The phrase “insurance claims automation” used to mean some basic workflow tools to take care of repetitive manual tasks and a few email or SMS alerts sent out to policyholders when their policy was up for renewal or something happened with a claim in progress.
Now, claims processing is where AI can do some of the heavy lifting and humans only have to step in when nuance is needed. Most insurers aren’t yet fully deploying AI in insurance claims in this way, but the day is coming when more and more will, to the point that AI in claims processing will be commonplace.
So what does it mean to add AI to the mix?
What actually changes in claims management when machines start making decisions?
We’ll break it down and link to some helpful resources in this article.
Let’s start with what generative AI actually is.
While traditional AI sorts data and makes predictions, generative AI (GenAI) creates something new — like text, images, or simulations.
In the context of insurance, AI might generate customer communications, claim summaries, or even predictive models of claims outcomes.
Gen AI in claims processing isn’t about replacing people, though, it’s about helping them move faster, cut down on repetitive, mind-numbing tasks that suck their time, and get better results.
It also opens the door to more advanced capabilities like:
Insurers piloting GenAI in claims are already seeing improved cycle times and lower cost-to-close metrics.
And, truthfully, the need for good generative AI in claims processing is urgent.
Claims cycles are often the most resource-intensive part of insurance: inconsistent data, growing fraud sophistication, and rising customer expectations make it harder than ever to deliver fast, fair, and accurate outcomes. GenAI provides a scalable, intelligent engine to absorb that complexity and deliver consistency across high volumes.
You can’t just flip a switch to “turn on” AI and get AI magic pouring out of your systems.
There’s a method to the intelligence, and in this section we’ll go over what are the 4 steps of the AI process for claims to understand how GenAI gets applied in this area.
Every great AI model starts with good data. From FNOL (first notice of loss) to policy details and images from accident scenes, insurance generates mountains of data. AI needs that raw material to learn, process, and act upon. Sources for this data can range from internal systems to third-party databases, IoT sensors, telematics, and even social media.
And, unfortunately, data silos remain a top frustration in insurance. Claims data often lives separately from customer data, policy info, billing records, and external sources like weather reports or police records. The ability to gather and unify this data is foundational to any AI effort.
So, before AI can be effective in claims processing, an insurer has to have an effective platform to pull all that data into one place. A core platform like EIS OneSuite that’s API-first and customer-centric wins here, because all relevant data can be attached to a customer record — no matter the source — which creates more efficient and accurate processing.
This is where the AI actually learns. Models are trained on historical claims data to identify patterns: fraud risks, claim cost ranges, processing timelines, and more. Different models may be used for different lines of business (e.g., auto vs. health vs. life insurance claims).
But with generative AI, training isn’t just a one-time event, it’s an ongoing process.
As new data comes in, the model improves. It learns regional repair cost variations, fraud tactics, and even the subtle language signals that might indicate a frustrated claim, flagging it for further investigation. (And then it learns from the outcomes of those claim settlements, improving future AI-driven evaluations.)
Once you have the data, you need to clean it. Duplicate records, missing values, different formats… all of that gets handled in data preprocessing. Natural language, image, and audio data also need to be converted into machine-readable formats.
The end goal is structured, high-quality data for AI model training.
If the data isn’t cleaned to be high quality, or isn’t real-time or accurate, it can affect any AI model you use negatively, and actually contribute to more claims leakage instead of less.
AI can’t fix bad data, but if you have the right data processing steps in place, it can make sense of what you give it and be a great help.
Once trained, the AI model is launched into your claims environment. But this isn’t “set it and forget it.” The model’s decisions are constantly monitored, tested, and updated to stay sharp. Insurers use KPIs like accuracy, speed, false positive/negative rates on fraud, and customer satisfaction to refine outcomes.
But even though it does require continual evaluation, it’s still much more efficient than processing claims in legacy or modern legacy systems. Typically, legacy systems can only handle digitizing manual processes, and modern legacy systems can sometimes be limited to “if this then that” rules, which don’t allow for the fast evolution of generative AI decision making.
