Public-entity excess is law-enforcement liability, auto and a self-insured retention. The submission is municipal GL and WC schedules by function, facilities, and a loss run led by law-enforcement and fleet claims over a $25M lead. We ran a real regional authority, Tri-County Regional Authority, through InsightXtract.
Why public entity is an excess casualty risk
A public entity's excess sits over a self-insured retention and a lead umbrella, covering a broad book — public works, parks, utilities, transit, and law enforcement. Law-enforcement liability (civil-rights claims) and fleet exposure drive severity; sovereign-immunity considerations shape the structure. The retention, the SIR erosion and the by-function exposure all have to be read cleanly.
The submission packet
A real public entity placement is rarely one clean file. This one is five, in four different shapes — the everyday reality an underwriting team has to re-key by hand today:
- broker_email.pdf — the renewal narrative: account description, requested limits, exposure snapshot, and the excess-layer options to quote.
- rfq.pdf — the umbrella/excess application: general information, coverage requested, and the exposure bases.
- exposure_workbook.xlsx — the heart of the file: municipal GL exposure by function (public works, parks, utilities, transit), a facility schedule, WC payroll by class, and an entity schedule.
- loss_run.pdf — five years of currently-valued losses, led by law-enforcement civil-rights claims and fleet collisions (44 claims).
- schedule_of_underlying.pdf — the primary casualty and lead umbrella the excess attaches over.
Step 1 — Classification
Every file is routed to a document type first — evidence-bearing, with a confidence, not a black-box label:
Step 2 — Coverage structure, with citations
The requested tower is read from the email and application, every value grounded to its page:
| Coverage | Requested | Source |
|---|---|---|
| General Liability — each occurrence | $1,000,000 | rfq p.2 |
| General Aggregate | $5,000,000 | rfq p.2 |
| Products / Completed-Ops Aggregate | $5,000,000 | rfq p.2 |
| Commercial Auto — CSL | $1,000,000 | rfq p.3 |
| Employers Liability | $1,000,000 | rfq p.3 |
| Lead Umbrella | $25M | email p.1 |
Step 3 — Exposure schedules, typed & normalized
The workbook is where template OCR falls over — many sheets, hundreds of rows, broker-specific wording. InsightXtract reads every sheet, maps each to a typed schedule, and normalizes the codes against governed glossaries. A slice of the GL exposure schedule:
| State | WC Code | Description | Revenue | Payroll |
|---|---|---|---|---|
| IL us_state_codes | 66600 ncci | Municipal operations - general | $22,400,000 | $4,032,000 |
| IL us_state_codes | 48925 ncci | Parks & recreation | $8,900,000 | $1,602,000 |
| IL us_state_codes | 98090 ncci | Public works / streets | $12,700,000 | $2,286,000 |
| … 38 GL rows · workers_compensation 50 · named_insured_mix 12 · location_list 22 | ||||
Column bindings: state → us_state_codes, class → ncci_wc_class_codes / iso_gl_class_codes, occupancy → occupancy_types. Out-of-vocabulary values are flagged by validation, not silently kept — and fuzzy column resolution means a rule written for payroll still binds a broker’s “Total Payroll” column.
Step 4 — Derived exposures (deterministic, not guessed)
The account is rated on totals. Rather than ask a model to eyeball-sum hundreds of rows, the document type declares deterministic sum / count / group_by rules that reduce the extracted schedules exactly — the same numbers every run, each traceable to its source table:
Step 5 — The loss run
A municipal loss run is led by law-enforcement civil-rights claims and fleet collisions, with premises and sidewalk/road-defect claims and a public-works WC book. Every claim is extracted — coverage line, status and cause normalized — and rolled up, with a by-line split for the loss pick:
| Claim # | Coverage | Cause | Status | Incurred |
|---|---|---|---|---|
| TRI-1110 | GL coverage_lines | Law enforcement - civil rights | Open claim_statuses | $560,000 |
| TRI-1088 | AL coverage_lines | Fleet collision | Open claim_statuses | $310,000 |
| TRI-1051 | GL coverage_lines | Sidewalk/road defect | Closed claim_statuses | $88,000 |
| TRI-1033 | WC coverage_lines | Public-works injury | Closed claim_statuses | $120,000 |
| … 44 claims across 5 policy years, currently valued | ||||
What the underwriter reads first
The coded record surfaces exactly the drivers that move this class of business:
- Law-enforcement liability — civil-rights claims are the dominant severity driver.
- Fleet exposure across departments, from the auto schedule.
- Self-insured retention & SIR erosion — where the excess actually attaches.
- Sovereign-immunity considerations, noted from the narrative.
- A 44-claim loss run extracted in full, coded and by-line.
Why it holds up in production
- Every value is cited — page/region provenance on fields and schedules; a file review can click any number to its source.
- Codes are normalized against governed glossaries; out-of-vocabulary values are validated, not hidden.
- Totals are computed, not guessed — deterministic derived fields over the extracted rows, exact and reproducible.
- Configuration is versioned — document types, glossaries and rules pinned to a published version, so an output made today reproduces tomorrow.
- Nothing is dropped — the all-sheets extractor surfaces every schedule, even ones the base schema didn’t anticipate.
Any casualty class, one agent
Construction, trucking, public entity, and more — the same Excess Casualty agent, configured with document types and glossaries, not per-account code. See the end-to-end walkthrough → or talk to us about your own submissions.