Artificial intelligence (AI) adoption is a clear differentiator between insurers that proactively adapt and those that lag or fall behind. As one survey respondent noted in Cherry Bekaert's inaugural CFO survey, “Widespread adoption of AI and automation is set to reshape everything from underwriting to financial reporting.” This statement reflects the transformational shift felt across the industry.
According to McKinsey, generative AI (GenAI) could unlock up to $70 billion in additional annual revenue for the insurance industry. As data volumes grow and customer expectations evolve, insurers are under increasing pressure to modernize operations and remain competitive.
At the same time, the cost of inaction is rising. Research by Capgemini shows that 92% of insurers believe AI agents will enable expansion into new geographies without requiring significant upfront infrastructure investments. Insurers that delay risk falling behind more agile competitors who are redefining pricing, service delivery and risk evaluation through AI-driven models.
In this article, we will walk through some of the key value drivers for insurers and explore what AI means for those operating in the insurance industry, how it is being adopted, where it delivers the most value and what insurers should consider moving forward.
What Does Artificial Intelligence Look Like in Insurance?
Artificial intelligence in insurance refers to the use of technologies that can analyze information, identify patterns, generate content and support decision-making across core insurance functions, such as underwriting, claims management, fraud detection and customer engagement, among others. This shift enables insurers to move toward more dynamic and responsive operating models, where processes improve over time with experience and data inputs, providing useful, proactive information with which to make decisions.
For years, the insurance industry depended on human underwriters and claims adjusters to manually review complex policy documents, medical records and inspection reports and to analyze large amounts of data to make decisions. Today, AI systems support these professionals by quickly highlighting key insights, visualizing trends and unique patterns in data and helping management teams spend more time on complex and high-value decisions.
Types of AI Utilized in Insurance
Technologically advanced insurance organizations are implementing AI to serve different functions across the value chain.
Machine Learning (ML)
ML analyzes historical data for tasks like risk assessment, pricing policies and detecting fraudulent claims, allowing insurers to make more informed decisions.
Natural Language Processing (NLP)
NLP extracts insights from unstructured data such as policy documents, emails and customer conversations. It also powers chatbots and virtual assistants for customer support.
Robotic Process Automation (RPA)
RPA handles routine tasks like data entry, policy administration and claims processing, streamlining back-office operations by reducing manual intervention.
Generative AI (GenAI)
GenAI drafts reports, summarizes documents and generates customer communications. It also assists teams in creating personalized content at scale.
Computer Vision
Computer vision assesses damage from images and videos, especially in auto and property claims, accelerating inspections and improving claim evaluations.
Application Programming Interfaces (APIs)
APIs connect AI solutions with policy administration systems, claims platforms and third-party platforms, enabling real-time information exchange and streamlined workflows.
Predictive Analytics
Predictive analytics forecasts claim likelihood, customer behavior and risk exposure. It supports decision-making in underwriting, pricing and customer retention strategies.
Understanding AI Adoption in Insurance
Insurers are increasingly adopting AI to improve underwriting accuracy, streamline claims processing, enhance customer experiences and reduce operational costs. However, unsuccessful implementation requires addressing challenges related to data quality, regulatory compliance, governance and workforce readiness.
Benefits of AI in the Insurance Industry
Understanding the benefits of AI helps insurers identify where it can create real value, guiding smarter investment and adoption decisions. The benefits of utilizing AI in insurance include:
- Speed and Efficiency: Reduces processing time across operations
- Improved Accuracy: Minimizes errors in data handling and evaluations
- Stronger Fraud Prevention: Detects unusual patterns more effectively
- Cost Optimization: Lowers operational overhead through automation
- Scalable Operations: Supports growth without proportional resource increases
- Consistent Decision-making: Ensures standardized outcomes across processes
Risks of AI for the Insurance Industry
The use of AI in the insurance industry also introduces certain risks that organizations must carefully consider. Key risks include:
- Data Privacy Concerns: Increased reliance on sensitive data raises security risks
- Algorithmic Bias: Models may reflect biases present in training data
- Integration Challenges: Aligning AI with legacy systems can be complex
- Regulatory Uncertainty: Evolving compliance requirements create additional pressure
- Lack of Explainability: Some AI outputs are difficult to interpret and audit
- Model Drift: Performance can decline over time if models are not continuously monitored and updated
AI Governance & Compliance
As AI adoption expands, insurers must establish governance frameworks that address model risk, transparency, regulatory requirements and responsible AI practices. Strong governance helps organizations maintain trust while ensuring AI-driven decisions remain fair, explainable and auditable.
