Policy Audit Data and Premium Adjustments

This dataset provides detailed records of insurance premium audits, including policy information, initial and actual exposure values, audit findings, and premium adjustment calculations. It enables insurers and auditors to track policy compliance, identify discrepancies, and manage premium adjustments efficiently. The dataset is ideal for risk analysis, regulatory reporting, and operational audit optimization.

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

Premium audit tracking and reconciliation

Sample rows

preview · 8 of 600 rows · all 26 columns
audit_idstringpolicy_holder_typestringinitial_estimated_exposurefloatexposure_unitstringpolicy_idstringpolicy_numberstringpolicy_holder_namestringpolicy_effective_datedatepolicy_expiry_datedateaudit_datedateactual_exposurefloatpremium_basisstringinitial_premiumfloataudited_premiumfloatpremium_adjustment_amountfloatpremium_adjustment_typestringadjustment_reasonstringaudit_statusstringauditor_namestringauditor_contact_emailstringpolicy_holder_street_addressstringpolicy_holder_citystringpolicy_holder_statestringpolicy_holder_postal_codestringpolicy_holder_countrystringaudit_findings_notesstring
AUD20240001individual54200.25USDPOL-10001PNX34812Jessica M. Sanders2022-05-012023-05-012023-05-1757310.15payroll2168.012292.41124.4additionalUnderreported overtime hours.completedDavid Thompsondavid.thompson@auditpro.com384 Pinecrest DrSpringfieldIL62711USASlight increase in payroll due to overtime reported in Q4.
AUD20240002business890000USDPOL-10002QRT920184TechNova Solutions LLC2022-07-122023-07-122023-07-30867500payroll1780017350-450returnOverestimated payroll.completedEllen Martinezellen.martinez@auditsync.com1200 Oak Ridge AveDallasTX75234USAPayroll was slightly lower due to staff attrition.
AUD20240003organization35000unitsPOL-10003JPLX4862Sundale Foundation2022-01-242023-01-242023-02-0935000units210021000noneblankcompletedSven Olssonsven.olsson@audit.seStorgatan 23StockholmStockholm11446SwedenReported units matched estimates exactly.
AUD20240004business1200000salesPOL-10004FZ3902W1GlobalTech Corp2021-11-012022-11-012022-11-221320000sales24000264002400additionalUnderreported sales.completedMaria Evansmaria.evans@auditsync.com4557 Elm StreetAtlantaGA30312USASales exceeded projections for Q3 and Q4.
AUD20240005individual1500hoursPOL-10005YT384719Rajesh K. Sinha2022-03-082023-03-082023-03-181455other7572.75-2.25returnOverestimated work hours.completedPriya Chauhanpriya.chauhan@audit.inPlot 72 Sector 4MumbaiMaharashtra400701IndiaFewer hours worked than estimated.
AUD20240006business290000USDPOL-10006HJG92814Sunlight Roofing Inc.2022-08-152023-08-152023-08-28297500payroll58005950150additionalAdditional staff hired.completedRobert Leerobert.lee@auditpro.com601 Willow LaneClevelandOH44115USAIncreased payroll due to new hires.
AUD20240007organization12000unitsPOL-10007VTRZ5902Cairo Medical Trust2022-10-012023-10-012023-10-2110800units720648-72returnOverestimated service units.completedNabil Fahmynabil.fahmy@audit.eg67 El Nasr StCairoCairo11511EgyptLower number of service units than projected.
AUD20240008business215000salesPOL-10008BNP8491QLakeside Ventures2022-09-102023-09-102023-09-26215000sales430043000noneblankcompletedEdward Harrisedward.harris@auditsync.com88 Lake AveMinneapolisMN55409USASales matched original estimate.

What the 600 rows show

from the 600-row sample

Business (policy holder type) stands out: mean initial_estimated_exposure is 514,448, against 36,211 for the rest.

