AI has become the focus of insurance’s ongoing technological transformation. Using AI, underwriters can assess risk and price policies with unprecedented precision by processing real-time data from unstructured data and documents. On the claims side, AI is critical to improving financial results and the customer experience with faster and more accurate decisions and payouts.
Insurers that have successfully moved AI-powered tools out of a test environment and into production have taken a cautious approach to evaluating and understanding the technology’s potential impact. A vital step in this process is finding use cases where AI will deliver the best ROI. Let’s look at some of the most common ones.
AI Use Cases for Underwriting
Successfully evaluating potential customers is the key to profitability. Improving underwriting efficiency with AI can reduce revenue leakage, speed policy issuance, and improve the customer experience, helping insurers stay competitive in a fast-moving market. Here are areas that can be significantly improved with AI.
- Submission Intake and Data Extraction, Endorsement Processing, and Renewals: AI accurately extracts, analyzes, and validates key data from insurance applications, loss runs, statements of value (SOV) and other documents, automatically passing this data to the underwriting platform. Eliminating manual work from these processes gives underwriters more “bandwidth” to focus on building agent/broker relationships and other higher-value tasks.
- Submissions Triage: AI improves submission triage by automatically extracting key information from documents, assigning risk scores, prioritizing submissions based on risk potential, and providing insights to underwriters. Streamlined workflows lead to better decision-making while minimizing human error.
- Request Documents from Brokers: Incomplete submissions can slow the underwriting process, causing lost opportunities to create new business and improve agent/broker relationships. AI can identify missing items and collect all necessary information without human intervention to reduce back-and-forth communication.
- Process Supplemental Questionnaires: These documents capture additional descriptions of the applicant’s products or services, employment categories, industry-specific risk factors and other information. AI-powered systems can automatically extract unstructured and semi-structured data from these surveys and populate key fields in an underwriting platform. This allows insurers to improve risk assessment, personalize coverage more efficiently, and improve quote turnaround times.
AI Use Cases for Claims
AI can instantly and accurately process insurance claims with minimal human intervention, reducing claims overpayment and delivering an outstanding customer experience. AI eliminates manual document and data processing to empower adjusters, managers, and examiners to settle claims faster and more accurately while expanding teams’ capacity during CAT season and other peak demand periods. Use cases where insurers have deployed AI with great success include:
- Claims Indexing and Classification: Insurers are overwhelmed by documents and data, which often require manual processing. AI automatically reads, understands, and classifies unstructured and structured information from medical records, police reports, ACORD forms, and other documents at up to 99% accuracy. Benefits include downstream support for adjusters, reduced dependence on BPO, and more efficient claims handling.
- FNOL/FNOI Setup: Initial policyholder submissions after a covered loss typically include unstructured and structured documents. AI streamlines FNOL/FNOI setup by automatically extracting key information from these reports to verify coverage and create claims in the carrier’s system of record. Increased efficiency in FNOL/FNOI processing can significantly boost productivity and capacity.
- Identify Legal Demands: Legal demand packages – formal documents submitted to insurers by attorneys – pose a serious challenge for claims teams. Demand packages often contain structured and unstructured information on multiple pages. AI can find and track time-sensitive requests buried within these documents, ensuring prompt action from adjusters to prevent missed deadlines and improve overall demand compliance.
- Calculate Reserves: AI can analyze claim data to calculate and set initial reserve estimates, minimizing subjectivity and improving consistency across the process. It can also flag and assign potential high-value claims to more seasoned adjusters.
- Identify Subrogation Opportunities: AI can automate subrogation by analyzing claims data, identifying recovery opportunities, assessing liability, and tracking deadlines. This accelerates the process and helps to reduce loss ratios by maximizing recovered losses.
AI Use Cases for Policy Servicing
AI increases operational efficiency across the insurance value chain, helping insurance operations teams reduce costs, facilitate regulatory compliance and reporting, and enhance customer service to improve insurance for all. Ideal policy servicing use cases include:
- COI (Certificate of Insurance): AI can generate Certificates of Insurance and instantly distribute them to policyholders, boosting customer satisfaction while significantly reducing manual servicing efforts.
- Endorsement Processing: Endorsements allow customers to modify coverage to suit changing business needs. AI can analyze endorsements – and flag them for review by human experts when necessary – to accelerate this workflow for more responsive customer service.
- Managing correspondence: Managing large volumes of requests for additional claim information or status updates is labor- and resource-intensive. AI automates these requests and collects and evaluates data from incoming correspondence to ensure critical information is captured and acted on quickly.
- Send EOB (Explanation of Benefits): AI can distribute explanations of benefits (EOBs) and policy ID information to claimants, streamlining customer communications with timely delivery of important information.
- Premium audits: This step is vital to setting final accurate premiums for a business’s workers’ compensation risk. AI can request, collect, and extract data automatically from employer payroll records, job classifications, tax forms, and other data to accelerate precision policy pricing.
Identifying use cases where AI can help price policies more accurately, reduce claims overpayment, and delight customers is an important step toward improving performance across the insurance value chain.
Learn more about how other insurers evaluate their business needs and drive operational excellence with AI by downloading this annual 2025 State of AI Adoption in Insurance Report.