6 Common Use Cases of AI in Insurance Companies
Exploring these use cases provides a clearer view of how AI is being applied in practice across different areas of the insurance value chain. The use cases include:
- Claims Management
- AI-powered Underwriting
- Fraud Detection
- Risk Management
- Customer Service
- Code Modernization
1. Claims Management
AI accelerates how claims are reviewed, validated and resolved, which directly impacts customer satisfaction and operational costs. By reducing manual intervention and enabling faster decision-making, AI helps insurers deliver quicker settlements and more consistent outcomes. It is especially valuable during high-claim volumes, such as natural disasters or peak periods.
Claims teams, adjusters and operations leaders benefit the most from these capabilities.
2. AI-powered Underwriting
AI brings greater consistency and depth to risk evaluation processes, directly affecting profitability and risk exposure. By incorporating multiple data sources and reducing reliance on manual judgment, AI enables more precise and scalable decision-making. It is best used when insurers are handling large volumes of applications or expanding into new markets.
Underwriters, risk analysts, and product teams can leverage AI to improve efficiency and decision quality.
3. Fraud Detection
AI continuously monitors patterns and identifies unusual behaviors that may go unnoticed manually. This is crucial because fraud can lead to significant financial losses and reputational damage. By enabling early detection and real-time alerts, AI helps insurers take proactive action and minimize risks. It is most effective in environments with high transaction volumes and complex claim scenarios.
Fraud investigators, compliance teams and claims departments benefit greatly from these insights.
4. Risk Management
AI provides deeper visibility into potential threats and emerging trends, helping insurers maintain financial stability and meet regulatory requirements. By analyzing both internal and external data, AI helps insurers anticipate risks and make proactive adjustments to their strategies. It is particularly useful during periods of market uncertainty or when entering new risk segments.
Risk managers, executives and strategic planners can use these insights to make more informed decisions.
5. Customer Service
AI transforms customer service by enabling faster, more consistent and scalable interactions, which is especially important as customer expectations continue to rise. By automating routine queries and supporting agents with relevant information, AI improves both efficiency and customer experience. It is best applied in high-volume customer interaction scenarios, such as policy inquiries or claims updates.
Customer support teams and service managers are the primary beneficiaries.
6. Code Modernization
AI-driven code modernization helps insurers transition from legacy systems to more agile and scalable technology environments, accelerating innovation and decreasing maintenance costs. By automating code analysis, refactoring and documentation, AI accelerates modernization efforts and reduces technical debt. It is most valuable when organizations are undergoing digital transformation or integrating new technologies.
IT teams, developers and digital transformation leaders can take full advantage of these capabilities.
Next Steps on Insurance and AI
Technologies such as generative AI, Internet of Things (IoT), and even drones are enabling insurers to anticipate risks, automate underwriting and process claims in near real time. As these capabilities mature, AI is expected to become nearly universal across insurance operations, fundamentally reshaping how policies are designed, priced and delivered.
This transformation is giving rise to a hybrid workforce, where human expertise is augmented by AI-driven insights and decision support. Rather than replacing human roles, AI in insurance is enhancing them, allowing teams to focus on complex judgment, customer relationships and strategic decisions. As a result, the future of insurance will be defined by closer collaboration between humans and intelligent systems.
Your Guide Forward
A critical aspect of successful AI adoption is building the right organizational and governance framework. Insurers must ensure data quality, establish clear accountability for AI-driven decisions, and address concerns related to transparency, fairness and compliance. This often involves developing internal guidelines, investing in talent, and creating processes to monitor and refine models over time.
Cherry Bekaert’s Insurance practice professionals can support this journey by helping insurers address adoption challenges, align AI initiatives with business goals, and implement solutions that deliver measurable outcomes. With the right guidance, organizations can move beyond pilots and turn AI into a sustainable competitive advantage.
