Commercial Underwriting

Experience level: Intermediate
Reasoning types: Rules-based
Industry: Financial Services
Tags: Rules-Based ReasoningUnderwritingEligibilityRisk ClassificationHierarchical OntologyInsurance to Value

What this template is for

Commercial property and casualty carriers triage every submission against a book of underwriting rules — insurance-to-value adequacy, fire-protection class, year-built floors, occupancy restrictions, industry appetite, operating history — and the answer for each account depends on the worst thing found anywhere beneath it. One old building or one restricted industry can decline an otherwise clean insured. Encoding that judgment as maintainable, auditable logic (rather than a tangle of procedural if-checks) is the hard part.

This template shows how to express that rulebook as declarative logic on a four-level hierarchy of insured entity, policy, location, and coverage, so lower-level eligibility checks roll up automatically into a per-entity risk tier. The result is an underwriting view that says not just which tier each account lands in, but exactly which rule put it there.

The reasoning approach is rules-based: every eligibility check and rollup is a declarative derived property, and PyRel resolves the dependencies between levels automatically — no procedural rule chains, no explicit ordering.

Who this is for

Insurance carriers, MGAs, and brokers automating commercial underwriting triage
Data scientists building rule engines on hierarchical ontologies
Developers learning how to chain derived Relationships across multiple concept levels in PyRel

What you’ll build

A four-level commercial property/casualty ontology with InsuredEntity, Policy, Location, and Coverage
Eight underwriting rules expressed as derived Relationship flags on the appropriate level
Two entity-level rollup rules (decline factors, marginal factors) that aggregate per-entity verdicts from lower-level flags
Four mutually exclusive risk-tier subtype concepts (RiskTier_Decline, RiskTier_NonStandard, RiskTier_Standard, RiskTier_Preferred)
Reporting that prints the eligibility flag matrix and the final per-entity tier classification

What’s included

Model: the four-level InsuredEntity / Policy / Location / Coverage ontology, eight lower-level eligibility rules, two entity-level rollup rules (decline factors, marginal factors), and four mutually exclusive risk-tier subtype concepts — all as declarative derived Relationships.
Runner: commercial_underwriting.py — a single Python script that loads the CSVs, applies the rules, and prints results end to end.
Runbook: runbook.md — a paste-testable walkthrough that reproduces the template step by step with the RAI skills; as important a reference as the script itself.
Sample data: four CSVs under data/ — 8 insured businesses across 5 industries (insured_entities.csv), 8 commercial property policies one per insured (policies.csv), 11 scheduled locations (locations.csv), and 33 coverage lines at every location (coverages.csv).
Outputs: the printed eligibility flag matrix, the final per-entity tier classification, and the premium-by-tier business rollup.

Prerequisites

Access

A Snowflake account that has the RAI Native App installed.
A Snowflake user with permissions to access the RAI Native App.

Tools

Python >= 3.10
RelationalAI Python SDK (relationalai) == 1.11.0

Quickstart

Download the ZIP file for this template and extract it:
Terminal window
```
curl -O https://docs.relational.ai/templates/zips/v1/commercial_underwriting.zip
unzip commercial_underwriting.zip
cd commercial_underwriting
```
You can also download the template ZIP using the “Download ZIP” button at the top of this page.

Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip

Install dependencies:
Terminal window
```
python -m pip install .
```
Configure Snowflake connection and RAI profile:
Terminal window
```
rai init
```
Run the template:
Terminal window
```
python commercial_underwriting.py
```

Expected output (truncated):

Stage 1: Location- and coverage-level eligibility flags
Locations with poor fire protection (FP > 6): 0
Locations with old construction (year_built < 1950): 1
    location_id                      address  year_built
              8  800 Foundry Rd Pittsburgh PA        1948
Locations with high-risk occupancy: 3
    location_id                      address     occupancy
              7  700 Cannabis Way Denver CO  CANNABIS_RETAIL
             10   900 Highway 10 Phoenix AZ      GAS_STATION
             11    910 Old US 80 Tucson AZ      GAS_STATION
Underinsured BUILDING coverages (ITV < 80%): 1
    coverage_id           location_address      limit  replacement_value  itv_pct
             13 500 Industry Dr Detroit MI  7500000.0         10000000.0     75.0

Stage 3: Risk-tier classification
                    name               industry  years         tier
    PreferredCorp Holdings                RETAIL     20    PREFERRED
               GoodOps Inc  PROFESSIONAL_SERVICES      8     STANDARD
              AutoFix Plus            AUTO_REPAIR     12 NON-STANDARD
                ChemFab Co    MANUFACTURING_HEAVY     15 NON-STANDARD
            NewVenture LLC                 RETAIL      1      DECLINE
                 CannaCorp               CANNABIS      5      DECLINE
     OldFactory Industries    MANUFACTURING_LIGHT     25      DECLINE
          GasStation Group                 RETAIL     10 NON-STANDARD

Tier counts:
    NON-STANDARD    3
    DECLINE         3
    PREFERRED       1
    STANDARD        1

Premium by tier:
               count       sum          mean
    DECLINE        3  149500.0  49833.333333
    NON-STANDARD   3  236000.0  78666.666667
    PREFERRED      1   42000.0  42000.000000
    STANDARD       1   18000.0  18000.000000

Each entity falls into exactly one tier — the four tier concepts partition the InsuredEntity set. The premium-by-tier rollup is the underwriting business view: about 236K is non-standard and could be repriced.

