How to Integrate AI Into an Insurance Core System

Don’t rip out your policy admin or claims platform to do it. The carriers seeing real results add AI as a governed layer around the core through APIs and middleware, and they start with the document-heavy work that already slows their teams down. That keeps integration risk low, makes the payback easy to measure, and keeps regulators satisfied.

That last point matters more than the pilot count. Almost every insurer says it’s doing something with AI. The NAIC’s line-of-business surveys put it at 88% of auto insurers and 92% of health insurers either using or planning to use AI and machine learning.

But Capgemini’s World Property & Casualty Insurance Report 2026 found only about 10% have actually scaled it, and 42% track no AI metrics at all. The space between “we’re experimenting” and “it’s in production” is where most programs get stuck. Here’s how to get across it.

Start where the documents pile up

Insurance is a paper business wearing a digital coat. Loss runs, ACORD forms, medical records, police reports, broker emails, statements of value. Accenture estimates roughly 80% of enterprise data is unstructured, and McKinsey found that 30 to 40% of a commercial underwriter’s time goes to admin work like rekeying data from those documents. That’s expensive idle time, sitting in plain sight.

So when you ask how AI can be used in the insurance industry, the first answer usually isn’t fraud scoring or dynamic pricing. It’s the boring, high-volume document handling.

Intelligent document processing reads structured and unstructured files, pulls out the fields you need, and passes them to a person to confirm before anything touches the core.

It runs next to your platform instead of inside it. That’s exactly why it’s the safest place to begin: you get a measurable win without going near the system of record.

The hardest version of this is the loss run. There’s no standard format. Every carrier names policy types, lines of business, and claim categories its own way, and a single workers’ comp loss run can run well past 100 pages.

Extracting loss data from those non-normalized formats and converting it into one common structure is genuinely difficult, which is why so many teams still do it by hand. This is the kind of work specialist providers have built around.

Kumaran Systems’ cognitive document processing service, for example, pulls loss data from complex non-normalized loss run formats across carriers and converts it into a common form, then auto-indexes key value pairs from large unstructured documents. The result is verified frequency and severity an underwriter can price against, instead of a folder of PDFs to read line by line.

Get this layer right and two things happen. Cycle times drop on work nobody enjoys, and you build the clean, normalized data that every later AI use case depends on.

Then add the use cases that move loss ratios

Once intake is solid, the rest of the map opens up.

Claims is the obvious next domain. Aviva built more than 80 AI models across its motor claims operation and reported saving over 60 million pounds in 2024, while cutting complex liability assessment time by 23 days and complaints by 65%, per McKinsey’s case work.

Submission intake is another: tools that read and summarize broker submissions let underwriters spend their time judging risk rather than retyping it, and EY found 74% of insurers are prioritizing predictive analytics in underwriting and claims.

Fraud detection rounds it out, with US insurance fraud estimated at 308.6 billion dollars a year by the Coalition Against Insurance Fraud, which is why so many carriers now run multimodal checks across text, images, and video.

The pattern across all three: AI earns its keep when it reads, classifies, and pre-fills, and a human still makes the call.

How to actually connect it to the core

This is where most “how to implement AI in insurance” plans go wrong. They treat it as a platform replacement. It isn’t, and it shouldn’t be.

Your Guidewire, Duck Creek, Sapiens, or mainframe environment carries decades of business logic, and a large share of IT budgets already goes to keeping it running. Wrap that core in a controlled API layer and let the AI components talk to it through defined integration points rather than reaching into it. A few practical moves that keep the lights on while you build:

• Use APIs, message queues, and change data capture so the AI layer reads and writes through stable contracts, not direct database calls.
• Run new models in shadow mode first. Let them score real work in the background and compare against current outcomes before anything goes live.
• Keep the human-in-the-loop checkpoint on every output that affects a customer decision. That’s your accuracy backstop and your audit trail in one.

This approach also lets you swap models as they improve without re-engineering the core each time. The core stays stable. The intelligence around it evolves.

Don’t skip the governance, or it skips you

AI in insurance is regulated whether you plan for it or not. The NAIC Model Bulletin, adopted in December 2023 and now picked up by most states, expects a written AI program covering governance, bias testing, and oversight of third-party vendors. In Europe, the EU AI Act classifies AI used for risk assessment and pricing in life and health insurance as high risk, with the core obligations applying from August 2, 2026. New York, Colorado, and California have added their own rules on bias testing and automated decisions.

None of this should slow you down if you build it in from the start. Keep humans on adverse decisions, log how each model reached its output, and hold vendor documentation you could hand a regulator tomorrow. The carriers that treat governance as part of the build, not a later clean-up, are the ones who get to scale. The 42% tracking no metrics are usually the ones who stall.

A sequence that works

You don’t need a three-year roadmap to start. You need a first win and a way to repeat it.

In the first six months, pick one document-heavy workflow with obvious pain, loss run extraction or submission intake are good candidates, and run a proof of value on your own documents. Set hard numbers up front: processing time saved, straight-through rate, field-level accuracy, cost per item. Scale it only when it clears a real bar, say a 30% cycle-time cut at 95% accuracy with outputs you can audit.

Over the following year, industrialize the data pipelines feeding those models and stand up the governance program properly. Then reuse what works across claims, underwriting, and service, and let AI help with the core modernization itself.

The carriers pulling ahead aren’t the ones with the most pilots. They are the ones who picked an unglamorous, high-volume problem, proved it paid, and built the next thing on top of it. Start with the documents. The rest follows.