Regure vs RPA: Insurance-native intelligence vs screen automation
RPA (UiPath, Automation Anywhere, Blue Prism) automates keystrokes in existing systems — clicking buttons, copying data between screens, filling forms. Regure provides native intelligence that understands what documents are, where they should go, and what should happen next — without brittle screen scraping.
Understanding the difference: screen automation vs document intelligence
RPA (Robotic Process Automation) tools like UiPath, Automation Anywhere, and Blue Prism automate repetitive tasks by recording and replaying human actions — clicking buttons, navigating screens, copying data from one system to another. RPA bots watch where users click and replicate those keystrokes. They're industry-agnostic: the same RPA platform automates invoice processing for manufacturing, order entry for retail, and claims data entry for insurance.
The fundamental limitation: RPA bots don't understand content. They execute scripts blindly. If a screen layout changes, bots break. If a document is slightly different than training examples, bots fail. RPA requires extensive maintenance — IT teams constantly update scripts when underlying systems change. And RPA doesn't eliminate systems — it automates keystrokes in systems you still need to maintain.
Regure is fundamentally different: it's not screen automation, it's native intelligence. The AI understands what a medical bill is, what data should be extracted, what workflow should trigger, which adjuster should be assigned — based on document content, not screen coordinates. When you change a workflow, you reconfigure Regure's visual builder — you don't rewrite bot scripts. Regure replaces manual processes with intelligent automation, not just faster keystrokes.
Feature-by-feature: screen automation vs native intelligence
RPA and Regure both automate insurance operations, but through fundamentally different architectures. One scripts keystrokes. The other understands content.
| Capability | RPA (UiPath, Automation Anywhere, Blue Prism) | Regure |
|---|---|---|
| Automation Approach | Screen automation: records mouse clicks, keyboard entries, screen coordinates — replays actions in existing systems | Native application: provides complete claims operations platform with built-in intelligence — doesn't automate other systems |
| Document Understanding | None — RPA extracts data from fixed screen positions or uses OCR coordinates. Cannot distinguish a medical bill from police report without explicit screen position mapping. | AI document classification trained on insurance: recognizes 20+ document types (ACORD forms, medical bills, police reports, repair estimates), extracts structured data, understands context |
| Insurance-Specific Knowledge | Zero — RPA is industry-agnostic. Doesn't understand insurance terms like "subrogation," "loss ratio," "deductible," "coverage limits." Scripts must be explicitly programmed for every insurance scenario. | Insurance-native: understands claims terminology, coverage types, ACORD form structures, state regulatory requirements, typical claims workflows — built-in domain knowledge |
| Workflow Configuration | Scripting required: developers write bot scripts defining exactly which buttons to click, which fields to populate, which conditions to check. Scripts are code, not configuration. | Visual workflow builder: claims managers drag-and-drop workflow steps, define conditional logic, set SLA timers — no coding required, changes deploy immediately |
| Brittleness & Maintenance | High maintenance burden: bots break when underlying systems update UI, change field positions, modify workflows. Requires ongoing developer time updating scripts — typical 20-30% of automation effort is maintenance. | Low maintenance: workflow automation is independent of external systems. Changes to integrations handled via API updates, not UI script rewrites. Continuous deployment without customer involvement. |
| System Replacement vs Enhancement | Enhancement only — RPA automates keystrokes in existing systems. You still need all underlying systems (Applied Epic, carrier portals, email, drives) — RPA just makes data entry faster. | System replacement — Regure replaces manual document handling, Excel trackers, email threads, network drives with unified platform. Reduces system count rather than adding another tool. |
| Human-in-the-Loop Scenarios | Challenges with exceptions: RPA bots follow predefined scripts. When scenarios deviate (unexpected screen popup, missing field, system timeout), bots error out and queue for human intervention. | Intelligent exception handling: AI flags uncertain document classifications for human review, workflows route edge cases to senior adjusters, system learns from corrections — graceful degradation |
| Team Collaboration | Not included — RPA automates data entry but doesn't provide collaboration tools. Teams still use email, Teams, Slack for communication. RPA doesn't consolidate systems. | Built-in claim-linked messaging, video conferencing, mobile field app — replaces separate collaboration tools with unified claim-centered communication |
| Audit Trails | Bot execution logs showing which scripts ran when — sufficient for tracking automation but not designed for insurance compliance (regulatory examination, E&O defense) | Immutable Merkle tree logging with cryptographic tamper-evidence — purpose-built for insurance compliance, regulatory audits, E&O claim defense |
