An insurer is, to a large extent, a document-processing machine. Applications, policies, claim notifications, inspection reports, invoices, correspondence with customers and reinsurers, all of it still flows largely through people who read, retype and copy data between systems. Insurance automation starts exactly where a qualified specialist does work they should not be doing: manually pulling a policy number out of a PDF, or transcribing a loss report into the claims system.
In our experience the obstacle is not willingness but trust: in a regulated environment you cannot roll out a model that “usually gets it right”. That is why, for UNIQA, we built an AI assistant based on RAG and an LLM, running on the Polish PLLuM model inside one of the region’s largest insurers. We reuse the same foundation, controlled retrieval of knowledge, predictable answers and data security, to automate documents and processes.
Where does insurance lose the most time on manual document work?
The biggest waste sits where the data already exists, just in an inconvenient form. A claim arrives as a scan and ten photos, a policy as a PDF, a repair invoice as a phone snapshot. The person then becomes the interface between the document and the system: reading, classifying, keying in, checking completeness. It is repetitive, draining and error-prone work, and it is expensive, because it is done by people who should be assessing risk or handling difficult claims.
A second drain is context switching. An underwriter or claims handler hops between the policy system, email, the document repository and product knowledge. Each jump costs seconds and focus, and across thousands of cases a month that turns into real operating cost and longer turnaround for the customer.
How does AI-powered document classification and data extraction work?
This is the foundation for everything else, because most processes begin with a document that has to be understood. We combine several layers and match them to the type of content:
- OCR and intelligent document processing (IDP) turn scans and photos into text together with the page layout,
- classification recognises what we are dealing with (application, policy, report, invoice, judgment),
- document data extraction with an LLM pulls out specific fields and relationships, even from documents with variable structure,
- validation checks the result against business rules and data from source systems.
The confidence threshold is key. Every extracted field gets a reliability score, confident cases move on automatically, and anything below the threshold goes to a human. That way we do not trade quality for speed; we decide deliberately what the model may do alone and what it may not.
How do you automate claims triage in insurance?
Triage is where value shows up fastest. Instead of one queue where every notification waits the same, we build a layer that reads the report, enriches it with policy data and pre-qualifies it. Simple, complete, low-value losses can take a fast track, while complex, unusual or fraud-flagged cases go straight to the right team with the context already assembled.
The point is not to let a model “decide the payout”. The point is for the claims handler to receive a case that is already sorted, described and enriched, rather than a raw file to crack from scratch. The decision stays with the human, who simply makes it faster and on complete data.
How does an AI assistant support an underwriter?
Here we come back to what we have proven in practice. An underwriter needs knowledge scattered across documents, procedures and systems, and hunting for it by hand eats time. An assistant built on RAG (retrieval-augmented generation) fetches the right pieces of knowledge and returns an answer with its source, rather than guessing. For UNIQA we built exactly this mechanism, grounded in controlled access to knowledge. In risk assessment the same pattern can summarise an application file, surface gaps and point to the procedure clauses an underwriter relies on when making a call.
How do you automate back-office processes with workflow, RPA and AI?
AI rarely works on its own. Real savings appear when the model’s intelligence is wired into a workflow that carries a case from start to finish. So we combine three things:
- a workflow engine orders the steps, decisions and handovers between teams,
- RPA closes integration gaps where a system has no API and you have to act “like a human at the screen”,
- AI takes on what requires understanding content: classification, extraction, summarisation, pre-qualification.
In this setup the model is not a gadget but one component of a process you can monitor, version and hold accountable for results.
How do you ensure human-in-the-loop, compliance and data security?
In insurance this is not an add-on; it is the price of entry. We design solutions so that a human stays in the loop wherever a decision has material consequences, with the model acting as an assistant, not a judge. We keep traceability, where a piece of data came from and on what basis an answer was produced, along with access control over documents and knowledge.
We treat sensitive data as sensitive data. Working for UNIQA, we operated in a regulated environment with a Polish language model, which shows that security, compliance and local context can be reconciled with automation, provided they are designed in from the start rather than bolted on at the end.
What skills do we bring to insurance automation?
We build the team around a specific process, not “just in case”. Most often we bring several roles into one unit:
- AI/ML and LLM engineers for the models, RAG and data extraction,
- data engineers for integration, quality and feeding the models,
- backend developers for workflow, APIs and wiring into policy and claims systems,
- security and compliance experts, plus an architect holding the whole thing together.
We staff that team in a model matched to the stage: from augmenting your own team to a dedicated team running the implementation end to end.
How do we do it at RITS?
We do it as a partner that has already delivered AI in the regulated world of insurance. For UNIQA we implemented an AI assistant based on RAG and an LLM on the Polish PLLuM model, a solution running on the insurer’s real knowledge, with control over where answers come from. We carry that same foundation, controlled access to knowledge, predictable model behaviour and data security, over to document classification, data extraction and process automation. We start with the process that hurts most, match the technology and the team to it, and only then scale into further areas.
Where do you start automating insurance documents and processes?
Start with one high-volume, repetitive process, for example keying data from claim documents, or the first-pass qualification of notifications. Work out what it costs manually today, set the confidence threshold at which you trust automation, and design the points where a human steps in. That delivers a measurable result in a few weeks and a foundation for the next steps. If you are weighing up which model to run such an implementation in, we covered it in our article on IT engagement models.
Want to automate insurance documents and processes with AI? Let’s talk - we’ll match the approach, the technology and the team to your process.