| Use case | Supported effect | Planning use |
|---|---|---|
| FNOL intake | Extracts claim form data and validates it against policy details | A sensible first pilot where claim forms arrive in consistent formats |
| Initial claim assessment | Applies predefined rules and flags discrepancies for review | Keep adjusters on flagged items and let clean claims move on |
| Claimant status updates | Bots send updates to claimants while the claim is handled | Add after intake data is reliable, so updates reflect the right claim state |
| Policy document management | Automates creation, updating and storage of policy documents | Suits back-office teams that move the same documents between systems |
| Claims decisions (PZU) | 15% increase in decisions issued per person, under two months after one process went live | Treat as one insurer’s reported result, not a forecast for your book |
| Call handling (PZU) | Average call time cut from six minutes to three minutes | Record your own baseline handling time before the pilot starts |
| Data entry (PZU) | 100% accuracy in entered data after automating data entry | Compare your error rate before and after, on the same process |
The first four rows describe effects set out in Baker Tilly’s overview of RPA in insurance operations, while the PZU rows are vendor-reported in UiPath’s PZU Insurance Group case study.
The strongest RPA use cases in insurance are rule-based, high-volume tasks such as claim intake, validation, routing and policy documents. Start with a stable process and keep people on the exceptions.
For the wider picture of tooling choices, see our hub on AI and automation tools for software teams.
Claims processing: where the effects show first
Claims work arrives as forms, policy lookups and rule checks, which is why it is the clearest target for software robots. RPA is a technology that uses software robots to automate repetitive tasks, and a claims queue is full of them. The practical consequence is that handlers receive claims that are already captured, checked and sorted, so their time goes to the cases that need judgement.
FNOL intake and validation
FNOL automation works as a short chain of rule-driven steps that replaces manual re-keying at first notice of loss. According to Baker Tilly, the chain looks like this:
- Capture: the bot extracts data from the claim form instead of a handler typing it into the claims system.
- Validate: the extracted details are checked against the policy details held on file.
- Assess: predefined rules judge whether the claim looks valid and flag any discrepancies for further review.
- Update: automated bots send status updates to the claimant, so the customer is kept informed about progress.
The decision for a team is where to draw the line between bot and person. Let the bot own capture and validation, and let a person own every flagged discrepancy. A bot that flags is useful; a bot that silently rejects is a risk you do not need in a first pilot.
Routing, data pulling and fraud triage
Once intake is dependable, the next gains come from moving claims and data to the right place. RPA can auto-route claims based on type or severity, which shortens the wait before the right handler sees a claim. It can also pull and distribute data from multiple sources, for both customer and adjuster, faster and more accurately.
The same source notes that RPA can be set up to help detect fraudulent claims. Read that as triage support: rules surface suspicious claims, and investigators decide. It suits teams that already have written fraud indicators; it does not suit teams hoping the bot will invent them.
For a model of what to automate first, UiPath’s case study lists preliminary analysis of car damage claims among the steps PZU automated. It is a bounded, repeatable step, which is the kind of claims task that makes a manageable pilot.
Policy administration and back-office work
Policy teams spend much of their time producing, correcting and filing documents, and these tasks respond well to automation. The effect is less rework between systems and fewer hand-offs where details can be mistyped.
Policy documents
Baker Tilly describes RPA as significantly enhancing policy document management by automating the creation, updating and storage of policy documents. For a back-office team, that means choosing a document workflow with a clear start and finish, such as a routine update that always follows the same steps, rather than a case that needs negotiation.
Data entry, forms and customer contact
Manual data entry is where RPA is described as an excellent solution, and it often sits underneath other jobs such as claims and policy changes. The same source says that populating data in new places or generating new forms can happen automatically once the business rules are set. That makes compliance paperwork a reasonable early candidate, provided the rules are written down before the bot is built.
PZU’s reported results in the table above show what a mature programme can aim for on accuracy and call handling. Use them as a prompt to measure your own baseline, not as a target. A phone or data-entry process with unstable rules will not reproduce another insurer’s outcome.
How the robots fit existing systems
RPA works through the same applications staff already use, which is the main reason it can start quickly. Baker Tilly states that the technology integrates with existing systems without requiring significant changes to the underlying infrastructure. For an insurer, that lowers the barrier to a pilot: the first bot does not have to wait for a platform replacement.
That strength shapes how to think about workflow automation more broadly. RPA is best at joining steps that cross several systems where the rules are fixed. When a task depends on interpreting messy content or making a judgement, a different tool may fit better. Teams weighing that choice can read AI Agents Explained: What They Are, How They Work, and Why They Matter In 2026 – Interestingdevelopment, or look at How To Build A Large Language Model: A Practical Guide For Developers In 2026 – Interestingdevelopment if language models are part of the plan.
Where to start: a phased plan
Start with the process, not the tool. Sonant notes that the most successful implementations typically begin with processes that are rule-based, high-volume, stable and a bottleneck. A process that fails the stability test is a reason to wait: fix the rules first, then automate.
- Select: score candidate processes against those four criteria and pick the one that meets all of them. FNOL intake and policy document updates usually fit the description.
- Pilot: run a small front-office and back-office pair. PZU’s rollout, described by UiPath, began with two individual pilot processes, one in the back office and one in the front office, so the team learned from both ends of the business.
- Expand: once the pilots behave, widen to adjacent areas. PZU followed its pilots with four more areas.
- Scale: move into the wider process list. PZU then added about 10 more processes, and the UiPath Platform is now in place for 5 of its critical applications, including claims and core policy handling.
The order matters more than the pace. Each step gives you a measured baseline, such as handling time or error rate, which makes the next business case easier to defend. Skipping straight to scale removes that evidence and makes problems harder to trace.
Frequently asked questions
Do RPA bots leave an audit trail?
Yes. Baker Tilly says RPA can maintain detailed logs of all actions taken, which provides an audit trail for review.
Can RPA work with legacy systems that lack APIs?
It can bridge the gap between modern applications and legacy systems, according to Sonant. The same source cautions that legacy systems may lack APIs or have limited integration capabilities, so check each system’s options before committing to a timeline.
Who should own bots once pilots start to scale?
Name an owner for each bot early. Sonant points to clear ownership and maintenance responsibilities as a requirement as pilots grow into a wider programme.
