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AI CLAIMS & SUBMISSION AUTOMATION

An AI Claims Automation Development Company Built Around the Submission, Not a Feature List.

SoluLab builds AI claims automation for carriers, MGAs, and insurtech startups — from ACORD extraction to audit trail, with a human sign-off on anything low-confidence or fraud-flagged.

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Workflow Walkthrough for AI Claims Automation Development

STEP 1 Submission Arrives STEP 2 ACORD / Extraction STEP 3 Validation & Enrichment STEP 4 Triage STEP 5 Decision STEP 6 Audit Trail
1

Submission Arrives

What arrives / happens here:
Two distinct entry points feed the same downstream pipeline: a new-business submission (broker email with ACORD forms, loss runs, statements of values attached) and a First Notice of Loss (FNOL) — a claim reported via phone, mobile app, web portal, or voice AI after a loss event.
The problem:
Submissions and FNOL reports arrive in wildly inconsistent formats — system-generated PDFs, scanned or faxed documents, handwritten sections — across channels that were never built to feed a single processing pipeline.
How we build it:
A normalized intake layer accepts submissions and FNOL reports across email, portal, API, and voice channels, and routes everything into one processing pipeline regardless of source format or entry point — so the extraction and validation logic downstream doesn't need to know or care how something arrived.
2

ACORD / Document Extraction

What arrives / happens here:
OCR combined with LLM-based extraction pulls structured fields from ACORD forms (125, 140, 126, 24, 25, and others), loss runs, statements of values, and free-text broker emails or adjuster notes.
The problem:
Structured forms and free-text narrative are genuinely different extraction problems, and marketing claims of a single flat accuracy number usually hide that. A named-insured field on a clean, system-generated ACORD form and a handwritten annotation on a faxed copy are not the same extraction task.
How we build it:
Extraction is field-level and confidence-scored, not document-level and binary — every field gets its own confidence score, structured forms and free-text are handled with different extraction logic, and low-confidence fields are flagged for review rather than silently accepted. Realistic accuracy ranges and exactly how the confidence thresholds work are covered in the Accuracy & Human-in-the-Loop section below.
3

Validation & Enrichment

What arrives / happens here:
Extracted data is cross-referenced against your policy administration system and prior submissions, then enriched with external data — property and vehicle data, business classification lookups, sanctions/OFAC screening — before it reaches an underwriter or adjuster.
The problem:
Inconsistencies (a mismatched named insured, a missing required field, a conflicting effective date) are exactly the kind of thing that either gets caught here or turns into a downstream underwriting or claims-handling problem weeks later.
How we build it:
Automated cross-referencing flags inconsistencies inline, with enrichment data attached to the submission before it's routed anywhere — so whoever reviews it next is looking at a validated, enriched record, not a raw extraction dump they have to sanity-check themselves.
Related: this validation and enrichment layer is what feeds directly into underwriting decisioning — see AI in Insurance Underwriting for how that side of the workflow works.
4

Triage

What arrives / happens here:
Every submission or claim is routed to the correct queue, line of business, or specialist based on complexity, value, and risk signals — and scored for fraud indicators in the same pass.
The problem:
Fraud detection has to happen without creating a false-positive nightmare that delays legitimate claims — over-flagging erodes trust with genuine policyholders just as fast as under-flagging costs the carrier money.
How we build it:
Risk and fraud scoring run against claim history, cross-referenced patterns, and narrative inconsistencies, producing a score attached to the case file rather than a binary block. Routing logic — which queue, which specialist, straight-through or escalated — increasingly runs on agentic systems that coordinate across multiple internal systems dynamically rather than a static rules tree.
Related: see AI Agents in Insurance for more on how agentic routing and coordination works across these systems.
5

Decision

What arrives / happens here:
Low-complexity, high-confidence cases move through straight-through processing (STP) under defined policy or claims rules. High-value, low-confidence, or fraud-flagged cases route to a human underwriter or adjuster.
The problem:
The industry-wide reality is that only a small minority of claims are currently eligible for full straight-through processing — the rest need a human, and the goal is making that human's job faster, not eliminating it.
How we build it:
Escalated cases arrive with a pre-assembled file: extracted and validated data, enrichment results, and a fraud/risk score already attached — so the underwriter or adjuster is reviewing a prepared case, not starting from a stack of raw documents.
6

Audit Trail

What arrives / happens here:
Every extraction, validation flag, triage decision, and STP-or-escalation outcome is logged immutably, with the confidence scores and model version attached to each one.
The problem:
Insurance is a heavily audited industry — state DOI exams, E&O exposure, and increasing regulatory attention on insurer use of AI all mean a decision needs to be explainable and reconstructable after the fact, not just fast in the moment.
How we build it:
An immutable, timestamped audit log ties every automated or escalated decision back to the specific data, confidence scores, and model version that produced it — the same discipline SoluLab applies to audit logging in other regulated verticals we build for, adapted here to insurance's specific audit and examination needs.

