AI in insurance means using software that learns from data to do work people used to handle by hand, such as reading claim documents, scoring the risk of a new policy, and spotting claims that look fake. The payoff is measurable: faster settlements, quicker quotes, and fewer fraudulent payouts.
The insurance industry is a natural fit for AI, because the work revolves around documents, data, and decisions that repeat all day long. With our artificial intelligence development services, we automate those routine tasks inside the systems a business already runs, instead of replacing them. For most insurers, trying AI is no longer the hard part, but getting value from it across the whole company still is. BCG reported in 2025 that only 7% of insurers had brought their AI systems to scale, while about two-thirds were running pilots.
This guide covers the four areas where AI in insurance pays off most reliably: claims, underwriting, fraud, and AI agents that work under human supervision. It also explains, in plain terms, what building these systems involves.
What Are the Benefits of AI in Insurance?
Most of the value comes from a handful of jobs rather than a broad AI program. Each automates slow, manual steps in a process an insurer runs every day.
Claims processing
Reads claim forms, photos, and invoices, then checks them against the policy
Faster payouts with far less manual data entry
Lemonade’s claims handling costs fell to a record 5% of premiums, against an industry average of about 9%
Underwriting
Gathers data from applications and outside sources to score risk
Quicker, more consistent quotes
AIG’s AI assistant sorts and prioritizes every new submission for unusual, hard-to-place risks, so the team handles more policies without new hires
Fraud detection
Flags claims and documents that don’t fit normal patterns
Fewer fraudulent payouts
Deloitte’s 2026 insurance outlook estimates real-time fraud analytics could save property and casualty insurers up to $160 billion by 2032
AI agents with human review
Splits a claim into steps and hands the payout decision to a person
Settlement in hours instead of days
Allianz cut processing and settlement time on small claims by 80%
All four depend on clean data and on knowing when a person has to step in. The sections below show how each works.
AI Insurance Claims Processing Takes Over the Paperwork
A claim rarely arrives as one tidy file. It’s usually a form, a few photos, a repair estimate, and sometimes a police report. Someone has to read all of it, type the key details into the claims system, and check them against the policy. AI insurance claims processing handles that reading and checking, so adjusters, the people who assess each claim, spend their time on decisions instead of data entry.
Three technologies do most of the work:
- Document reading: Optical character recognition (OCR) turns scanned pages into text, and a language model picks out the details that matter, such as dates, amounts, and policy numbers.
- Photo analysis: Computer vision, meaning software that interprets images, estimates damage from photos of a car or a roof. Our overview of computer vision applications shows where the technology works well today.
- Validation: The system compares every extracted detail with the policy and flags anything missing or inconsistent.
Simple claims that pass every check can go straight to payment with no one touching them, which the industry calls straight-through processing. What makes or breaks the system is how it treats uncertain cases. A good setup scores its confidence in each detail it extracts and sends anything below the threshold to a person. Our playbook for automating document review explains that routing, including a second verification pass for high-value claims.
We’ve built the text-reading side before. For Recruit Media, our engineers trained a neural network on more than 1.5 million texts to analyze CVs and job descriptions and suggest the most relevant keywords. Generic document tools often stumble over an insurer’s own forms and codes, and AI custom software development lets us train a model on the paperwork your team actually handles.
How AI Underwriting Cuts the Time to a Quote
Underwriting is how an insurer decides whether to cover a risk and what to charge for it. An underwriter gathers information from the application, past claims, inspection reports, and outside databases, then judges the risk. Accenture found that underwriters still spend more than a third of their time on non-core work such as data collection and administration.
AI underwriting targets that lost time. Predictive models learn patterns from historical policies and claims, then score each new application against them. They can also draw on real-time data, such as property details or driving behavior recorded by a device in the car. The underwriter receives a pre-filled file and a suggested risk score, then makes the decision.
Two benefits follow:
- Speed: Quotes go out faster, since the slow work of collecting and checking data is done before anyone opens the file.
- Consistency: Similar applicants get similar prices, because the model applies the same logic every time, whoever handles the case.
Insurers expect AI underwriting tools to become routine. In an Accenture survey of 430 senior executives, adoption is set to jump from 14% to 70% within three years.
However, there’s one catch: a model that learns from past decisions can also learn past bias, and regulators check for exactly that.
AI Flags the Doctored Evidence Fraudsters Submit
Insurance fraud ranges from padding a genuine claim to staging an accident that never happened, and AI now makes faking evidence easier than ever. In a 2026 study, Verisk found that 98% of insurers agree AI editing tools are driving a rise in digital media fraud, meaning doctored photos and documents. Only 32% felt very confident they could spot a deepfake, which is an image, video, or voice recording generated by AI so convincingly that it looks real.
The good news is that AI in insurance works on the defense side too, in three layers:
- Anomaly detection: A model learns what normal claims look like and flags any that don’t fit, such as a repair bill far above typical for that damage.
- Document forensics: Software checks files for signs of editing, such as mismatched fonts, altered file details, or pixel patterns left behind by image tools.
- Network analysis: The system maps links between claimants, repair shops, clinics, and addresses to reveal organized rings that a single-claim review would miss, such as several claims tied to one phone number.
None of these layers makes the final call. Together they produce a risk score, and the special investigations unit, the team that handles suspected fraud, reviews the claims ranked highest.