| Implementation Complexity | High: 3-6 months typical implementation. Process: (1) Map existing workflows, (2) Identify automation candidates, (3) Develop bot scripts, (4) Test across scenarios, (5) Deploy with exception handling, (6) Train users on bot supervision. | 14 days standard deployment: insurance document types and claims workflows pre-configured — minimal customization required for standard operations |
| Developer Requirements | Yes — RPA requires developers or trained business analysts to script bots, maintain scripts when systems change, troubleshoot failures. Ongoing IT involvement essential. | No — claims managers configure workflows via visual builder without IT involvement. Integration via pre-built connectors, not custom code. |
| Scalability Model | Bot licensing: pay per bot instance (attended bots on user machines, unattended bots on servers). Costs scale with automation complexity and volume. Typical: $5K-15K per bot annually. | Per-user subscription: $75-225/user/month regardless of automation volume. Costs scale with team size, not automation complexity — encourages broad automation adoption. |
| Mobile Capabilities | Limited — RPA automates desktop applications. Mobile automation requires separate mobile testing frameworks (Appium) with additional complexity. | Native mobile field app: offline capability, photo capture with GPS/timestamp, signature collection, claim investigation forms — designed for field adjuster workflows |
| Integration Approach | Surface-level: RPA automates UI interactions without deep system integration. Can't access data not visible on screen. Requires systems to remain accessible via UI. | API-first: bi-directional integration with core systems (Applied Epic, Guidewire, Duck Creek) — deeper integration than UI automation allows, independent of screen design |
| Use Case Fit | Best for: high-volume repetitive data entry between systems you can't replace (legacy mainframes, vendor portals without APIs, multiple disconnected systems requiring manual data copying) | Best for: insurance claims operations needing document intelligence, workflow automation, team collaboration — where you can replace manual processes with purpose-built platform |
| Total Cost of Ownership (Year 1) | $100K-300K: Bot licensing ($5K-15K per bot × 5-10 bots), implementation services ($50K-150K for process mapping and scripting), ongoing maintenance (20-30% of implementation cost annually) | $45K-135K: Per-user subscription ($75-225/user/month × 5 users × 12 months) with no implementation fees, no bot licensing, no maintenance contracts |
| Best For | Organizations with legacy systems that can't be replaced, requiring high-volume data entry automation between disconnected systems — where UI automation is only option | Insurance operations ready to replace manual processes with intelligent platform — document classification, workflow automation, compliance — without maintaining legacy systems via bots |
Who should choose what — an honest assessment
RPA and Regure solve different problems. RPA automates existing systems you can't replace. Regure replaces systems with intelligent alternatives.
Choose RPA if:
- You're locked into legacy mainframe systems without APIs that you cannot replace for regulatory, contractual, or political reasons
- Your automation need is primarily high-volume data entry between disconnected systems (copying claim data from carrier portal to your AMS)
- You have dedicated IT staff or RPA developers to build, maintain, and troubleshoot bot scripts when systems change
- Your core systems work well — you just need to eliminate manual data entry between them without changing workflows
- You're automating processes outside claims operations where insurance-specific features aren't relevant (accounting, HR, IT provisioning)
- You have budget for ongoing maintenance (20-30% of implementation cost annually) to update scripts when underlying systems change
- Your automation ROI comes primarily from eliminating data entry labor — not from workflow intelligence or document understanding
RPA excels at automating the un-automatable — legacy systems without APIs, vendor portals you can't control, disconnected applications requiring manual data copying. If system replacement isn't an option, RPA is valuable.
Choose Regure if:
- You need AI document intelligence that understands insurance forms — not just screen scraping fixed positions
- You want to replace manual processes (email attachments, network drives, Excel trackers) with intelligent platform — not automate legacy systems
- Your team consists of claims professionals, not RPA developers — you need tools they can configure without scripting
- You need visual workflow automation that claims managers configure in hours, not developers script over months
- You want low maintenance burden — automation that doesn't break when external systems update
- You need claim-linked collaboration and mobile field tools in addition to document automation
- Your compliance requirements demand insurance-specific audit trails (regulatory examination, E&O defense) — not just bot execution logs
- You want predictable per-user pricing instead of per-bot licensing with complex volume-based tiers
Regure provides native intelligence instead of screen automation. If you can replace manual systems, purpose-built automation delivers better ROI. Calculate your savings.
