RPA Bot Rescue: Claims Pre-Adjudication Orchestration with Microsoft Copilot

1. Problem / Context

Claims pre-adjudication is a perfect storm for brittle RPA: variable documents, inconsistent data quality, changing business rules, and heavy compliance requirements. Mid-market insurers and health payers often rely on screen-scraping bots to move data from inboxes into core systems. Those bots break on minor UI changes, struggle with unexpected attachments, and can’t reason about edge cases like missing prior authorizations or conflicting policy details. The result is rework, leakage, and delays in first-contact resolutions—exactly where customer experience and loss containment hinge.

Meanwhile, regulatory pressure keeps rising. Every claim touches sensitive PII/PHI, adjuster decisions must be auditable, and fraud surveillance must be defensible. Lean teams can’t babysit brittle automations. They need governed agentic workflows that ingest, validate, decide, and route with human oversight built in. This is where Microsoft Copilot—paired with a governance-first approach—can replace fragile RPA with resilient, API-driven orchestration.

2. Key Definitions & Concepts

• Claims pre-adjudication: The checks performed before a claim moves to full adjudication—entity extraction, eligibility verification, policy and coverage validation, prior authorization checks, fraud screening, preliminary liability estimation, and routing.
• FNOL (First Notice of Loss): The initial claim intake package, often coming via email or portal with PDFs, images, forms, and free text.
• Agentic orchestration: AI-driven workflows that can perceive inputs, reason with policies, take actions via APIs, and hand off to people when required.
• Microsoft Copilot & Copilot Studio: Tools to build skills and prompts that connect to enterprise systems via connectors/APIs, enabling governed, auditable assistance and automation.
• Human-in-the-loop (HITL): Adjusters and supervisors review proposed dispositions, edit outreach, and approve high-dollar or suspected fraud escalations.

3. Why This Matters for Mid-Market Regulated Firms

Mid-market carriers and payers operate under the same regulatory microscope as larger peers but with tighter budgets and smaller teams. Pre-adjudication is a major driver of cost-to-serve and customer experience. Replacing brittle bots with governed agentic workflows reduces exception handling, accelerates cycle time, and strengthens audit readiness. It also frees scarce talent to focus on complex investigations and policyholder communication instead of rekeying data.

For organizations with $50M–$300M in revenue, the priority is safe productivity gains—not buzzwords. A Copilot-led approach replaces UI scraping with APIs, centralizes audit trails, enforces role-based access, and adapts to policy changes via prompts rather than code rewrites. The outcome is speed with control.

4. Practical Implementation Steps / Roadmap

1) Intake and parsing

• Copilot ingests FNOL and supplemental documents from email and portals, normalizes file types, and extracts entities (claimant, policy number, dates of loss/service, providers, locations, amounts).

2) Eligibility and policy validation

• Via core system APIs (e.g., Guidewire/Duck Creek for P&C, or core admin for health), Copilot validates policy status, coverage limits, deductibles, and—where relevant—prior authorizations.

3) Cross-checks and consistency checks

• The workflow flags inconsistencies (e.g., coverage dates not matching date of loss, missing provider NPI, mismatched VIN or ICD-10 code) and compiles a traceable checklist.

4) Preliminary liability and scoring

• Using policy prompts that express business rules, Copilot computes a preliminary liability or coverage likelihood and identifies documentation gaps to resolve.

5) Fraud signals

• External fraud services are called (e.g., device reputation, identity verification, claim pattern checks) to produce a risk score. Thresholds drive routing to SIU or fast-track.

6) Draft outreach and routing

• Copilot drafts claimant or provider outreach emails to request missing information. It proposes routing: fast-track, standard, or SIU escalation.

7) Human-in-the-loop reviews

• Adjusters review the proposed disposition, edit communications, and approve. High-dollar or suspected fraud escalations route to supervisors via Teams Approvals.

8) System updates and audit

• All actions, prompts, and decisions log to Dataverse for audit. Purview applies sensitivity labels for PII/PHI. Retention policies store artifacts in SharePoint. Access is enforced with Entra ID RBAC.

9) Monitoring and rollback

• Model and rule drift are tracked. On anomalies, the system falls back to human-only mode and can roll back to prior decision plans.

10) RPA to agentic upgrade

• Screen-scraping bots are deprecated in favor of APIs and Microsoft Graph connectors. The orchestration tolerates variable document sets and adapts rules through versioned policy prompts instead of brittle UI flows.

[IMAGE SLOT: agentic AI workflow diagram showing email/portal intake, entity extraction, core system APIs (Guidewire/Duck Creek), fraud APIs, Teams Approvals, Dataverse audit, and SharePoint retention]

Kriv AI often implements this blueprint with Copilot Studio skills, prebuilt connectors, and claims dashboards—providing a governed backbone so lean teams can operate confidently.

5. Governance, Compliance & Risk Controls Needed

• Data protection: Apply Microsoft Purview sensitivity labels for PII/PHI at ingestion; enforce data loss prevention; require encryption at rest/in transit.
• Identity and access: Use Entra ID for RBAC and conditional access. Limit access to claim artifacts by role and case.
• Auditability: Log all AI prompts, decisions, and human approvals in Dataverse. Capture versions of policy prompts and model configurations for traceability.
• Records and retention: Store claim artifacts and generated communications in SharePoint with retention schedules aligned to regulatory requirements.
• Model and rule risk: Establish change management for prompt and rule updates, with pre-production testing and signoff. Monitor drift, bias, and performance; implement rollback and “safe mode” procedures.
• Vendor lock-in and portability: Prefer API-first integrations and standards-based data exports; document connectors and mappings so workflows remain portable across systems.

