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🤖 '''Robotic process automation (RPA)''' refers to the use of software "bots" that mimic human interactions with digital systems to automate repetitive, rules-based tasks across insurance operations. In an industry still heavily reliant on legacy [[Definition:Policy administration system | policy administration systems]], manual data entry, and multi-step workflows, RPA has become one of the fastest-adopted [[Definition:Insurtech | insurtech]] tools because it sits on top of existing applications without requiring deep integration or replacement of core platforms. Carriers, [[Definition:Managing general agent (MGA) | MGAs]], and [[Definition:Third-party administrator (TPA) | TPAs]] deploy RPA across functions ranging from [[Definition:Underwriting | underwriting]] and [[Definition:Claims processing | claims processing]] to [[Definition:Regulatory compliance | regulatory reporting]] and [[Definition:Policy issuance | policy issuance]].

⚙️ A typical insurance RPA deployment begins by mapping a manual process — say, extracting data from emailed [[Definition:Loss run | loss runs]], keying it into an underwriting workbench, and cross-referencing it against [[Definition:Pricing model | pricing models]]. Developers then configure bots to replicate each step: opening emails, parsing documents (often aided by [[Definition:Optical character recognition (OCR) | OCR]] or [[Definition:Natural language processing (NLP) | NLP]]), entering values into the target system, running validation checks, and flagging exceptions for human review. Because bots follow deterministic rules, they execute with consistent accuracy and can operate around the clock, dramatically compressing cycle times. More sophisticated implementations layer RPA with [[Definition:Artificial intelligence (AI) | AI]] and [[Definition:Machine learning (ML) | machine learning]] to handle semi-structured inputs like handwritten [[Definition:First notice of loss (FNOL) | FNOL]] forms or variable document layouts — a hybrid often called intelligent automation.

📈 The business case for RPA in insurance is compelling beyond simple labor savings. By reducing manual touchpoints, insurers lower [[Definition:Operational risk | operational risk]] and [[Definition:Errors and omissions (E&O) | E&O]] exposure while freeing skilled underwriters and [[Definition:Claims adjuster | adjusters]] to focus on complex judgment calls. [[Definition:Regulatory compliance | Compliance]] teams benefit because bots generate detailed audit trails for every transaction, simplifying examinations by state [[Definition:Insurance regulator | regulators]]. However, RPA is not a silver bullet — poorly documented processes automated without redesign simply speed up inefficiency. Leading insurers treat RPA as a gateway technology: once they have stabilized and automated a process, they pursue deeper transformation through [[Definition:Application programming interface (API) | API]]-based integrations and end-to-end digital platforms, ensuring that automation evolves alongside the business.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Machine learning (ML)]]
* [[Definition:Insurtech]]
* [[Definition:Straight-through processing (STP)]]
* [[Definition:Optical character recognition (OCR)]]
* [[Definition:Digital transformation]]
{{Div col end}}

Definition:Robotic process automation (RPA) - Revision history

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