Top 12 AI Tools Transforming Insurance in 2025: The Complete Guide to AI in Insurance

The AI in insurance story has moved past “innovation theater.” Most insurers now use AI somewhere in the value chain—but only a few are getting outsized business results.

Industry research shows why this matters:

• By 2025, 91% of insurance companies are expected to have adopted AI technologies in some form, and the AI-in-insurance market is projected to grow from about $7.7 billion in 2024 to $35.8 billion by 2029, with an estimated $10.3 billion size in 2025 and more than $13 billion in 2026.
• Insurers that are AI leaders have generated roughly 6.1× higher total shareholder returns than AI laggards over the past five years.
• In real “domain rewires,” AI has delivered around 10–20% better new-agent success and sales conversion, 10–15% higher premium growth, 20–40% lower customer-onboarding costs, and 3–5% more accurate claims.

So the problem is no longer whether to use AI in insurance, but which AI tools to bet on, and where to apply them first.

This guide walks through 12 practical AI tools in insurance across distribution, underwriting, claims, fraud, service, and analytics. For each, you’ll see:

• What it does
• Key features
• A referenced datapoint where available
• Who it’s best for

We’ll close with a grounded 2026 outlook: what “normal” will look like inside an AI-powered carrier or agency.

1. AI Phone Agents & Lead Response

Distribution is where many insurers quietly lose the most money: slow lead response, inconsistent follow-up, and overwhelmed producers. It’s also one of the fastest places for AI in insurance to show measurable gains.

OneAI – AI Phone Agents for Insurance

What it does

OneAI is a KPI-driven phone marketing platform for insurance, built around AI phone agents that:

• Call new digital leads within seconds
• Collect structured insurance data (for P&C, life, Medicare and more)
• Schedule appointments or perform warm transfers to human agents
• Run full outbound campaigns with detailed attribution

Key capabilities are outlined on the OneAI insurance use-case page, including instant quote callbacks, aged lead revival, and renewal campaigns tailored to insurance workflows and its broader outbound and inbound platform overview.

Why it matters

• Response time is critical: industry data shows that around 78% of consumers in insurance purchase from the first responder, and that contacting leads within a minute can multiply conversion rates.
• AI phone agents that consistently respond in seconds and never forget follow-up give agencies a structural edge in speed-to-lead and coverage discovery, while freeing human producers to focus on complex cases and closing.

Best for

Independent agencies, captives, and MGAs that want to win on speed-to-lead and producer productivity, and treat the phone as a primary growth channel.

Liberate – Voice AI for Carriers & Large Agencies

What it does

Liberate is a verticalized voice and “agentic AI” platform for insurance. It automates large chunks of sales, service, and claims conversations and plugs directly into policy and claims systems.

On its site, Liberate reports handling millions of automated resolutions per month across lines of business. In catastrophe scenarios, their CEO has described situations where AI-assisted claims response times fell from roughly 30 hours to about 30 seconds for certain events.

Best for

Carriers and large intermediaries needing always-on, high-volume voice automation across claims and service, especially in CAT-heavy or multi-lingual environments.

2. Claims Processing Automation

Claims is one of the most advanced areas for AI in insurance—and one of the easiest places to show hard numbers: cycle time, costs, and NPS.

Tractable – Computer Vision for Motor & Property Claims

What it does

Tractable uses computer vision to assess vehicle and property damage from images and video, giving carriers AI-generated repair estimates and triage recommendations.

In a case study with Admiral Seguros, Tractable reports that 70–75% of customers who receive the AI web-app link complete their claim digitally, often in about two minutes. 

Other references show AI review of estimates happening in seconds instead of roughly 30 minutes per claim, with the potential to cut cycle times by several days.

Best for

P&C carriers with meaningful volumes of auto and property claims who want more touchless claims options and faster settlement for low-complexity losses.

Hyperscience – Intelligent Document Processing for Insurance

What it does

Hyperscience provides intelligent document processing (IDP) to extract structured data from forms, emails, and attachments in claims and policy operations.

A Hyperscience case study describes a Fortune 500 insurer reducing processing times by around 85% and responding to customers roughly five times faster after introducing IDP into claims workflows. Another industry summary notes that Hyperscience implementations can cut document handling times by up to 85% in real deployments.

Best for

Carriers and MGAs with document-heavy claims and service operations who want to move from manual rekeying to straight-through ingestion.

3. Fraud Detection Systems

As AI becomes mainstream in insurance, fraudsters are evolving too. Dedicated AI fraud platforms are becoming standard infrastructure.

Shift Technology – AI Claims Fraud Detection

What it does

Shift provides AI-based fraud detection across underwriting and claims. Its product pages highlight the ability to detect suspicious claims in real time or in batch, using a wide range of structured and unstructured data.

Shift and its partners report that their claims fraud solution can deliver around 3× higher detection hit rates compared with manual or rules-based methods, based on benchmarks with P&C insurers.