[IMAGE SLOT: governance and compliance control map with Purview labels, Entra ID RBAC, Dataverse audit logs, and SharePoint retention lifecycle]

Kriv AI’s governed approach emphasizes auditable workflows and human oversight, helping mid-market firms satisfy auditors without slowing the business.

6. ROI & Metrics

Executives should demand measurable impact within one to two quarters. Practical metrics include:

• Cycle time: Reduce pre-adjudication turnaround from 24–72 hours to same-day in most cases. Track median and 90th percentile times.
• Error rate: Lower data-entry and eligibility-check errors by 30–50% through API validation and HITL review.
• Leakage and overpayment prevention: Increase early detection of ineligible claims or missing prior authorizations; quantify avoided payouts.
• Fraud triage effectiveness: Measure SIU routing precision (precision/recall) and average time-to-flag suspicious claims.
• Labor efficiency: Track claims per adjuster per week; quantify time saved from automated parsing and outreach drafts.
• Payback period: Combine labor savings, avoided leakage, and improved customer retention to target a 3–6 month payback for mid-market volumes.

Example: A regional P&C carrier processing 12,000 claims/year replaced five screen-scraping bots with Copilot-led orchestration. Pre-adjudication cycle time dropped from 36 hours to 6 hours, SIU routing precision improved 20%, and two FTEs were redirected to complex investigations—producing payback in under five months while strengthening audit readiness.

[IMAGE SLOT: ROI dashboard visualizing cycle-time reduction, error rates, SIU routing precision, and payback period]

7. Common Pitfalls & How to Avoid Them

• Lifting-and-shifting RPA logic: Rebuilding brittle UI steps defeats the purpose. Redesign around APIs, policy prompts, and connectors.
• Skipping HITL: Without adjuster and supervisor checkpoints, risk tolerance is misaligned. Wire Teams Approvals and enforce thresholds.
• Weak document coverage: FNOL packages vary. Train extractors on realistic samples; include fallback to manual classification.
• No drift monitoring: Rules and models change in the real world. Monitor performance and trigger safe-mode fallback and rollback when anomalies appear.
• Unversioned prompts: Treat prompts as policy artifacts—version, test, and approve changes.
• Incomplete audit: If prompts, decisions, and approvals aren’t logged, audits will fail. Centralize in Dataverse with retention in SharePoint.

30/60/90-Day Start Plan

First 30 Days

• Discovery: Inventory pre-adjudication workflows across intake, eligibility, policy checks, prior authorization, fraud screening, and routing.
• Systems mapping: Identify core systems (e.g., Guidewire/Duck Creek or health admin), email/portal sources, and existing RPA scripts to retire.
• Data and security checks: Classify PII/PHI, define Purview labels, confirm Entra ID roles, and outline SharePoint retention policies.
• Governance boundaries: Define HITL checkpoints, approval thresholds, audit fields for Dataverse, and change-management steps for prompts.

Days 31–60

• Pilot build: Implement Copilot Studio skills for intake parsing, policy/eligibility validation via APIs, fraud scoring, and outreach drafting.
• Orchestration: Connect Teams Approvals for adjuster/supervisor reviews; configure routing (fast-track vs SIU) with documented thresholds.
• Security controls: Enforce Purview, RBAC, and DLP policies; start end-to-end audit logging to Dataverse; store artifacts in SharePoint.
• Evaluation: Run parallel with current process; compare cycle time, error rate, and SIU precision; collect adjuster feedback.

Days 61–90

• Scale: Gradually deprecate RPA screen-scraping; expand coverage to more claim types and document formats.
• Monitoring: Stand up drift dashboards; define rollback triggers and human-only fallback protocols.
• Metrics and reporting: Publish ROI dashboards; align incentives and SLAs; finalize a playbook for prompt versioning and approvals.
• Stakeholder alignment: Brief compliance, SIU, IT, and business leaders; agree on a quarterly change window and audit cadence.

[IMAGE SLOT: human-in-the-loop review screen with adjuster approvals, SIU escalation, and audit trail snippets]

9. (Optional) Industry-Specific Considerations

• Property & Casualty: Heavier emphasis on FNOL variety (photos, police reports, telematics), VIN checks, and preliminary liability estimation. Core connectors often target Guidewire or Duck Creek.
• Health Payer: Prior authorization and coverage rules are central; ensure secure handling of PHI, EDI mapping (e.g., 837/835), and provider credential checks. Outreach drafts may target providers rather than claimants.

10. Conclusion / Next Steps

Replacing brittle RPA with Microsoft Copilot–led agentic orchestration turns pre-adjudication into a governed, auditable, and efficient flow that scales with your business. By combining API-first integrations, human-in-the-loop approvals, and end-to-end controls, mid-market organizations can cut cycle time, reduce leakage, and strengthen compliance—all with teams they already have.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone. As a governed AI and agentic automation partner, Kriv AI helps with data readiness, MLOps, and workflow orchestration so your Copilot initiatives move from pilot to production with confidence.