Use Cases
Insurance Claims Processing Automation
What is Claims Processing Automation?
Claims processing automation with AI agents runs the file from first notice of loss to determination: classifying documents, extracting the loss facts, evaluating coverage against the policy, and drafting the determination with evidence linked. Adjusters decide; the agent assembles and checks.
Automate Insurance Claims Processing
A single claim file: medical records, police reports, photos, repair estimates, policy declarations, claimant correspondence, often a large packet in formats no two processors agree on. MightyBot classifies every document, extracts the data, evaluates coverage, and enforces state-specific regulations in minutes.
How MightyBot Executes
-
Document classification and extraction
Entire claim file ingested and classified. FRS canonicalization maps varied fields to a canonical schema regardless of format.
-
Policy terms evaluation
Coverage terms, exclusions, sublimits, endorsements encoded as executable logic. Same claim, same policy, same result. Deterministic.
-
State regulatory compliance
State-specific requirements enforced automatically. Timelines, notice obligations, fair claims practices. Updated in plain English by your compliance team.
-
Exception routing
High-value claims, coverage disputes, fraud indicators routed to adjusters with full context. Edge cases handled.
The Problem
Adjusters must review every document, extract facts, and cross-reference against policy language, coverage limits, deductibles, and exclusions. State-specific claims handling standards vary by jurisdiction. At volume, carriers choose between hiring more adjusters or accepting inconsistent outcomes and regulatory scrutiny. Adjusters spend much of their time gathering information rather than making coverage decisions.
Document chaos
Medical records, police reports, photos, estimates, declarations from every party in different formats
Coverage complexity
Policy terms, exclusions, sublimits, endorsement interactions per claim
State regulations
Processing timelines, documentation requirements, fair claims practices vary by jurisdiction
Consistency
Two adjusters evaluating the same claim reach different conclusions
Audit exposure
Every determination must withstand regulatory examination
Before vs After
Production Metrics
Measured in MightyBot production deployments.
Production-grade claims automation. Audit-ready from day one.
Use-case map
How Insurance Claims Processing Automation works in MightyBot
MightyBot automates insurance claims end-to-end: document classification, data extraction, policy evaluation, state regulatory compliance, and auditable adjudication outputs.
| Inputs | Claim files, policy declarations, medical records, police reports, repair estimates, photos, adjuster notes, and claimant correspondence. |
|---|---|
| Execution | Classifies documents, extracts loss and coverage data, evaluates policy terms, applies state-specific handling rules, and routes exceptions. |
| Outputs | Coverage determinations, exception packages, adjuster-ready summaries, compliance flags, and evidence-backed adjudication records. |
| Audit trail | Every decision traces to the policy clause, extracted value, source document, page, and timestamp. |
| Best for | Carriers and TPAs with high claim-file volume, multi-jurisdiction handling rules, and regulatory audit exposure. |
FAQ
Frequently Asked Questions
How do AI agents process insurance claims?
They read the claim file (forms, photos, records, correspondence), extract the facts, check coverage against the policy's actual terms, and assemble a determination package with each fact linked to its source. Complex or borderline claims route to adjusters with the work already organized.
What does claims automation with audit trails look like?
Every determination records what was extracted, which policy provisions applied, what the evaluation concluded, and who approved it. When a claim is disputed or examined, the file replays end to end instead of being reconstructed from memory.
Can MightyBot handle multi-peril claims with overlapping coverage?
The Policy Engine evaluates each peril against its applicable coverage section, handles stacking and sublimit interactions, and produces a consolidated determination with evidence pointers for each component.
How does MightyBot handle state-specific claims regulations?
State requirements are encoded as policy rules in plain English. Your compliance team authors and maintains them. When regulations change, rules are updated with version-controlled tracking and enforced by jurisdiction.
What types of claim documents does MightyBot process?
PDFs, scanned images, photos, varied form layouts, medical records, police reports, repair estimates, declarations, endorsements, adjuster notes, correspondence, and damage photos. The pipeline classifies and extracts from all of them.
Does MightyBot replace our claims management system?
No. MightyBot connects to your existing claims management system via APIs. Extraction results, determinations, and audit trails flow back into your system of record.
How does MightyBot handle fraud indicators?
Fraud rules are configured alongside coverage evaluation. Claims exhibiting defined indicators - inconsistent dates, duplicates, or suspicious patterns - are flagged and routed to your SIU with full evidence trails.
What audit trail does MightyBot produce?
Every determination links to the specific policy clause, extracted data, applicable state regulation, and source pages so auditors and regulators can verify any finding without re-reviewing the entire file.