Validate a program for comparison

A reproducible competitive study needs evidence for the rating source, input mapping, and comparison basis. Use this workflow when adding a program or changing its source, tables, prepare function, or assumptions. Install Relativities and download the synthetic examples before running this page. Run the examples packaged with the synthetic program:

from pathlib import Path

from relativities import Program, Rater

samples = Path("relativities-examples/ppa")
with Rater.load(samples / "carrier-a.zip") as engine:
    engine_id = engine.bundle_id
    program = Program.pack(
        engine,
        samples / "program_a",
        "validated-program.zip",
        prepare="program:prepare",
        schema="program:SCHEMA",
        examples="examples",
    )
checks = program.check()
checks[["submission_id", "status", "expected_premium", "premium", "matches"]]
#   submission_id          status  expected_premium  premium  matches
# 0            S2       succeeded             825.0    825.0     True
# 1            S3       succeeded             375.0    375.0     True
# 2            S4  invalid_inputs               NaN      NaN     True

The examples cover two successful ratings and invalid inputs. Their expected premiums were calculated independently from the synthetic rules.

Confirm the source and scope

Record the company, state, product, program version, effective date, and new-business or renewal basis. Review the applicable manual, tables, amendments, and underwriting rules together. Identify supported coverages, fees, discounts, and any omitted components before interpreting totals.

Check	Evidence it supplies
Bundle hashes and `bundle_id`	Identity of the downloaded implementation
`rslc check`	Structural and calculation properties of that implementation
Source review and citations	Evidence for how the filing was interpreted
Independent examples or matched quotes	Evidence that specified inputs reproduce expected premiums
Book-level status counts	Coverage of the population used in the study

Each check answers a different question. A verified bundle or successful rating alone establishes only the corresponding property above.

Inspect the mapping before the premium

Review the book a program accepts, then the carrier inputs for a representative submission:

schema = program.schema.to_pandas()
schema.query("table == 'drivers' and type == 'date'")[["column", "type", "required"]]
#                    column  type  required
# 11             birth_date  date     False
# 14    first_licensed_date  date     False
# 15  defensive_course_date  date     False
program.explain(program.examples, "S2").json_in
# {'drivers': [{'age': 45}, {'age': 19}], 'vehicles': [{'annual_mileage': 6000, 'collision_deductible': 500}, {'annual_mileage': 9000, 'collision_deductible': 0}]}

program.schema lists every table and column that prepare can read. A program checks each book against it before prepare runs. Check dates, vehicle and driver assignments, symbols, territories, coverage selections, units, and supplied model inputs. Keep reported values separate from derived values, proxies, and carrier-specific estimates. Document each mapping’s source and assumptions beside prepare. explain shows the prepared inputs and RSL calculations. The Python prepare function remains the source for its mapping logic.

Reconcile independent examples

Use filing examples, independently calculated cases, or quotes with established rating facts and scope. Reconcile by coverage and vehicle where available, then the policy total, including the applicable fees and rounding. For a mismatch, inspect inputs first, followed by table keys, factors, calculation order, and rounding. Retain unresolved differences and the evidence needed to settle them. Choose cases at rating-band boundaries and with material interactions, such as a young driver in a multi-vehicle household. Include refusals, missing facts, and unsupported selections so the study reports those outcomes explicitly. Program.check compares packaged expectations within half a cent. It provides regression evidence only for those cases and the quality of their expectations. with_rater recalculates expected outcomes for a replacement rater while checking statuses. Validate the changed premiums independently before accepting them as new expectations.

Preserve the run and its exceptions

run_identity = {"bundle_id": program.rater.bundle_id, "engine_id": engine_id}
checks.to_csv("program-validation.csv", index=False)
program.close()

Retain the IDs with the book snapshot, mapping assumptions, comparison basis, and analysis code. For sampled facts, retain the sampling procedure and seed too. Count every program’s successes, refusals, invalid inputs, and mapping failures before summarizing premiums. A calculated premium is a rating result. Underwriting appetite needs its own evidence in a placement-oriented exhibit. Compare carriers shows how to keep those populations visible.

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Comparative pricing

Prepare and validate

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Rater development

Python reference

Data reference

Validate a program for comparison

Confirm the source and scope

Inspect the mapping before the premium

Reconcile independent examples

Preserve the run and its exceptions

​Confirm the source and scope

​Inspect the mapping before the premium

​Reconcile independent examples

​Preserve the run and its exceptions

Confirm the source and scope

Inspect the mapping before the premium

Reconcile independent examples

Preserve the run and its exceptions