How Insurers Are Using AI To Quote Policies And Process Claims

Artificial intelligence continues to transform the insurance industry, affecting underwriting, pricing, claims processing and customer engagement. What was once manual, time-consuming work is rapidly becoming automated. A major development in 2025 was the rise of agentic AI—autonomous systems capable of performing insurance tasks without human input. Agentic AI started to be utilized in insurance processes in early 2025, with pilot projects reported by insurers and vendors. By mid-2025, industry publications and consulting firms documented real-world use in claims processing, fraud detection and underwriting. The shift signals a fundamental change in how the insurance industry operates and serves customers.

For policyholders, the speed and accuracy of AI-driven systems can mean faster quotes, quicker claim payouts, and more consistent decisions. For insurers, the efficiency gains are substantial: AI-driven algorithms allow underwriters to analyze risks more effectively, enhance pricing accuracy, and accelerate application processing. With ML and deep learning, underwriting timelines shrink from days or hours to mere minutes as most processes become automated and data-driven. Yet this transformation also raises important questions about fairness, transparency, and oversight that regulators and the industry are beginning to address.

How AI Quotes Insurance Policies 📋

By leveraging artificial intelligence and machine learning technologies, insurers can now automate complex underwriting processes that previously required extensive human intervention. This advancement not only reduces operational costs but also enhances risk assessment and improves the overall customer experience.

The foundation of AI-powered quoting is intelligent document processing. One of the most important applications of machine learning in insurance underwriting is automating submission triage. This involves utilizing AI-driven algorithms to efficiently sort and allocate applications to underwriters based on predefined criteria. When a customer applies for a policy, submission triage reads the package, extracts risk attributes, enriches with external data, scores against carrier appetite, and either declines, refers, or prepares a quote draft.

AI models can analyze a broader set of data points—including social behavior, credit scores, and environmental factors—to assess risk more accurately. This leads to more competitive pricing and better alignment with the policyholder's risk profile. Instead of waiting days or even weeks, traditional underwriting processes often take weeks to evaluate a policy. With automated systems, this time can be reduced to minutes, allowing insurers to serve customers faster and more efficiently.

The Technology Behind Claims Processing

To shorten the claims lifecycle, insurers leverage AI's ability to automate key claims processing activities, such as claim data analysis, document generation, and automatic payouts. AI also removes time-consuming, error-prone tasks from insurers' workflows, optimizing efficiency and productivity.

AI-powered claims processing automates these steps. For example, computer vision can analyze images and videos of car and property damage to evaluate the extent of damages, necessary fixes, and reimbursement costs. AI systems can analyze documents and media files to quickly assess claims. This allows insurers to settle simple claims quickly, reduce processing times, and standardize workflows.

AI enables automated triage of first notice of loss, computer vision based damage assessment, fast-track settlement under defined thresholds, and real-time prediction of ultimate cost. The result is a dramatic reduction in handling time. Straight-through processing is the automated end-to-end handling of insurance claims without human intervention. AI-enabled STP rates for simple claims have jumped from 10–15% to 70–90%, meaning the vast majority of straightforward claims can be resolved automatically.

Speed and Efficiency Gains

The numbers tell a compelling story. Among the 82% of insurance companies using AI in claims, average processing time dropped from 10 days to 36 hours. Some carriers have achieved even more dramatic improvements. Lemonade holds the world record: a full claim settlement in 2 seconds using its AI bot "AI Jim." As of Q4 2025, 55% of all Lemonade claims are fully automated from start to finish.

Cycle time reductions of 40 to 60% and handling cost reductions of 25 to 35% are now well documented in carrier disclosures. These efficiency gains translate to lower operational costs, which can potentially benefit customers through more competitive pricing.

Fraud Detection and Prevention

One of the most valuable applications of AI in insurance is spotting fraudulent claims. AI can uncover hidden correlations and patterns previously undetectable to the human eye, making it useful for detecting suspicious activity or fraudulent behavior. AI and machine-learning tools can considerably reduce fraud by enabling insurers to spot unusual patterns that a human might miss.

