Claims Adjuster Assignments and Workload
This dataset offers detailed tracking of claims adjuster assignments, including caseloads, pending claims, claim complexity, geographic territories, and performance metrics. It enables insurance organizations to optimize resource allocation, monitor adjuster workloads, and improve claim resolution quality across regions. Ideal for operational analytics, workforce planning, and quality management in claims processing.
Sample rows
preview · 8 of 400 rows · all 16 columns| assignment_idstring | claim_statusstring | quality_scorefloat | resource_allocation_flagboolean | adjuster_idstring | adjuster_namestring | territory_idstring | territory_namestring | territory_statestring | claim_idstring | claim_received_datedate | claim_complexity_ratinginteger | caseload_countinteger | pending_claims_countinteger | average_resolution_time_daysfloat | last_performance_review_datedate |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ASSIGN-00001 | pending | 92.4 | true | ADJ-1001 | Lisa Ray | TERR-CA01 | San Francisco Bay Area | CA | CLM-50001 | 2023-11-10 | 4 | 22 | 7 | 14.3 | 2024-01-15 |
| ASSIGN-00002 | closed | 88.1 | false | ADJ-1012 | Samuel Lee | TERR-NY02 | Manhattan | NY | CLM-50002 | 2023-07-22 | 3 | 14 | 2 | 8.7 | 2023-12-10 |
| ASSIGN-00003 | in review | 76.3 | false | ADJ-1005 | Priya Patel | TERR-TX01 | Dallas Metro | TX | CLM-50003 | 2024-03-05 | 2 | 18 | 6 | 10.6 | 2024-02-20 |
| ASSIGN-00004 | approved | 80.5 | false | ADJ-1020 | James Robinson | TERR-FL01 | Miami-Dade | FL | CLM-50004 | 2024-04-17 | 1 | 9 | 1 | 5.9 | 2024-03-18 |
| ASSIGN-00005 | denied | 70.4 | true | ADJ-1009 | Meghan Clark | TERR-IL01 | Chicago North | IL | CLM-50005 | 2023-09-30 | 5 | 27 | 8 | 19.2 | 2024-03-05 |
| ASSIGN-00006 | pending | 84.2 | false | ADJ-1015 | Carlos Gomez | TERR-AZ01 | Phoenix Region | AZ | CLM-50006 | 2024-02-12 | 2 | 10 | 4 | 8.2 | 2024-01-20 |
| ASSIGN-00007 | reopened | 78.9 | false | ADJ-1011 | Emily Chen | TERR-CA02 | Los Angeles West | CA | CLM-50007 | 2023-12-06 | 3 | 15 | 3 | 11.7 | 2023-11-30 |
| ASSIGN-00008 | closed | 81.6 | true | ADJ-1002 | John Martin | TERR-TX02 | Houston Area | TX | CLM-50008 | 2023-10-14 | 4 | 21 | 5 | 13.4 | 2024-02-01 |
| ASSIGN-00009 | pending | 73.2 | true | ADJ-1017 | Arjun Singh | TERR-CA03 | Sacramento Valley | CA | CLM-50009 | 2024-01-19 | 5 | 34 | 10 | 17.9 | 2024-02-28 |
| ASSIGN-00010 | approved | 89.7 | false | ADJ-1019 | Olivia Price | TERR-GA01 | Atlanta Central | GA | CLM-50010 | 2024-04-02 | 1 | 11 | 2 | 6.2 | 2024-03-20 |
| ASSIGN-00011 | pending | 80.2 | false | ADJ-1008 | Nathaniel Brooks | TERR-NY03 | Brooklyn | NY | CLM-50011 | 2024-01-07 | 3 | 17 | 6 | 12.1 | 2024-02-12 |
| ASSIGN-00012 | denied | 66.2 | true | ADJ-1006 | Fatima Hassan | TERR-FL02 | Orlando Area | FL | CLM-50012 | 2023-08-29 | 4 | 25 | 7 | 16.3 | 2023-11-15 |
| ASSIGN-00013 | in review | 85.4 | false | ADJ-1016 | Jacob Green | TERR-NJ01 | Newark Metro | NJ | CLM-50013 | 2023-12-27 | 2 | 13 | 4 | 9.1 | 2024-01-03 |
| ASSIGN-00014 | pending | 90.8 | false | ADJ-1014 | Sophia Kim | TERR-WA01 | Seattle North | WA | CLM-50014 | 2024-02-24 | 3 | 16 | 5 | 11.2 | 2024-01-30 |
| ASSIGN-00015 | closed | 88.7 | false | ADJ-1003 | Michael Stewart | TERR-NV01 | Las Vegas Strip | NV | CLM-50015 | 2023-09-12 | 2 | 12 | 3 | 7.5 | 2023-12-08 |
| ASSIGN-00016 | approved | 96.3 | false | ADJ-1007 | Charlotte Moore | TERR-MA01 | Boston Downtown | MA | CLM-50016 | 2024-04-06 | 1 | 8 | 1 | 4.1 | 2024-03-25 |
| ASSIGN-00017 | reopened | 81.1 | true | ADJ-1013 | David Turner | TERR-CO01 | Denver East | CO | CLM-50017 | 2023-11-30 | 4 | 19 | 5 | 15.8 | 2024-01-28 |