Template structure

.
├── README.md                       # this file
├── pyproject.toml                  # dependencies
├── commercial_underwriting.py      # main script (end-to-end)
└── data/
    ├── insured_entities.csv        # top of the hierarchy
    ├── policies.csv                # one policy per insured
    ├── locations.csv               # scheduled locations on the policy
    └── coverages.csv               # coverage lines at each location

Start here: run python commercial_underwriting.py for the full run end to end, or follow runbook.md to reproduce it step by step with the RAI skills.

Sample data

The eight insureds are designed so each tier is represented and every rule fires at least once:

Entity	Industry	Years	Why this tier
PreferredCorp Holdings	RETAIL	20	Long history, single low-FP location at 100% ITV → Preferred
GoodOps Inc	PROFESSIONAL_SERVICES	8	Clean but only 8 years and FP class 4 → Standard
AutoFix Plus	AUTO_REPAIR	12	Marginal industry list → Non-Standard
ChemFab Co	MANUFACTURING_HEAVY	15	Marginal industry and underinsured BUILDING (75% ITV) → Non-Standard
NewVenture LLC	RETAIL	1	Insufficient history (< 3 years) → Decline
CannaCorp	CANNABIS	5	Restricted industry → Decline
OldFactory Industries	MANUFACTURING_LIGHT	25	One location built in 1948 (< 1950 floor) → Decline
GasStation Group	RETAIL	10	Both locations are GAS_STATION (high-risk occupancy) → Non-Standard

Model overview

Key entities: InsuredEntity, Policy, Location, and Coverage — a four-level property/casualty hierarchy, plus four mutually exclusive risk-tier subtype concepts (RiskTier_Decline, RiskTier_NonStandard, RiskTier_Standard, RiskTier_Preferred) that extend InsuredEntity.
Primary identifiers: each level has an integer id; the hierarchy is threaded by relationships (Policy.insured_entity, Location.policy, Coverage.location) rather than by shared keys.
Important invariants: every policy belongs to exactly one insured entity, every location to one policy, every coverage to one location; the ITV check applies to BUILDING coverages only; the four risk tiers partition the InsuredEntity set (each entity lands in exactly one).

For the full concept and property definitions, see commercial_underwriting.py; runbook.md builds them step by step with the RAI skills.

How it works

Rules run at three altitudes on the hierarchy and feed each other. A coverage is checked for insurance-to-value (ITV) adequacy; a location for fire-protection class, year-built, and occupancy class; an insured entity for industry and operating history. Each check is a declarative flag: a single where(...).define(...), defined once per member for list-based checks (industry, occupancy) so PyRel unions the matches, and joining across concept levels in one clause for the ITV check.

Those lower-level flags then roll up. Entity-level rollup rules traverse the InsuredEntity → Policy → Location → Coverage chain to raise a flag on the entity if any descendant matches, and an OR-style aggregator (has_decline_factor, has_marginal_factor) unions the contributing flags via PyRel’s set-union semantics. The tiers themselves are typed sub-concepts that extend InsuredEntity: any decline factor pushes the entity to Decline, marginal factors to Non-Standard, clean entities are Standard, and clean long-tenured entities with the highest fire-protection grades are Preferred. The tier conditions are written with explicit exclusions so the four sets partition the InsuredEntity set — Preferred being a tightened subset of “no decline, no marginal.” Everything is declarative: lower-level rules feed entity-level rollups, which feed the tier classification, and PyRel resolves the ordering.

InsuredEntity -> Policy -> Location -> Coverage

See commercial_underwriting.py for the implementation and runbook.md to reproduce it step by step with the RAI skills.

Customize this template

Use your own data

Replace the four CSVs with your own underwriting submissions. Required columns are listed in the Model overview section. The script’s Concept.new(...) and Relationship definitions match the CSV column names exactly — keep the same headers or update the script’s model.data(...) calls to match.

