Policy Fee and Service Charge Schedules

This dataset provides a detailed schedule of non-premium insurance fees—including policy, service, installment, reinstatement, NSF, audit, and other administrative charges—organized by US state and insurance product type, with effective and expiration dates for each entry. It enables regulatory compliance checks, fee benchmarking, and operational integration for insurers and agencies. The dataset is ideal for pricing analysis, state-by-state comparisons, and policy administration.

  • last updated 1 Nov 2025
  • by GoMask
The brief that made it

Regulatory compliance and audit support

Sample rows

preview · 8 of 300 rows · all 14 columns
schedule_idstringproduct_typestringaudit_feefloatstatestringpolicy_feefloatservice_feefloatinstallment_feefloatreinstatement_feefloatnsf_feefloatother_admin_feefloatfee_currencystringeffective_datedateexpiration_datedatenotesstring
FS-00001Auto0CA28.56.7531612.51USD2020-01-012022-01-01Standard auto policy fees for CA; annually reviewed
FS-00002Homeowners0NY265220142.5USD2021-03-012023-03-01First year after system migration
FS-00003Commercial Property75TX8515.51048324USD2022-06-012023-06-01Premium segment with higher fees
FS-00004Auto0FL000000USD2023-01-01blankPromotional: Zero fees for new policyholders
FS-00005Homeowners0IL3273.512103USD2020-09-01blankStandard home policy fees for IL market
FS-00006Commercial Property100WA99.99201260417.5USD2024-01-012024-01-02Edge case: 1-day schedule for high-risk region
FS-00007Auto0GA225.252.515110USD2021-06-01blankSegment: budget auto policies – minimal fees
FS-00008Homeowners0NC19.54.2518.591.75USD2020-05-012022-05-01Low fees for rural market segment

What the 300 rows show

from the 300-row sample

Commercial Property (product type) stands out: mean audit_fee is 67.5, against 0.46 for the rest.

  • 0median audit_fee
  • 51states
  • 27.0median policy_fee
  • 7.0median service_fee
  • 3.5median installment_fee
  • 16.5median reinstatement_fee
Mean audit_fee by product_type300 rows
040800.0Auto107 rows0.95Homeowners100 rows67.5Commercial P…93 rows
audit_fee300 rows, in bands of 10
01052102039000251919223050100audit_fee →

Median 0, from 0 to 100.

14 columns by typefrom the column list below
  • string 5
  • float 7
  • date 2

Columns

14 columns in three groups
blueprint · 14 columns
columntypedescriptionexample
Text 5 columns
schedule_idstringUnique identifier for each fee schedule entryuniqueFS-00001
statestringUS state where the fee schedule applies (two-letter abbreviation)CA
product_typestringType of insurance product (e.g., Auto, Homeowners, Commercial Property)3 typesAuto
fee_currencystringCurrency in which fees are denominated (e.g., USD)USDUSD
notesstringAdditional notes or comments regarding the fee scheduleoptionalCommercial pilot for IA
Numbers 7 columns
policy_feefloatStandard policy fee charged for the product in the specified state0 or more · optional28.5
service_feefloatService fee charged for administrative services0 or more · optional6.75
installment_feefloatFee charged for installment payment plans0 or more · optional3
reinstatement_feefloatFee charged for policy reinstatement after lapse or cancellation0 or more · optional16
nsf_feefloatNon-sufficient funds (NSF) fee for failed payments0 or more · optional12.5
audit_feefloatFee charged for policy audits (typically for commercial products)0 or more · optional0
other_admin_feefloatOther miscellaneous administrative fees not covered by standard categories0 or more · optional1
Dates and times 2 columns
effective_datedateDate when this fee schedule becomes effective2020-01-01
expiration_datedateDate when this fee schedule expires (if applicable)optional2022-01-01

Use it for

  • median audit f…0300 rowsmean audit fee by pro…0.0Auto0.95Homeow…67.5Commer…

    An insurance dashboard

    Audit_fee by product_type. Excel, Power BI or Tableau.

  • Why do the 93 Commercial Property rows have a mean audit_fee of 67.5?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable schedules with state, product_type and policy_fee to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 14 columns, your size and your rules. See 20 rows before you pay.

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This dataset300 rows14 columns
Yours1,000 rows14 columnsstate: UK only

blueprint · policy-fee-and-service-charge-schedules

Behind this dataset

Same schema. As many rows as you need.

These 300 rows came out of a blueprint — 14 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Policy fee assessed per policy, not per coverage
  • Service fee for endorsements in some states
  • Installment fee for payment plans
  • State regulation limits on certain fees
  • Fee disclosure required on billing statements
  • Non-refundable fees specified
  • Pro-rata refund of fees on some states
  • Audit fee for premium audits
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
policy-fee-and-service-charge-schedules

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