Fraud detection has been substantially enhanced by generative AI systems that analyse the textual content of claims submissions, claimant communications, and supporting documentation to identify patterns indicative of fraudulent activity. Unlike rule-based fraud detection systems that rely on predefined indicators, LLM-based approaches can detect subtle linguistic cues, narrative inconsistencies, and contextual anomalies that suggest fabricated or exaggerated claims.

Insurance companies may also use visual analytics to assess auto or property damages based on images and videos. Using visual evidence, AI can examine whether the damage is worth the amount that has been quoted by analyzing data from similar claims in the past. This combination of textual and visual analysis makes it significantly harder for fraudulent claims to slip through undetected.

The Role of Autonomous AI Agents

Shift launched "Shift Claims," an agentic AI product that does more than flag suspicious claims. It orchestrates the full investigation workflow: assigning tasks, gathering evidence, and routing cases to the right specialist. These systems represent a new generation of automation where each agent handles a different step: intake, coverage verification, documentation, and payout recommendation. The agents coordinate autonomously and escalate to humans only on exceptions.

This autonomous approach enables insurers to handle far more cases with fewer human investigators, freeing staff to focus on complex or unusual situations that require judgment and experience. The systems improve over time as they process more claims and receive feedback from human reviewers.

Regulation and Transparency Requirements

As AI use expands across underwriting and claims, regulators have begun setting standards. By late 2025, 23 states and Washington, D.C., had adopted the NAIC's AI Model Bulletin, with some variations. The NAIC's AI Model Bulletin is principle-based, requiring insurers to establish governance, documentation and audit procedures, but not prescribing specific standards.

Insurers must ensure that any use of AI does not violate antidiscrimination laws, including prohibiting unfair discrimination in underwriting and claims decision processes. Insurers must maintain internal documentation that explains how an AI model functions, including its input data, assumptions and how outputs influence decisions. These requirements ensure that automated systems operate fairly and can be audited if concerns arise.

Human Oversight Remains Important 👥

Many elements of underwriting can be automated, but the process is unlikely to become fully automated. Automated underwriting systems can handle easy tasks and cases, but human involvement will always be necessary to ensure AI's accuracy, comprehension, and lack of bias. The adoption of AI and ML in underwriting could "unload" underwriters, taking the most tiresome and repetitive work from them. Instead, employees could focus on more interesting and complex cases of risk assessment.

The goal is collaboration, not replacement. AI handles high-volume routine work efficiently and consistently, while trained professionals review edge cases, complex situations, and any claims flagged for potential issues. This hybrid model leverages the strengths of both technology and human judgment.

Key Practical Takeaways 💡

• Quotes arrive faster: With automated systems, policy evaluation time can be reduced to minutes, letting applicants get answers quickly.
• Claims settle quicker: Simple claims can now be resolved automatically at rates as high as 70–90%, dramatically reducing the time to payment for straightforward cases.
• Fraud protection improves: AI patterns and anomalies better than traditional rule-based systems, helping protect the insurance pool and keep premiums reasonable.
• Consistency increases: Standardized AI processes ensure that similar situations are treated the same way, reducing arbitrary outcomes.
• Transparency matters: Insurers are required to document and explain how their AI makes decisions, and regulators now have oversight authority to audit these systems.
• Humans still decide: AI handles routine cases; complex, unusual, or sensitive claims still go to trained professionals for review and decision-making.

Looking Ahead

The insurance industry is in the middle of a significant technological transformation. What began as experimental pilot projects has moved into mainstream operations at major carriers. AI is making insurance faster, more transparent, and increasingly aligned with actual risk. As the technology matures and regulation catches up, customers and insurers alike stand to benefit from more efficient operations and fairer, better-informed decisions. The companies that get the balance right—combining automation with meaningful human oversight and clear ethical guardrails—are setting the standard for the industry's future.