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Insurance runs on expert judgment. But most of that judgment is buried under manual work.
Underwriters spend 41% of their time on administrative and operational tasks instead of assessing risk, according to a Capgemini report. Claims teams re-key data from PDFs and photos. Service teams answer the same policy questions hundreds of times a day.
For years, the answer was to buy more insurance underwriting software or claims processing software. That solves the platform problem. It rarely solves the process problem. Broker emails, ACORD forms, loss runs, and legacy system handoffs are workflows that off-the-shelf underwriting software solutions don’t fully absorb.
AI agents change that equation. Instead of replacing your core systems, an AI agent for insurance works around them: it reads the documents, extracts the data, applies your rules, and hands clean, decision-ready work to your people.
This isn’t a distant bet. McKinsey estimates that by 2030, more than 90% of pricing and underwriting for many policies will be automated.
In this guide, we compare the top AI agents for insurance underwriting and claims processing, as well as agentic AI platforms for insurance. Beyond the comparisons, you’ll learn whether to choose off-the-shelf agents or build custom AI agents.
What Is an AI Agent for Insurance?
An AI agent is software that can understand a goal, break it into steps, use your existing tools and data to complete those steps, and escalate to a human when it’s unsure.
That’s what separates agentic AI in insurance from the two technologies it’s often confused with:
- Chatbots answer questions. They don’t complete work.
- RPA (robotic process automation) follows fixed scripts. It breaks when a document format or workflow changes.
An AI agent does the work and adapts: it can read a broker submission it has never seen before, pull the fields your underwriter software needs, check them against your risk appetite, and route the case, with a full audit trail of every step.
AI agents support underwriters, adjusters, and service representatives rather than replace them. Every use case below keeps a human in the loop for judgment calls.
Best AI Agents and Software Platforms for Insurance
Selecting the right AI agent for insurance depends on whether you want a specialized backend tool for risk and document analysis, an omnichannel front-office conversational agent, or an AI framework integrated directly into your core insurance platform.
The leading AI agents across underwriting and claims are categorized below by their primary strengths:
Top AI Agents for Underwriting
Sixfold AI Underwriter — Best for AI-driven underwriting decision support
Sixfold’s AI agent for underwriting reviews each submission against the risk details, carrier appetite, guidelines, broker history, similar past cases, and portfolio fit.
It extracts key data, flags missing information, reasons through the risk, and recommends the next step, such as a referral, declination, or broker follow-up.
Sixfold’s AI agent helps underwriting teams at carriers and MGAs speed up quoting, peer reviews, referrals, decision documentation, and audit prep.
Why it stands out
Sixfold AI agent produces underwriting work product with a clear point of view and rationale.
It uses a centralized Underwriting Brain that is pre-trained on core risk patterns. It analyzes complex submission PDFs and carrier manuals
It also builds institutional memory from prior submissions, broker outcomes, and team decisions, so knowledge carries forward across the underwriting team while the human underwriter stays in control.
Target Buyer
Mid-to-large commercial line insurers looking to accelerate manual, high-touch underwriting desk decisions.
Brisc Property Submissions AI Agent — Best for property submission intake and triage
The underwriting AI agent by Brisc automatically extracts and validates data from emails, PDFs, and spreadsheets. It captures critical details, including program information, loss history, insured values, property schedules, and risk factors.
Once turned into quote-ready data, the agent applies your company’s unique underwriting appetite, SOPs, triage rules, and business logic to instantly prioritize which submissions are worth pursuing.
By automating this front-end workflow, Brisc helps teams eliminate tedious data re-keying, chase fewer missing details, and triage submissions significantly faster.
Why it stands out
Brisc is built for property underwriting teams. It works from broker emails, so teams do not need a new portal or a deep integration to get started.
Underwriters can review and edit the results before pushing them to a workbench. Brisc also maintains an audit trail that shows what data was extracted, where it came from, and why it was used.
Target Buyer
Monoline property carriers or MGAs struggling with backlogged data entry.
GIA and ARKUS — Best for appetite checks and risk analysis for commercial underwriting
GIA and ARKUS are two AI agents by NeuralMetrics SAMA.
GIA reviews submissions against each insurer’s underwriting guidelines, risk appetite, and business classification rules. It helps teams identify in-appetite accounts and applies updated guidelines when appetite changes.
ARKUS analyzes risks using real-time public data and user-provided exposure information. It identifies risk factors, monitors new submissions and renewals, and helps teams detect similar or adjacent risks across the book.
