation not only expedites the underwriting process but also ensures that pricing and coverage are continually aligned with risk exposure, leading to more resilient and responsive insurance portfolios

Limitations and Considerations

Artificial Intelligence provides substantial benefits to the insurance industry, but it also introduces significant limitations and concerns related to fairness, transparency, regulation, and data integrity.

Algorithmic Bias and Discrimination

AI models in insurance can unintentionally encode or amplify historical biases embedded in data. This can lead to outcomes where certain customer groups are unfairly denied coverage or charged higher premiums due to indirect proxies for protected characteristics such as race, gender, or socioeconomic status. Lawsuits, such as those faced by major insurers like State Farm and Cigna, have highlighted how algorithmic bias in AI decision-making can create legal, ethical, and reputational risks for insurers.

Lack of Transparency and Explainability

Many AI systems operate as “black boxes,” making it difficult for insurers, regulators, and consumers to understand how decisions are made. This lack of explainability complicates regulatory oversight and accountability, especially in underwriting and pricing practices that impact consumer rights and access to insurance. The limited transparency also raises challenges when attempting to identify or rectify discriminatory outcomes from AI-driven processes.

Regulatory and Governance Concerns

The insurance industry currently faces a patchwork of emerging regulations across different states and countries regarding AI usage. Regulatory uncertainty increases the risk of non-compliance, particularly given the complexity of AI-enabled products and the rapid evolution of standards. Insurance firms must invest in robust data governance, audit trails, and documentation to ensure responsible AI use and to prepare for stricter regulatory regimes.

Data Quality and Security

AI’s effectiveness depends on high-quality, representative data. Problems such as incomplete, outdated, or skewed datasets can lead to erroneous predictions and risk assessments, directly impacting policyholder outcomes. Additionally, the heavy reliance on third-party and “scraped” data can introduce hidden liabilities if data is collected or used in violation of privacy rules or contractual terms.

Over-Reliance and Human Skills Erosion

Increased reliance on AI can lead to skill erosion among insurance professionals, who may lose the ability to recognize contextual nuances or adapt to exceptional cases not anticipated by algorithms. Operators may accept AI recommendations uncritically, increasing the risk of systematic errors when the technology fails or data changes unexpectedly.

Consumer Trust and Experience

Automated claims handling, chatbots, and decision-making processes may alienate customers who prefer human interaction, especially during stressful incidents like claims filing. Maintaining consumer trust will require balancing automation efficiency with quality customer service and clear communication regarding AI’s role in decisions.

While AI offers powerful capabilities for the insurance industry, its limitations and potential concerns must be carefully managed through ongoing investment in transparency, governance, fairness testing, and consumer education. Addressing these challenges is essential for ensuring trustworthy, equitable, and resilient insurance operations in the age of AI.