Premium Audit Exposure Adjustments by Territory
This dataset provides detailed records of premium audit exposure adjustments, comparing estimated and actual exposures (payroll, sales, receipts) by state, territory, and classification. It enables insurers and analysts to track audit outcomes, support accurate revenue recognition, and inform experience rating calculations, offering valuable insights for underwriting, compliance, and risk management.
Sample rows
preview · 8 of 400 rows · all 17 columns| audit_idstring | exposure_typestring | estimated_exposure_amountfloat | included_in_experience_ratingboolean | classification_codestring | audit_datedate | policy_numberstring | statestring | territory_codestring | classification_descriptionstring | actual_exposure_amountfloat | exposure_unitstring | adjustment_amountfloat | adjustment_reasonstring | auditor_idstring | revenue_recognition_period_startdate | revenue_recognition_period_enddate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AUDIT0001 | payroll | 95250.75 | true | 8810 | 2023-01-31 | PLCY-1000001 | TX | TX01 | Clerical Office Employees | 101313.88 | USD | 6063.13 | year-end payroll reconciliation | AUD001 | 2022-01-01 | 2022-12-31 |
| AUDIT0002 | sales | 452310 | false | 8742 | 2023-02-05 | PLCY-1000002 | NY | NY02 | Salespersons, Outside | 399800 | USD | -52510 | contract cancelled prior to close | AUD002 | 2022-04-01 | 2023-01-31 |
| AUDIT0003 | payroll | 158000 | true | 8829 | 2023-03-01 | PLCY-1000003 | CA | CA05 | Nursing Home Employees | 161750.5 | USD | 3750.5 | increase due to overtime shifts | AUD003 | 2022-07-01 | 2022-12-31 |
| AUDIT0004 | receipts | 785000 | true | 8017 | 2023-03-05 | PLCY-1000004 | FL | FL03 | Retail Store Employees | 770400 | USD | -14600 | post-holiday sales refund spike | AUD004 | 2022-10-01 | 2022-12-31 |
| AUDIT0005 | payroll | 120000 | true | 8601 | 2023-01-15 | PLCY-1000005 | IL | IL13 | Engineers, Professional | 120000 | USD | 0 | payroll matched estimate | AUD005 | 2022-01-01 | 2022-12-31 |
| AUDIT0006 | payroll | 66000 | false | 7380 | 2023-02-03 | PLCY-1000006 | GA | blank | Drivers, NOC | 59400 | USD | -6600 | seasonal layoff - winter | AUD006 | 2022-07-01 | 2022-12-31 |
| AUDIT0007 | receipts | 155000 | true | 9063 | 2023-04-01 | PLCY-1000007 | OH | OH22 | Janitorial Services | 163500 | USD | 8500 | grant for facility expansion | AUD007 | 2022-11-01 | 2023-03-31 |
| AUDIT0008 | payroll | 187500 | false | 5606 | 2023-02-28 | PLCY-1000008 | PA | PA14 | Construction Supervisors | 172500 | USD | -15000 | project delayed, payroll reduced | AUD008 | 2022-04-01 | 2023-01-31 |
| AUDIT0009 | sales | 97500 | false | 8723 | 2023-03-10 | PLCY-1000009 | CO | blank | Insurance Agents | 92900 | USD | -4600 | late reporting, commission adjustment | AUD009 | 2022-06-01 | 2023-02-28 |
| AUDIT0010 | receipts | 576000 | true | 8017 | 2023-01-31 | PLCY-1000010 | MI | MI09 | Retail Store Employees | 600000 | USD | 24000 | holiday sales surge | AUD010 | 2022-10-01 | 2022-12-31 |
| AUDIT0011 | payroll | 78000 | false | 8810 | 2023-02-02 | PLCY-1000011 | WA | WA08 | Clerical Office Employees | 78000 | USD | 0 | no adjustment required | AUD011 | 2022-07-01 | 2022-12-31 |
| AUDIT0012 | receipts | 82000 | true | 8602 | 2023-02-24 | PLCY-1000012 | MO | MO11 | Architectural Firms | 77900 | USD | -4100 | refund for cancelled contract | AUD012 | 2022-09-01 | 2023-01-31 |
| AUDIT0013 | payroll | 42000 | false | 5606 | 2023-03-15 | PLCY-1000013 | AZ | blank | Construction Supervisors | 42000 | USD | 0 | late reporting, matched estimate | blank | 2022-07-01 | 2022-12-31 |
| AUDIT0014 | payroll | 104500 | true | 8829 | 2023-04-03 | PLCY-1000014 | NJ | NJ06 | Nursing Home Employees | 119200 | USD | 14700 | staffing expansion for new wing | AUD013 | 2022-12-01 | 2023-03-31 |
| AUDIT0015 | sales | 120000 | true | 8742 | 2023-03-28 | PLCY-1000015 | OR | OR02 | Salespersons, Outside | 147600 | USD | 27600 | unexpected new contract | AUD014 | 2022-06-01 | 2023-02-28 |
| AUDIT0016 | receipts | 0 | false | 8017 | 2023-01-20 | PLCY-1000016 | WI | WI17 | Retail Store Employees | 0 | USD | 0 | policy inactive, no receipts | AUD015 | 2022-10-01 | 2022-12-31 |
| AUDIT0017 | receipts | 575000 | true | 8602 | 2023-02-07 | PLCY-1000017 | MN | MN23 | Architectural Firms | 573100 | USD | -1900 | rounding corrections | AUD016 | 2022-09-01 | 2023-01-31 |