At the market level, AI-driven risk models and fraud analytics are estimated to reduce fraud-related losses by tens of billions of dollars annually and cut leakage by more than $17 billion worldwide.

Best for

Mid- to large-sized carriers that want portfolio-level fraud intelligence built into claim and SIU workflows.

FRISS – End-to-End Fraud & Risk Scoring

What it does

FRISS specializes in AI-powered fraud and risk detection for P&C, covering underwriting, claims, and SIU.

External coverage of a FRISS case with Anadolu Sigorta, a major Turkish insurer, reports that the company achieved around 210% ROI within 12 months after implementing FRISS’s predictive analytics solution for fraud detection. FRISS itself highlights deployments where claims handling times dropped by about 66% for some carriers.

Best for

Carriers looking for end-to-end fraud prevention that spans from quote to claim, with a strong track record in P&C.

4. Underwriting Enhancement

Underwriting may be less visible than claims, but it’s central to the economics of AI in insurance. The goal: better risk selection, faster decisions, and portfolio-aware growth.

Cytora – Submission & Risk Flow Orchestration

What it does

Cytora uses AI and LLMs to ingest and normalize submissions, extract risk data from documents, and route cases based on appetite and complexity.

In a widely cited example, Markel reported a 113% increase in underwriting productivity (measured as written premium per FTE) after working with Cytora to rewire their commercial underwriting flow.

Best for

Commercial and specialty insurers dealing with submission-heavy lines who need to prioritize the right risks and reduce manual triage.

Federato – Risk-Aware Underwriting Platform

What it does

Federato offers a RiskOps underwriting platform that keeps underwriters aligned with real-time appetite and portfolio exposure. It brings together underwriting, portfolio, and distribution data to help underwriters pick the most attractive risks.

Federato positions its platform as a way to turn underwriting into a continuous “risk and capital allocation” function rather than a case-by-case process, with case studies describing improved win rates and better capital deployment.

Best for

Specialty, E&S, and complex commercial carriers that want portfolio-aware underwriting and clearer alignment between underwriters and enterprise-level risk appetite.

5. Customer Service & Engagement

Customers now expect fast, personalized experiences. AI in insurance is increasingly about meeting those expectations while controlling cost.

Zelros – Advisor Copilot & Recommendation Engine

What it does

Zelros provides an AI recommendation engine and advisor copilot for insurance, surfacing next-best products and actions in real time across channels.

A joint factsheet with Capgemini reports that using Zelros can lead to:

• Around 20% uplift in cross- and up-sell
• Up to 57% better quote conversion in some deployments

A Zelros case study also highlights a carrier that reduced advisor training time from approximately six months to two months, gained about 5% efficiency, and generated roughly $20 million in additional annual premium after adopting recommendation tooling.

Best for

Life, health, and multi-line carriers who want advisor productivity and cross-sell gains, plus consistent recommendations for compliance.

Meera AI – SMS-First Engagement

What it does

Meera delivers AI-powered conversational SMS for regulated industries, including insurance. It’s used for lead follow-up, renewals, appointment reminders, and payment nudges.

Across its case studies, Meera reports outcomes such as:

• Up to 40% higher connect rates and roughly 3× ROI on conversational text campaigns in several verticals.
• Lead-to-enrollment increases of around 47% and 62% in education clients when moving to AI-driven texting.

Those same patterns are applied in insurance for renewal outreach and marketplace engagement, where Meera showcases automated texting flows for renewals and marketplace follow-up.

Best for

Agencies and carriers that want text-first, compliant engagement for renewals, reminders, and lead nurturing—especially for mobile-native customers.

6. Document Processing & Compliance

Even in 2025, a lot of insurance value is still trapped in PDFs, binders, and schedules. Intelligent document processing is a core part of AI in insurance.

Chisel AI – Commercial Insurance Document Understanding

What it does

Chisel AI focuses specifically on commercial insurance documents, using NLP to read and compare submissions, policies, binders, and schedules.

While Chisel’s headline metrics tend to sit in sales materials rather than public case studies, comparable IDP deployments in insurance have been shown to:

• Cut document verification times by roughly 85%
• Reduce operating costs by around 30%
• Compress multi-stage document workflows from weeks to days

Chisel’s value lies in bringing that type of acceleration to complex, P&C-specific documents rather than generic forms.

Best for

Commercial lines brokers and MGAs needing faster comparison and analysis of complex submissions and policy documents at scale.

7. Predictive Analytics & Risk Modeling

Finally, the quieter backbone of AI in insurance: models that improve underwriting, claims, and pricing decisions behind the scenes.

Gradient AI – Predictive Models for Underwriting & Claims

What it does

Gradient AI provides pretrained and configurable machine-learning models for workers’ compensation, group health, and related lines, focusing on underwriting, claim outcomes, and pricing.