  • 61,000median initial_estimated_exposure
  • 3premium adjustment types
  • 4premium basises
  • 5policy holder countries
  • 68policy holder states
  • 116policy holder cities
Mean initial_estimated_exposure by policy_holder_type600 rows
0400k800k38,418individual210 rows514.4kbusiness212 rows33,607organization178 rows
initial_estimated_exposure600 rows, in bands of 200k
021042041368442935110600k1.2Minitial_estimated_exposure →

Median 61,000, from 0.0 to 1,200,000.

exposure_unit600 rows · 5 values
  1. USD349
  2. units140
  3. sales65
  4. hours33
  5. other13
26 columns by typefrom the column list below
  • string 18
  • float 5
  • date 3

Columns

26 columns in three groups
blueprint · 26 columns
columntypedescriptionexample
Text 18 columns
audit_idstringUnique identifier for each premium audit recorduniqueAUD20240001
policy_idstringUnique identifier for the insurance policy being auditedPOL-10001
policy_numberstringOfficial policy number assigned by the insurerPNX34812
policy_holder_namestringName of the policyholder or insured entityJessica M. Sanders
policy_holder_typestringType of policyholder (e.g., individual, business, organization)individual · business · organizationindividual
exposure_unitstringUnit of measurement for exposure (e.g., USD, hours, units, sales)USD · hours · units · sales · otherUSD
audit_findings_notesstringDetailed notes or summary of audit findingsoptionalUnit count confirmed.
premium_basisstringBasis for premium calculation (e.g., payroll, sales, units)payroll · sales · units · otherpayroll
premium_adjustment_typestringType of premium adjustment: additional premium or return premiumadditional · return · noneadditional
adjustment_reasonstringReason for premium adjustment (e.g., underreported exposure, overestimated exposure, classification change)optionalOverestimated payroll.
audit_statusstringStatus of the audit (e.g., completed, pending, disputed, cancelled)completed · pending · disputed · cancelledcompleted
auditor_namestringName of the person or entity conducting the auditoptionalDavid Thompson
auditor_contact_emailstringEmail address of the auditoroptionalsven.olsson@audit.se
policy_holder_street_addressstringStreet address of the policyholderoptional384 Pinecrest Dr
policy_holder_citystringCity of the policyholderoptionalSpringfield
policy_holder_statestringState or province of the policyholderoptionalIL
policy_holder_postal_codestringPostal code of the policyholderoptional62711
policy_holder_countrystringCountry of the policyholder5 countries · optionalUSA
Numbers 5 columns
initial_estimated_exposurefloatInitial exposure estimate used for premium calculation (e.g., payroll, sales, units)0 or more54200.25
actual_exposurefloatActual exposure determined during audit (e.g., final payroll, sales, units)0 or more57310.15
initial_premiumfloatPremium amount initially charged based on estimated exposure0 or more2168.01
audited_premiumfloatFinal premium amount determined after audit based on actual exposure0 or more2292.41
premium_adjustment_amountfloatDifference between audited premium and initial premium (positive for additional premium, negative for return premium)124.4
Dates and times 3 columns
policy_effective_datedateDate when the policy coverage begins2022-05-01
policy_expiry_datedateDate when the policy coverage ends2023-05-01
audit_datedateDate when the premium audit was conducted2023-05-17

Use it for

  • median initial…61,000600 rowsmean initial estimate…38.4kindivi…514.4kbusine…33.6korgani…

    An insurance dashboard

    Initial_estimated_exposure by policy_holder_type and a breakdown of exposure_unit. Excel, Power BI or Tableau.

  • Why do the 212 business rows have a mean initial_estimated_exposure of 514,448?

    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 audits with policy_id, policy_number and policy_holder_name to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset600 rows26 columns
Yours1,000 rows26 columnspolicy_holder_street_address: UK only

blueprint · policy-audit-data-and-premium-adjustments

Behind this dataset

Same schema. As many rows as you need.

These 600 rows came out of a blueprint — 26 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
  • Audit applies to policies with variable exposures (payroll, sales, receipts)
  • Common for workers comp, GL, and commercial auto
  • Physical vs desk audit
  • Audit period matches policy period
  • Estimated premium at inception
  • Actual exposures verified from records
  • Premium recalculated using actual exposures
  • Additional premium billed or return premium issued
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
policy-audit-data-and-premium-adjustments

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