Inside Our AI-Powered Claims Automation Architecture

1 · INTAKE Email / Portal / API / Voice-FNOL Normalized into 1 pipeline 2 · EXTRACTION OCR + LLM Field-Level Confidence Structured + free-text paths 3 · VALIDATION Policy System Cross-Ref + Enrichment Property / vehicle / sanctions 4 · TRIAGE Fraud Scoring + Agentic Routing Queue / specialist assignment 5 · DECISION STP or Human Escalation Pre-assembled case file 6 · AUDIT LOG Immutable Decision Trail Timestamp + model version HUMAN REVIEW Low-confidence / high-value / fraud-flagged Feedback loop — confidence-threshold tuning & model evaluation

AI-Speed Automation. Human-Level Accuracy

What SoluLab Has Actually Measured

Our clearest published result from an insurance AI engagement is from Ambetter Health Insurance: automated underwriting processes improved accuracy by 90%, and the same engagement prevented 85% of fraudulent claims. That's a real, verifiable result — and it's an underwriting-accuracy and fraud-detection figure, not an ACORD-extraction-specific benchmark, and this page isn't going to blur that distinction.

What's Realistic Industry-Wide

Independent benchmarks and vendor-reported figures across the ACORD-extraction industry show a consistent pattern: accuracy depends heavily on document quality and field type, not a single flat number.

Document / Field Type Typical Accuracy Context
System-generated ACORD forms (clean PDFs) 99%+ Consistent across multiple independent sources
Simple text fields (names, addresses) 97–99% Field-level, production data
Numeric fields (limits, deductibles) 95–98% Field-level, production data
Dates 92–96% Varies by format consistency
Handwritten annotations / faxed copies 70–85% Highly variable by legibility and scan quality
Overall blended field-level accuracy in production 93–96% After a system has processed a few hundred forms

A realistic, defensible configuration — not a specific SoluLab-proprietary number, but the industry-standard pattern we build toward — routes by confidence score:

Confidence above ~99%:

Auto-accepted, flows straight through to validation without a manual touch.

Confidence between ~85–99%:

Flagged for review — a human confirms or corrects before the field moves downstream.

Confidence below ~85%:

Escalated immediately to manual entry or review rather than risking a wrong value entering the system.

The same logic extends downstream: any fraud-flagged claim, any high-value submission, and any case where extraction confidence was low gets a human decision-maker — extraction and automation compress the work around that decision, they don't remove it.

See How We’re Putting AI Claims Automation to Work

Ambetter Health Insurance — Real Results

SoluLab partnered with Ambetter Health Insurance to automate claims processing and strengthen fraud detection within their existing insurance operations.

85%
of fraudulent claims prevented.
70%
reduction in claims processing time.
90%
improvement in automated underwriting accuracy.
25%
increase in customer satisfaction scores.
30%
reduction in operational costs.

View Case Study

Talk Through Your Submission or Claims Workflow, Step by Step.

Book a technical discovery call. We'll walk through your actual submission or claims pipeline, where extraction and automation realistically help, and where a human should keep the final call.

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30-minute call with an engineer, not a salesperson. No obligation.

Frequently Asked Questions

What extraction accuracy can we realistically expect from ACORD automation?−
It depends on document type and field, not one number: system-generated ACORD forms typically extract at 99%+, while handwritten or faxed fields land closer to 70–85%. Blended field-level accuracy in production typically settles around 93–96% after the system has processed a few hundred forms. Low-confidence fields route to a human rather than getting silently accepted — see the Accuracy & Human-in-the-Loop section above for the full breakdown.
How does this handle FNOL differently from a new-business submission?+
Both feed the same downstream pipeline — extraction, validation, triage, decision, audit trail — but FNOL intake is built for claim-specific channels (phone, mobile app, voice AI) and feeds claims-specific validation and fraud scoring, while new-business submissions come primarily through broker email and feed underwriting-specific validation and enrichment.
Can this integrate with our existing policy administration or claims system?+
Yes — the intake, extraction, and validation layers are built to integrate with your existing policy admin and claims systems rather than requiring you to replace them. Integration scope is one of the first things we size during technical discovery.
How do you catch fraud without creating false positives that delay legitimate claims?+
Fraud and risk scoring produce a confidence-scored flag attached to the case file, not a binary block — genuinely low-risk claims still move quickly, while flagged cases route to a human reviewer with the specific risk indicators attached, rather than a blanket delay across all claims of a similar type.
What's a realistic MVP timeline for claims or submission automation?+
A single-workflow MVP (e.g., ACORD extraction plus validation for one line of business) typically takes 8–14 weeks from technical discovery to a working pilot. Multi-workflow platforms spanning extraction, triage, and decisioning across multiple lines of business take longer — sized precisely during discovery.
Do you offer this as a dedicated team or an outsourced engagement?+
Both models are available. Carriers and MGAs with an existing technical team often bring us in for a specific workflow as a dedicated build team; insurtech startups without in-house engineering capacity more often outsource the full build, sometimes starting with fractional technical leadership before a full team is scoped.
Who owns the IP and code?+
You do. Full IP ownership of the code, models, and fine-tuned artifacts built for you transfers to your company upon payment.
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