One Gradient AI study across more than 200,000 workers’ comp claims from 60 insurers reports:

• Around 15% reduction in attorney involvement in lost-time claims when using AI-predicted escalation risk
• Millions of dollars in reduced legal expenses for carriers that adopted the models

Gradient also notes that advanced models can improve the accuracy of claim outcome predictions by roughly 10 percentage points compared with traditional approaches.

Best for

Carriers who want field-tested, insurance-specific ML models without building a full data science organization from scratch.

Making the Right Choice: Where to Start with AI in Insurance

With so many options, the biggest mistake isn’t “missing AI, it’s implementing AI that doesn’t move the numbers that matter.

A few evidence-backed principles:

1. Start with your biggest leak.
   
   
   
   • If leads are going cold because no one calls back quickly, start with AI phone agents and SMS. External research shows that reaching leads within a minute can massively lift conversion, and that most buyers go with the first responder.
   • If fraud leakage is material, start with AI fraud detection, where the broader industry is seeing billions in savings and significant improvements in detection rates.
   • If underwriters are drowning, start with submission intake and prioritization.
2. Prioritize integration over novelty.
   McKinsey’s work across more than 200 insurers emphasizes that AI creates real value when it rewires entire domains (like distribution or claims), not when tools sit off to the side as isolated pilots .
3. Measure hard outcomes.
   Tie each AI initiative to clear KPIs:
   
   • Distribution: contact rate, bind rate, cost per bound policy
   • Claims: cycle time, leakage, cost per claim, NPS
   • Underwriting: hit ratio, loss ratio, turnaround time
     This mirrors the domain-level metrics McKinsey uses to quantify impact (e.g., 10–20% better agent success, 20–40% lower onboarding costs) in real AI transformations .
4. Think in 90-day slices.
   Many of the case studies above—Aviva’s claims domain, Markel’s underwriting productivity, FRISS’s fraud ROI—show meaningful impact within months, not years . Design your roadmap the same way.

The 2026 Outlook: How AI in Insurance Will Feel from the Inside

By 2026, asking “Are you using AI?” will be as dated as asking “Do you have a website?” The real questions will be:

• How much of your book flows through AI-augmented journeys?
• How quickly can you move from pilot to full domain transformation?

Grounded in what’s already happening, here’s a realistic view.

1. Agentic AI Runs Simple Journeys End-to-End

McKinsey describes multi-agent AI systems that can handle intake, risk profiling, pricing, compliance checks, and decision orchestration across underwriting and claims. For straightforward policies and low-complexity claims, expect:

• Data collection
• Third-party enrichment
• Pricing within appetite
• Policy issuance or small-claim settlement

to increasingly run as AI-first, human-reviewed only by exception.

2. Hyper-Personalization Becomes Standard

Stats from CoinLaw show AI improving pricing accuracy by over 50% and helping insurers deliver more tailored offers and experiences, pushing customer-retention gains into the 15–20% range for some deployments.

In practice, that means:

• Renewals with coverage and pricing tuned to individual behavior
• Outreach cadence adjusted to actual responsiveness
• Real-time recommendations that close protection gaps before customers ask

Customers may not say “I want AI,” but they’ll feel the gap when they don’t get this level of personalization.

3. Voice-First Becomes Normal

Between AI phone platforms (OneAI, Liberate, others) and the rapid adoption of voice assistants generally, simple insurance tasks will increasingly start with a conversation, not a form:

• Instant quote callbacks from AI phone agents
• FNOL and endorsements handled over voice, with optional digital follow-up
• Outbound renewals, cross-sell, and reactivation campaigns driven by AI voice and SMS, with warm transfer to humans when needed

CoinLaw notes that AI chatbots and virtual assistants already handle around 42% of customer service interactions in 2025 across insurance. Voice is the next frontier of that same trend.

4. Governance & the “Human Premium”

As AI spreads, regulators are tightening expectations around fairness, transparency, and bias. Tools that offer explainable AI and governance will move from “nice to have” to “non-negotiable”.

At the same time, human expertise doesn’t disappear. It gets more concentrated:

• Complex commercial risks
• High-net-worth personal lines
• Specialized coverages and emerging risks

In those areas, experienced underwriters, producers, and claims leaders—augmented by AI, not replaced by it, will command a higher premium in both compensation and customer demand.

The Bottom Line

AI in insurance is no longer a slide in an innovation deck. It’s the operating system for carriers, MGAs, and agencies that are serious about growth and profitability.

The difference between the few AI leaders and the many laggards is simple:

• They pick specific, painful problems to solve.
• They choose tools with real-world case studies and defensible numbers.
• They integrate AI into the actual flow of work, not into side experiments.
• They measure outcomes in hard business KPIs and scale what works.

The 12 tools in this guide are not hypothetical. They’re live in the market, with publicly available statistics behind many of their claims.

If you’re ready to move from “talking about AI” to seeing it in your pipeline, loss ratio, and expense ratio, the next step isn’t to read another trend piece. It’s to pick one domain, one metric, and one AI-enabled use case, and start.