| AUDIT0018 | payroll | 25000 | false | 7380 | 2023-02-09 | PLCY-1000018 | NC | NC12 | Drivers, NOC | 23350 | USD | -1650 | understaffing due to illness | AUD017 | 2022-07-01 | 2022-12-31 |
| AUDIT0019 | payroll | 93000 | true | 5606 | 2023-03-11 | PLCY-1000019 | TN | TN21 | Construction Supervisors | 102300 | USD | 9300 | unexpected overtime | AUD018 | 2022-07-01 | 2022-12-31 |
| AUDIT0020 | payroll | 1800 | false | 8810 | 2023-02-14 | PLCY-1000020 | IN | blank | Clerical Office Employees | 1800 | USD | 0 | late reporting, matched estimate | AUD019 | 2022-07-01 | 2022-12-31 |
What the 400 rows show
from the 400-row sample- 46%included_
in_ experience_ rating = true - 120,000median estimated_
exposure_ amount - 44classification descriptions
- 51states
- 109,175median actual_
exposure_ amount - 0.0median adjustment_
amount
Median 120,000, from 0.0 to 7,000,000.
- string 10
- float 3
- date 3
- boolean 1
Columns
17 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 10 columns | |||
audit_id | string | Unique identifier for each premium audit adjustment record.unique | AUDIT0001 |
policy_number | string | Unique identifier for the insurance policy associated with the audit. | PLCY-1000001 |
state | string | U.S. state or territory where the exposure occurred, using standard two-letter abbreviation. | TX |
territory_code | string | Code representing the insurance rating territory within the state.optional | TX01 |
classification_code | string | Standard classification code (e.g., NCCI, ISO) for the type of exposure (such as industry or occupation).10 codes | 8810 |
classification_description | string | Description of the classification code (e.g., 'Clerical Office Employees').optional | Salespersons, Outside |
exposure_type | string | Type of exposure being measured: payroll, sales, or receipts.payroll · sales · receipts | payroll |
exposure_unit | string | Unit of measurement for the exposure amount (e.g., USD, units, etc.).USD · units · other · optional | USD |
adjustment_reason | string | Brief reason or code for the adjustment (e.g., 'underreported payroll', 'sales increase').optional | payroll matched estimate |
auditor_id | string | Identifier for the auditor or audit team responsible for the adjustment.optional | AUD001 |
| Numbers 3 columns | |||
estimated_exposure_amount | float | Original estimated exposure amount (e.g., payroll, sales, or receipts) used for premium calculation.0 or more | 95250.75 |
actual_exposure_amount | float | Actual exposure amount determined during the audit.0 or more | 101313.88 |
adjustment_amount | float | Difference between actual and estimated exposure amounts (actual - estimated). | 6063.13 |
| Dates and times 3 columns | |||
audit_date | date | Date when the premium audit was conducted or finalized. | 2023-01-31 |
revenue_recognition_period_start | date | Start date of the period for which revenue is recognized.optional | 2022-01-01 |
revenue_recognition_period_end | date | End date of the period for which revenue is recognized.optional | 2022-12-31 |
| True or false 1 column | |||
included_in_experience_rating | boolean | Indicates if this adjustment is included in the experience rating calculation. | true |
Use it for
An insurance dashboard
The included_
in_ experience_ rating rate, estimated_ exposure_ amount by exposure_ type and a breakdown of classification_ code. Excel, Power BI or Tableau. Why do 184 of 400 rows have included_
in_ experience_ rating = true? A class exercise
Hand out the rows and one question. Everyone works from the same 400 rows.
- Audits400AUDIT000195250.75payrollAUDIT0002452310salesAUDIT0003158000payroll
A software demo
Believable audits with audit_
date, policy_ number and state to fill a screen in front of a buyer.
This dataset400 rows17 columns
Yours1,000 rows17 columnsstate: UK only
blueprint · premium-audit-exposure-adjustments-by-territory
Behind this dataset
Same schema. As many rows as you need.
These 400 rows came out of a blueprint — 17 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
- Estimated payroll at inception
- Actual payroll from audit
- Additional or return premium from audit
- Payroll by state for multi-state risks
- Payroll by class code
- Sales/receipts for GL rating
- Square footage for premises
- Seasonal employment variations
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- premium-audit-exposure-adjustments-by-territory