How does AI underwriting work? A step-by-step guide

AI underwriting works by routing a broker submission through a sequence of specialist AI agents — each handling one job (parsing, risk analysis, flood scoring, compliance checking, memo synthesis) — and returning a structured decision pack to the underwriter within minutes. The human underwriter reviews, adjusts, and approves the bind decision; the AI handles every mechanical and analytical step before that moment.

Key Takeaways

• The pipeline has five main stages: intake, parse, specialist analysis, memo synthesis, and human decision
• Specialist agents run in parallel during the analysis stage, cutting total pipeline time to under two minutes
• Every step produces a structured output the next step consumes — the system is a data pipeline, not a chat session
• The underwriter's role shifts from data assembly to decision review — higher-value work on every submission
• [Request a demo](/demo) to see the full pipeline on a live submission from your book

Why the traditional process is slow

Before walking through the AI pipeline, it is worth being precise about what makes manual underwriting slow. It is not that underwriters are slow thinkers. It is that the average commercial submission requires an underwriter to:

• Open a broker email and download 3–7 attachments
• Extract 20–40 structured fields from unstructured PDFs
• Look up flood zone, wind zone, and wildfire exposure for each location separately
• Check treaty attachment points against the proposed TIV
• Cross-reference OFAC sanctions manually
• Write a memo from scratch synthesising all of the above

Industry benchmarks put this at 35–55 minutes per submission for a mid-complexity commercial property risk. Most of that time is mechanical data assembly, not judgment. AI underwriting attacks exactly that portion.

Step 1 — Submission intake

The pipeline starts when a submission arrives. In practice this means one of three things: a broker emails a PDF to a monitored inbox, uploads via a broker portal, or pushes structured data through an API integration.

The intake layer does three things immediately:

• Deduplication: Is this submission already in the queue? Brokers often send the same risk to multiple MGA contacts.
• Classification: What line of business is this? Commercial property, casualty, E&O, management liability? The classification determines which specialist agents are activated downstream.
• Triage scoring: Is this risk within appetite at first glance? Submissions clearly outside appetite (e.g., occupancy class the MGA has explicitly excluded) can be flagged immediately rather than consuming a full pipeline run.

Intake typically takes 2–5 seconds. The output is a normalised submission record with a line-of-business tag and an appetite-check score.

Step 2 — Document parsing

The parser agent receives the raw submission package — PDFs, Excel SOVs, prior loss runs, inspection reports — and returns structured JSON.

This is not OCR. Modern LLM-based parsers understand document semantics, not just character recognition. They can:

• Extract a statement of values from a non-standard Excel template
• Parse a narrative loss run and return structured prior-loss records
• Identify which attachments are relevant (the slip vs. the marketing brochure)
• Flag missing fields the underwriter needs before analysis can continue

Parser output is a schema-validated JSON object: insured name, address list, occupancy codes, construction class, year built, TIV per location, prior losses, requested coverage terms, and any broker notes flagged as underwriting-relevant.

Parsing takes 10–25 seconds depending on submission complexity. All downstream agents consume this structured output — none of them read the raw PDF.

Step 3 — Specialist parallel analysis

This is the most important stage and the one that differentiates agentic AI from single-model approaches. Multiple specialist agents run in parallel, each working on a different dimension of the risk.

Risk agent reasons about the core hazard profile: occupancy class, construction vintage, loss history trends, industry-specific exposures. Returns a risk narrative with confidence scores and underwriting flags.

Flood agent geocodes each insured location and calls external APIs — FEMA National Flood Hazard Layer, NOAA storm surge, USGS elevation — to return flood zone, base flood elevation, and modelled annual loss for each location. This agent is tool-heavy; its output is grounded in external data, not inference.

Catastrophe / wildfire agent (for property books) checks each location against wildfire risk models, calculates probable maximum loss under CAT scenarios, and flags any locations exceeding the portfolio's CAT exposure guidelines.

Compliance agent runs OFAC SDN screening on the insured entity, checks state filing requirements, verifies the proposed policy form is approved in relevant jurisdictions, and flags any regulatory exclusions.

Treaty agent checks the proposed risk against the MGA's treaty parameters: attachment points, per-risk limits, excluded occupancy classes, and aggregate accumulation constraints. This prevents the underwriter from binding a risk the treaty won't support.

These five agents run simultaneously. Total time for parallel analysis: 15–40 seconds, dominated by the external API calls in the flood and CAT agents. Without parallelism, sequential execution of the same five agents would take 75–200 seconds.

Step 4 — Memo synthesis

Once all specialist agents have returned their outputs, the memo synthesis agent aggregates them into a structured underwriting memo. This is the only step that produces human-readable prose; all previous steps produce structured JSON.

The memo includes:

• Executive summary (2–3 sentences, written for a reinsurer or senior underwriter)
• Per-peril analysis (one section per active specialist agent, citing the agent's output)
• Pricing rationale (base rate, loadings, credits, resulting premium)
• Proposed subjectivities and exclusions
• Bind recommendation with confidence level
• Open questions for the broker (missing information flagged by the parser)

The synthesis agent does not invent data. It is explicitly prompted to cite only information that appears in upstream agent outputs. Any gap in agent coverage becomes a gap in the memo — which is correct behaviour; the underwriter needs to see where the analysis is incomplete.

Memo synthesis takes 15–30 seconds. The total pipeline time from intake to memo is typically 45–90 seconds for a mid-complexity commercial property risk.

Step 5 — Human decision

The pipeline terminates with a structured decision pack delivered to the underwriter's queue. The underwriter sees:

• The synthesised memo
• Per-agent evidence panels they can expand
• A pricing worksheet they can adjust
• A subjectivities checklist
• One-click actions: bind, refer, decline, or request-more-information

The underwriter is not approving a black-box recommendation. They are reviewing a transparent, cited analysis and applying their judgment to the bind decision. The AI eliminated the mechanical assembly work; the underwriter contributes the judgment that the AI cannot reliably provide.

This is the correct division of labour. Insurers that automate the bind decision itself expose themselves to regulatory and reputational risk. Insurers that automate the analysis and preserve the human bind decision get the efficiency gains without the liability.

What happens after the bind decision

Post-bind, the pipeline packages the full decision record — submission, parser output, all agent outputs, memo, and the underwriter's decision and any manual adjustments — into an audit pack that satisfies delegated underwriting authority reporting requirements. This pack is stored and retrievable for state DOI examination, reinsurer audits, and internal governance reviews.

How Vortic approaches this

The pipeline described above is how [Vortic's platform](/platform) works in production. The [demo](/demo) runs a live submission through all five stages so you can see the agent traces, the timing breakdown, and the final decision pack.

For background on the agentic architecture that makes parallel specialist analysis possible, see [what is agentic AI in insurance](/blog/what-is-agentic-ai-in-insurance).