Can Regure and RPA work together?
Yes, and many insurance operations use both — RPA for legacy system automation, Regure for claims intelligence and collaboration.
Common architecture: RPA bots extract claim data from carrier portals (which lack APIs) and push to Applied Epic. Regure integrates with Applied Epic via API to pull claim data for document processing and workflow automation. Adjusters work in Regure for document-heavy workflows and team collaboration. RPA handles data entry that can't be eliminated. Regure handles operations that benefit from intelligence and collaboration.
This hybrid approach makes sense when you have: (1) Legacy systems you can't replace that require RPA for data extraction, (2) Claims operations that need document intelligence and collaboration beyond what RPA provides. RPA fills gaps between systems without APIs. Regure provides the intelligence layer for document understanding, workflow orchestration, and team coordination — capabilities RPA doesn't offer.
Strategic evolution: many organizations start with RPA to automate legacy system data entry, then add Regure for claims operations layer. Over time, as legacy systems retire or vendors add APIs, RPA footprint shrinks while Regure footprint grows — replacing brittle screen automation with native intelligence where possible.
What buyers ask when evaluating RPA vs insurance-specific automation
Can RPA replace Regure for claims automation?
Only partially. RPA can automate data entry from documents into your claims system — but it can't intelligently classify documents (it follows scripts to click fields, not understand content), can't provide claim-linked collaboration, can't offer mobile field tools, and requires extensive maintenance when systems change. If your only need is data entry automation and you have IT staff to maintain bot scripts, RPA might suffice. If you need document intelligence, workflow orchestration, collaboration, and compliance features, Regure provides capabilities RPA doesn't offer.
What happens when RPA bots break? How often does this occur?
Bot failures are common: underlying system UI changes (buttons move, new popup appears, field names change), system performance degrades (timeouts increase, pages load slower causing bots to click before elements render), unexpected data variations (field contains unexpected format). Industry data: 20-30% of RPA implementation effort is ongoing maintenance. Organizations typically budget 1 FTE RPA developer per 10-15 bots for maintenance and troubleshooting. Regure's workflow automation is independent of external UIs — changes to integrations handled via API without workflow reconfiguration.
Can we use RPA to automate document classification instead of Regure AI?
Technically yes, but limitations are significant. RPA can route documents based on file names or folder locations (if adjuster manually files into correct folder), or integrate with separate OCR tools to extract text then run keyword matching (if "police" in document text, assume police report). This approach lacks Regure's semantic understanding — RPA can't distinguish between a police report about a claim and a claims file that mentions police. RPA also can't learn from corrections or handle edge cases gracefully. Result: 70-85% accuracy with RPA keyword matching vs 99%+ with Regure insurance-trained AI.
What's the real total cost comparison?
RPA: Bot licensing ($5K-15K per bot × 5-10 bots = $25K-150K annually), implementation services ($50K-150K for process discovery and scripting), ongoing maintenance (20-30% of implementation = $10K-45K annually), RPA developer headcount ($80K-120K salary for 1 FTE to maintain bots). Total year 1: $165K-465K. Regure: $75-225/user/month × 5 users = $4.5K-13.5K monthly = $54K-162K annually, no implementation fees, no maintenance contracts, no dedicated developer required. Year 1 savings: $100K-300K with Regure. Calculate your specific scenario.
When does RPA make more sense than Regure?
Three scenarios: (1) You're locked into legacy mainframe claims systems without APIs that you cannot replace — RPA is only automation option, (2) Your automation needs are primarily outside claims operations (RPA for HR onboarding, IT provisioning, accounting) where insurance-specific features aren't relevant, (3) You have existing RPA infrastructure and expertise from other departments — marginal cost to add insurance bot scripts is lower than standalone platform. If you're starting fresh and can replace manual systems, Regure provides better insurance-specific ROI.
See native intelligence vs screen automation
Upload sample claims documents during a 20-minute demo. We'll show you AI document classification, workflow automation without scripts, claim-linked collaboration — and discuss whether native intelligence or RPA fits your architecture better.