Tune parameters

All thresholds are at the top of the script under # Configure inputs:

Parameter	Default	Effect
`MIN_YEARS_IN_BUSINESS`	3	Below this → decline
`MAX_FP_CLASS_FOR_ELIGIBILITY`	6	Above this → decline
`MIN_YEAR_BUILT`	1950	Older → decline
`ITV_THRESHOLD`	0.80	Building coverage limit must reach this fraction of replacement value
`RESTRICTED_INDUSTRIES`	tuple	Industries that decline outright
`MARGINAL_INDUSTRIES`	tuple	Industries that flag the entity as marginal but acceptable
`HIGH_RISK_OCCUPANCIES`	tuple	Occupancy classes that flag the location as marginal
`PREFERRED_MIN_YEARS`	15	Years-in-business floor for the Preferred tier
`PREFERRED_MAX_FP_CLASS`	3	All locations must be at this FP class or better

Extend the model

Add a Driver/Operator concept under InsuredEntity to model named individuals, license history, and MVR (motor vehicle record) checks. Same pattern as Location: declare the concept, add a Relationship to the parent, write rules.
Add a Catastrophe-Exposure rule that flags locations in flood zones, earthquake zones, or hurricane-prone counties. Joins to a separate CatastropheZone concept.
Pricing factor derivation. Currently the template only classifies risk tier. Add a derived Property (not Relationship) on Policy for pricing_factor, computed from a sum of additive multipliers per matched flag. See the derivation rule pattern in the shipment_compliance template.
Subline expansion. Add GENERAL_LIABILITY, UMBRELLA, WORKERS_COMP policies alongside COMMERCIAL_PROPERTY. The four-level structure carries over directly.

Scale up / productionize

Point at Snowflake tables. Swap the CSV model.data(...) calls for model.Table(...) references to your submission tables; the ontology, rules, and tier classification are unchanged. Size the RAI engine to the submission volume — rules-based classification is lightweight, so a small engine handles large books.
Schedule triage runs. Wire the script into your submission-intake pipeline (nightly batch or on-submission trigger) so every new account is scored as it arrives, and persist the per-entity tier and flag matrix for downstream underwriting review.
Pin dependencies for reproducibility. Keep the relationalai version pinned in pyproject.toml so rule outcomes stay stable across runs, and version-control your threshold constants (top of the script) as auditable underwriting parameters.

Troubleshooting

Why does OldFactory Industries decline despite 25 years of history?

Decline factors are OR-aggregated. The Pittsburgh location’s year_built is 1948, which fails the construction-floor rule (< 1950). The rollup rule InsuredEntity.has_old_construction_location fires for OldFactory Industries on the Pittsburgh location alone, even though its Cleveland location built in 2000 is fine. One bad location pushes the entire entity to Decline.

Why is GoodOps Inc Standard rather than Preferred?

GoodOps Inc has only 8 years in business, which is below the PREFERRED_MIN_YEARS=15 floor. It also has FP class 4 at its single location, which exceeds the PREFERRED_MAX_FP_CLASS=3 cap. Either factor alone disqualifies it from Preferred. Lowering both thresholds (e.g., PREFERRED_MIN_YEARS=5, PREFERRED_MAX_FP_CLASS=4) would promote it.

An entity isn't classified into any tier.

The script verifies mutual exclusivity and prints a warning if any entity is unclassified. The most likely cause is a rule condition that doesn’t match the data — verify with model.select(InsuredEntity.id).where(InsuredEntity.has_decline_factor()).to_df() and the analogous has_marginal_factor query that those flags fire as expected.

Two tiers fire for the same entity.

This indicates the tier conditions are not mutually exclusive. The script prints a warning if it finds any overlap. Check the model.not_() clauses on the lower-priority tier rules (NonStandard / Preferred / Standard) — each must explicitly exclude the higher-priority tier’s flag.

How do I add a new flag without writing a separate has_decline_factor rule?

Decline-factor membership is the union of all has_decline_factor() definitions. Add a new flag rule on the appropriate level (e.g., Location.has_seismic_exposure), add an entity-level rollup (InsuredEntity.has_seismic_exposure_location), then add one more model.where(InsuredEntity.has_seismic_exposure_location()).define(InsuredEntity.has_decline_factor()) line. PyRel automatically unions all definitions of the same Relationship.

Learn more

Core concepts

PyRel v1 modeling — concepts, properties, and derived Relationships, the building blocks this template uses at every level.
Rules-based reasoning — authoring declarative business rules and letting the engine resolve their dependencies.

Language / modeling reference

Derived Relationships and set-union semantics — how defining the same Relationship multiple times unions the matches, the pattern behind the OR-style rollups.
Subtype concepts (extends) — modeling mutually exclusive risk tiers as sub-concepts of InsuredEntity.

CLI / SDK guides

rai init and raiconfig.yaml — connecting the template to your RelationalAI account.

Support

File issues at the RelationalAI templates repository.

shipment_compliance — flat rules-based template (single-level entity, no hierarchy)
portfolio_balancing — uses rules + graph + prescriptive in a chained workflow; rules flag compliance violations that feed downstream optimization
supply_chain_resilience — multi-reasoner with rules-based supplier risk classification feeding a network-flow optimizer