We’ve applied the same methods in another industry where forged paperwork and faked photos are costly. Our guide to using AI in real estate to detect fraud and property anomalies shows how document AI, image forensics, and deepfake checks combine into one screening flow, with people reviewing the high-risk flags.
Agentic AI Handles the Claim Checks: A Person Approves the Payout
Agentic AI describes software agents that carry out a series of steps toward a goal instead of answering a single question. In claims, one agent might check coverage while another pulls weather data and a third looks for fraud signals, each passing its findings along. Human-in-the-loop means a person stays in charge of the decisions that matter most, usually the payout.
Allianz’s Project Nemo is the clearest example. Launched in Australia in July 2025, it handles food spoilage claims under AUD 500, such as a freezer full of groceries lost in a power outage. With seven AI agents splitting the work, turnaround time fell by 80%. Allianz states that payout decisions are never automated, so in this system a claims professional signs off on every payment.
Travelers followed in early 2026 by launching an AI claim assistant developed with OpenAI that takes the first report of car damage by voice. Customers can switch to a live specialist at any point. Celent found that 22% of insurers planned to have an agentic AI solution in place by the end of 2026.
Both launches share a design worth copying: agents do the groundwork, while a person keeps the final decision. A natural place to apply it is claims triage, which means sorting incoming claims by urgency and complexity. Agents gather facts, run checks, and draft a recommendation. They escalate anything unusual, such as a high claim value, a coverage question, or a fraud flag, to an adjuster with the full case file attached. Deciding who sets the escalation rules and who can stop an agent is its own topic, covered in enterprise AI agent governance. When we build agents through our AI agent development services, that hand-off to a person is part of the design from day one.
Three Things Decide Whether an AI Project Pays Off
These use cases already work at real insurers, but copying them doesn’t guarantee the same results. Most of the trouble lies outside the technology itself: the same BCG report found that only about 10% of the problems in scaling AI come from the models. Here’s where to focus first:
- Clean, connected data: A model is only as reliable as the records it learns from. When policy data, claims history, and customer details sit in separate systems, joining and cleaning them comes first, and it’s often the longest stage.
- Core systems that stay in place: Many insurers run policies and claims on decades-old core systems that are too risky to replace. The practical route is to connect AI to that software: the model reads data out and writes results back while the core platform keeps running. Adding AI to legacy systems walks through that approach step by step.
- Decisions you can explain: By April 2026, 24 states plus Washington, DC, had adopted the AI model bulletin from the National Association of Insurance Commissioners (NAIC). The bulletin expects a written AI program, safeguards against unfair discrimination, and oversight of outside vendors. The EU AI Act treats AI that assesses risk and sets prices in life and health insurance as high-risk. For what that label demands in practice, see AI compliance for financial applications. Every automated decision should leave a record of the data it used and why it scored the way it did.
Working around core systems that can’t be replaced is familiar ground for us. For KB&G’s KillerBee pricing platform, built for construction materials companies, our software had to exchange data with clients’ older, often undocumented tools. Even with that constraint, its automation sped up quoting by 90%.
Where Should You Start With AI in Insurance?
Proof of results is growing. The Evident AI Index found that 20 of the 30 insurers it tracks now report outcomes for at least one AI use case. The clearest example in this guide started narrow: Allianz began with food claims under AUD 500. Follow the same path: pick one costly, repetitive process, usually claims intake, underwriting preparation, or fraud screening, and automate it while a person keeps the final say. Once that works, move on to the next one.
If you already know which task takes up the most hours on your team, that’s your starting point. Redwerk can scope it with you, connect it to the systems you run today, and build in the human checks regulators expect. Tell us what you want to automate, and we’ll outline the steps to get there.
FAQ
What is AI in insurance?
AI in insurance is the use of software trained on past cases to support work across the policy lifecycle, from quoting a new customer to settling a claim. Older automation follows fixed if-then rules, while AI can cope with messy inputs like handwritten forms, photos, and free-text notes. In a typical setup, AI prepares decisions faster, and trained staff still approve anything beyond routine cases.
How is AI used in insurance claims processing?
When a customer reports a loss, AI can collect the details through an app or a voice assistant, read the attached photos and receipts, and confirm the damage is covered. Low-risk, low-value claims can then be paid within hours. Anything complex or suspicious goes to a claims adjuster, the specialist who assesses losses, along with an organized file instead of a stack of paperwork.
What is AI underwriting?
AI underwriting is the use of predictive models to evaluate an insurance application and estimate how likely it is to lead to a claim. The model compares each applicant with patterns in historical data and outside records, such as property or driving information, then recommends a price or raises concerns. An underwriter still decides, and the model should be tested regularly for unfair treatment of similar applicants.
Can AI detect insurance fraud?
AI can detect many kinds of insurance fraud, from an inflated repair bill to an accident photo altered with editing software. The software measures each claim against typical ones and connects related claims that share people, vehicles, or addresses. Rather than accusing anyone, the system ranks claims by risk, so investigators review the most suspicious first and honest customers get paid faster.
Is AI in insurance regulated?
AI in insurance is regulated, mainly state by state in the US. About half the states have adopted guidance from the National Association of Insurance Commissioners (NAIC). The NAIC expects insurers to document how they govern AI, test their models for unfair bias, and vet their vendors. Colorado has its own regulation. In the EU, the AI Act counts AI that scores and prices life and health policies as high-risk.
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