Together, these agents help teams improve risk selection, support renewals, reduce premium leakage, and increase underwriting capacity.
Why it stands out
GIA and ARKUS are role-based agents that handle different underwriting tasks, share information, self-correct outputs, and support transparent, explainable decisions.
GIA maps internal underwriting appetite. ARKUS scans the live web and public registries to collect real-time operational data. Together, they uncover hidden risks that brokers may have omitted from the submission.
Target Buyer
Commercial P&C insurers needing instant appetite checks during high-volume small-to-medium enterprise intake.
Top AI Agents for Claims Processing
Clive — Best for claims lifecycle automation
Five Sigma’s Clive is a multi-agent AI claims solution that helps insurers manage claims from first notice of loss (FNOL) through resolution.
It supports claims teams by reviewing claim details, assisting with triage, verifying coverage, analyzing liability, handling documents, and guiding next steps in the claims process.
It can work on top of an existing claims management system or come built into Five Sigma’s own claims platform.
Clive solves critical operational challenges for MGAs and claims adjusters, including mitigating claims leakage, complex disputes, and fraud exposure.
Why it stands out
Clive uses specialized AI agents for different parts of the claims process rather than relying on a single generic assistant. It can work with existing claims systems or run inside Five Sigma’s platform, so teams can modernize claims operations without replacing their current setup.
Target Buyer
Claims operations managers and independent adjusters seeking agile software overlays.
Shift Claims — Best for agentic claims assessment and prioritization
Shift Claims, powered by Shift Technology, provides an agentic intelligence layer to assess, prioritize, route, guide, and automate claims across the entire claims lifecycle.
The AI agent automatically reviews claim documentation to comprehensively evaluate policy coverage exclusions, liability, damage, injury severity, subrogation potential, and litigation risk.
It classifies incoming claims by operational complexity, urgency, financial exposure, and automation potential. This enables claims handlers and adjusters to accelerate cycle times, eliminate claims leakage, and improve the overall policyholder experience.
Why it stands out
Shift Claims uses agentic AI to support straight-through processing (STP) for straightforward claims and guide handlers when human judgment is needed. It can work with existing claims systems, helping insurers modernize claims without replacing their core setup.
Target Buyer
Large-scale tier-1 and tier-2 enterprise P&C insurers with high claim volumes.
All-in-One Agentic AI Platforms for Insurance
Guidewire — Best for end-to-end insurance operations
Guidewire is an industry-leading cloud platform for P&C insurers that manages the entire insurance lifecycle, including policy administration, claims, billing, pricing, and underwriting.
It helps replace legacy systems with connected, end-to-end workflows across core operations. The platform supports insurers, MGAs, and underwriting and claims teams with faster product launches, streamlined operations, and improved customer experiences.
It also provides a shared data model and integration ecosystem to simplify operations and accelerate digital transformation.
Why it stands out
Guidewire brings policy, billing, claims, pricing, and underwriting together on a single cloud platform with a shared data model. Its ProNavigator assistant works inside PolicyCenter and ClaimCenter as a secure, context-aware AI co-worker.
It automates multi-step admin tasks, pulls guidance from internal rules and documents, maintains an audit trail, and provides users with role-specific support directly within their workflow.
Target Buyer
Large global insurance corporations undergoing total digital core modernization.
Duck Creek Agentic AI Platform — Best for insurance carriers automating underwriting and claims workflows
Duck Creek Agentic AI Platform is an insurance-native platform for property and casualty and general insurers.
It enables carriers to create, deploy, and govern AI agents that work across underwriting, claims, policy administration, billing, and risk operations.
In underwriting, the platform can collect, enrich, assess, and prioritize submissions, helping teams quote faster and focus on the most valuable risks.
For claims, its Agentic First Notice of Loss application captures and validates incident details, checks policy coverage, flags potential fraud, and routes each case to the appropriate workflow.
By coordinating these activities across connected systems, Duck Creek reduces manual data entry, fragmented handoffs, incomplete information, and processing delays.
Why it stands out
Duck Creek uses insurance-specific data, terminology, business rules, and regulatory requirements to guide its AI agents.
Its neuro-symbolic technology combines generative AI with fixed decision rules, which helps insurers produce more explainable, controlled, and compliant outcomes.
The platform also gives insurers built-in governance, audit trails, security controls, and support for agents created by Duck Creek, its partners, or the carriers themselves.
Target Buyer
Modern SaaS-first P&C insurers that prioritize transparent, rule-governed AI workflows.