| ASSIGN-00018 | closed | 87.6 | false | ADJ-1004 | Katherine White | TERR-OR01 | Portland Central | OR | CLM-50018 | 2023-10-21 | 2 | 11 | 2 | 7.1 | 2024-01-15 |
| ASSIGN-00019 | pending | 77.5 | true | ADJ-1021 | Ryan Edwards | TERR-OH01 | Cleveland South | OH | CLM-50019 | 2024-01-23 | 5 | 29 | 9 | 18.7 | 2024-02-18 |
| ASSIGN-00020 | denied | 69.1 | true | ADJ-1018 | Isabella Diaz | TERR-NC01 | Charlotte Uptown | NC | CLM-50020 | 2023-08-07 | 4 | 23 | 6 | 15.6 | 2023-11-17 |
What the 400 rows show
from the 400-row sampleDenied (claim status) stands out: mean quality_
- 35%resource_
allocation_ flag = true - 85.7median quality_
score - 52territory states
- 3median claim_
complexity_ rating - 13median caseload_
count - 4median pending_
claims_ count
Median 85.7, from 62.7 to 99.6.
- string 8
- integer 3
- float 2
- date 2
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
assignment_id | string | Unique identifier for each claims adjuster assignmentunique | ASSIGN-00001 |
adjuster_id | string | Unique identifier for the claims adjuster | ADJ-1001 |
adjuster_name | string | Full name of the claims adjuster | Lisa Ray |
territory_id | string | Unique identifier for the geographic territory assigned | TERR-CA01 |
territory_name | string | Name or description of the geographic territory | San Francisco Bay Area |
territory_state | string | State or region of the territoryoptional | CA |
claim_id | string | Unique identifier for the claim assigned | CLM-50001 |
claim_status | string | Current status of the claim (e.g., pending, closed, in review)6 values | pending |
| Numbers 5 columns | |||
claim_complexity_rating | integer | Complexity rating of the claim (e.g., 1-5 scale)1 to 5 | 4 |
caseload_count | integer | Total number of active claims currently assigned to the adjuster0 or more | 22 |
pending_claims_count | integer | Number of claims assigned to the adjuster that are currently pending0 or more | 7 |
average_resolution_time_days | float | Average time in days taken by the adjuster to resolve claims0 or more · optional | 14.3 |
quality_score | float | Quality score for the adjuster's performance (e.g., 0-100 scale)0 to 100 · optional | 92.4 |
| Dates and times 2 columns | |||
claim_received_date | date | Date the claim was received | 2023-11-10 |
last_performance_review_date | date | Date of the adjuster's last performance reviewoptional | 2024-01-15 |
| True or false 1 column | |||
resource_allocation_flag | boolean | Indicates if additional resources are allocated to the adjuster for workload managementoptional | true |
Use it for
An insurance dashboard
The resource_
allocation_ flag rate and quality_ score by claim_ status. Excel, Power BI or Tableau. Why do the 40 denied rows have a mean quality_
score of 74.6? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Assignments400ASSIGN-0000192.4pendingASSIGN-0000288.1closedASSIGN-0000570.4denied
A software demo
Believable assignments with adjuster_
id, adjuster_ name and territory_ id to fill a screen in front of a buyer.
This dataset400 rows16 columns
Yours1,000 rows16 columnsterritory_id: UK only
blueprint · claims-adjuster-assignments-and-workload
Behind this dataset
Same schema. As many rows as you need.
These 400 rows came out of a blueprint — 16 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
- Adjuster ID and license information
- Specialization: auto, property, casualty, medical
- Geographic territory assignment
- Current caseload count
- Claim assignment based on complexity and workload
- Maximum caseload thresholds by complexity
- Claim complexity: routine, moderate, complex, catastrophic
- Average days to close metric
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
- claims-adjuster-assignments-and-workload