This is why modern, cloud-native platforms are crucial — they support flexible workflows, real-time monitoring, and seamless model updates.
Automated claims processing is a combo move: workflow engines, data pipelines, smart business rules, and AI.
Here are some things that are automated in claims processing, among others:
Insurers using modern platforms like EIS can set up event-driven workflows so the right tasks kick off instantly, and the right people (or bots) get involved only when necessary. That means less paperwork, fewer delays, and fewer chances to lose a customer due to the frustration of a bad claims experience.
Automation can also:
For example, a low-severity windshield claim can move from FNOL to payout in under an hour with automation. Meanwhile, a suspicious fire claim gets flagged, rerouted, and reviewed by a senior adjuster with all supporting data already surfaced.
And automation doesn’t mean rigidity. Smart systems let carriers configure workflows based on business rules, regulatory needs, or partner preferences. Adjusters aren’t left out—they’re elevated.
AI in insurance use cases span the entire claims lifecycle, and how AI is used in claims handling is no longer a mystery — it’s a blueprint for speed, accuracy, and better customer experience.
At the first notice of loss (FNOL), data comes in hot and messy: phone calls, emails, scanned documents, maybe even a frantic note written on a napkin. NLP (natural language processing) and OCR (optical character recognition) can be used by AI to turn that chaos into structured data. ClaimCore® ingests and parses inputs in real time — not just flagging missing info but auto-validating it against policy rules and pre-set data quality checks. This cuts manual triage and eliminates the endless back-and-forth with the claimant.
AI can be used in fraud detection to not just flag weird patterns; it can continuously update the fraud score as more data flows in during the claim lifecycle. AI has the ability to crunch data from internal systems (policy history, past claims, payments) and external sources (location data, social feeds, medical records, third-party data vendors) and apply it to spot patterns human reviewers might otherwise miss.
Based on historical data, AI can assess complexity and automatically assign claims to the most appropriate entity to process it — whether human or software. This reduces triage delays, and increases claim processing capacity without adding staff.
Here’s how AI can be used in claims processing: Computer vision tools analyze images and video to estimate the extent and cost of damage. This is especially powerful for auto and property lines, where visual evidence tells a big part of the story.
GenAI can draft real-time updates and explanations of next steps for claimants. With EIS ClaimPulse, these communications are tailored to the policyholder’s context and claim status; no more confusing emails or silent delays.
This kind of functionality is no longer reserved for tech-forward startups. With ClaimSmart by EIS, established carriers are delivering these capabilities at enterprise scale. ClaimPulse makes the journey personal and proactive. ClaimGuard keeps fraud at bay. Combined, they turn what was once a pain point into a competitive differentiator.
Let’s put the “will AI replace claims adjusters?” question to bed. It won’t. What it will do is change their job for the better.
Instead of drowning in paperwork and tracking down PDFs, adjusters become high-impact decision makers. AI handles the busywork—gathering data, flagging risks, drafting messages—so human expertise can focus where it matters most: empathy, negotiation, and judgment.
What this looks like:
With solutions like EIS ClaimSmart, you get an AI insurance claims processing infrastructure that enhances rather than replaces your workforce. ClaimPulse cuts down on inbound status calls. ClaimGuard reduces false positives and missed fraud. Together, they streamline work, improve accuracy, and preserve trust.
The best part? It scales. Whether you’re handling 500 claims a month or 50,000, AI makes sure the experience is consistent, compliant, and efficient across the board.
AI in claims processing isn’t a someday story. It’s already reshaping how insurance works today. By pairing the scale and speed of machines with the expertise of human adjusters, insurers are delivering faster, smarter, and more personalized experiences.
The trick isn’t to replace people. It’s to equip them.
If you’re not looking at how AI fits into your claims operations, you’re falling behind competitors who are already seeing gains in speed, accuracy, and customer loyalty.
Want to know how real insurers are using AI today? Here’s what we’re seeing: AI Fatigue vs AI Enthusiasm: Real-World Impacts on Claims Processing.
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