Buy vs. Build AI Agents for Insurance: Which One is Right for You?
Build vs. buy isn’t about right or wrong. It depends on your workflows.
Choose an off-the-shelf AI agent when it already supports your use case, integrates with your systems, and can be configured around your operating requirements. Build a custom AI agent when the workflow depends on company-specific rules, exceptions, integrations, or knowledge that a standard product cannot support effectively.
Off-the-shelf insurance AI solutions work well for common workflows such as fraud detection, submission intake, underwriting analysis, and claims triage.
The gap often appears in the work between these systems. Insurers may still face:
- Submissions arriving through multiple inboxes and file formats.
- Underwriting rules that vary by program, region, or line of business.
- Employees copying information between disconnected systems.
- Claims that follow company-specific review and escalation paths.
- Critical knowledge spread across spreadsheets, emails, guidelines, and experienced employees.
- Exceptions that require different actions based on authority limits, risk thresholds, or missing information.
An off-the-shelf solution may handle part of this process, but it may not support every carrier-specific handoff, exception, and decision rule.
A custom AI agent can integrate existing systems, use approved documents and guidelines, apply company-specific rules, and escalate cases that require human judgment. It does not need to replace the insurer’s core platform. It can operate as a process layer that coordinates the work across existing tools.
Successful insurance AI depends on more than the underlying model. Long-term value comes from reliable data, workflow integration, governance, human oversight, and continuous feedback from real decisions.
Why Choose Code District for Custom AI Agents?
Off-the-shelf insurance agents work well when your workflow fits the product. The challenge begins when a single workflow spans multiple systems, follows company-specific rules, or relies on manual handoffs.
Code District specializes in building custom AI agents for insurers whose workflows extend beyond the capabilities of off-the-shelf products.
We build AI agents around your existing systems, documents, business rules, and decision logic. Our agents eliminate the repetitive work that slows underwriting and claims teams.
The result is less manual data entry, fewer handoffs, more consistent rule application, and faster processing. Underwriters and claims professionals remain in control of exceptions, complex cases, and high-impact decisions.
Interested in seeing how custom AI agents help you underwriting and claims processing? Book a free 30-minute consultation with experts at Code District.
Frequently Ask Questions
What is an AI agent for insurance?
An AI agent for insurance is software that completes multi-step insurance work — reading submissions, extracting data, applying underwriting rules, routing claims — using your existing systems, and escalating to humans when judgment is required. It differs from a chatbot (which only answers questions) and RPA (which only follows fixed scripts).
How is agentic AI different from traditional insurance underwriting software?
Traditional underwriting software solutions are systems of record: they store policy data, apply rating tables, and manage workflow stages. Agentic AI is a working layer on top — it handles the unstructured inputs (broker emails, ACORD forms, loss runs) and manual handoffs that platforms leave to people. Most insurers need both.
Can AI agents fully automate underwriting?
Can AI agents fully automate underwriting? For low-complexity, high-volume policies, AI automated underwriting can handle end-to-end straight-through processing within defined thresholds.
For complex commercial and specialty risks, AI agents support rather than replace underwriters. They extract and enrich data, screen submissions against appetite, calculate risk scores, and prepare the file for review. The underwriter then makes the final decision.
McKinsey projects that more than 90% of pricing and underwriting for many policies could be automated by 2030, but human oversight of complex risks isn’t going away.
What are the key features to look for in insurance underwriting software?
If you are looking to buy off-the-shelf insurance underwriting software, make sure it includes intelligent automation, automated submission intake, AI-driven risk assessment, easy integrations, security and audit trails, and human-in-the-loop controls.
Should we buy underwriting tools or build a custom AI agent?
Buy the system of record; build the process layer. If your pain is policy administration or rating, evaluate insurance underwriting platforms. If your pain is intake, triage, document chaos, legacy handoffs, or the work between systems, a custom-built agent may fit better than an off-the-shelf tool because it can be trained on your documents and configured around your guidelines.
How much does it cost to build an AI agent for insurance?
A focused first agent (for example, submission intake automation for one line of business) typically costs about $80,000 to $150,000 as a custom MVP. The final cost depends on document complexity, system integrations, and compliance requirements. This is often comparable to one year of enterprise platform licensing, for an asset you own.
How long does it take to implement AI agents for insurance?
A scoped first workflow with one agent, one process, and human-in-the-loop can go live in weeks. This is much faster than a core system implementation, which typically takes 6 to 18 months. The main upfront work is defining escalation rules and success metrics with underwriting or claims leadership.
