Insurance
19 columns
200 rows
8 formats

Insurance Claim Severity Scores

This dataset provides detailed, claim-level insurance data including severity scores, key features used in risk assessment, and resolution times. It enables analytics teams to evaluate claim risk, optimize processing workflows, and improve payout accuracy. The comprehensive structure supports predictive modeling, operational analysis, and regulatory reporting.

opened 5 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Predictive modeling of claim severity and risk

Sample rows

preview · 8 of 200 rows · all 19 columns

claim_idstring	claim_typestring	feature_1float	location_countrystring	policy_idstring	claimant_idstring	claim_datedate	incident_datedate	claim_statusstring	severity_scorefloat	feature_2float	feature_3float	resolution_time_daysinteger	resolved_datedate	payout_amountfloat	adjuster_idstring	location_citystring	location_statestring	location_postal_codestring
AUTO0016M	auto	8750.55	USA	POL892134	C72841FG	2022-03-12	2022-03-09	closed	0.37	28	0.42	7	2022-03-19	8400	ADJ0024	San Diego	CA	92103
CLM-09ZZQ1	property	34600	Germany	POL107368	EDG84219	2023-01-19	2023-01-18	open	0.62	36	0.89	blank	blank	blank	ADJ1081	Berlin	BE	10409
LI-001A21	life	420000	UK	POL721823	F214982Y	2020-05-24	2020-05-13	closed	1	64	0.11	96	2020-08-28	420000	ADJ4102	Manchester	ENG	M1 1AA
HEALTH8832	health	2150	South Africa	POL622145	E193820A	2024-02-10	2024-02-09	in_review	0.51	41	0.32	blank	blank	blank	ADJ2098	Cape Town	WC	8001
AUTO0022N	auto	15320.1	USA	POL512934	C83279JK	2021-10-13	2021-10-12	closed	0.44	34	0.67	8	2021-10-21	14800	ADJ3030	Dallas	TX	75204
PROP-0312	property	111289	UAE	POL420981	P819374L	2022-09-05	2022-09-03	denied	0.22	43	0.47	18	2022-09-23	0	ADJ7783	Dubai	DU	00001
CLM-09ZZQ2	property	980000	Australia	POL808901	E104982B	2023-03-30	2023-03-28	closed	0.78	87	0.98	74	2023-06-12	980000	ADJ1122	Melbourne	VIC	3000
LI-003B09	life	520000	Canada	POL969201	F309184Z	2021-02-11	2021-02-03	closed	0.99	67	0.23	31	2021-03-14	520000	ADJ4211	Toronto	ON	M5H 2N2
LI-004C10	life	300000	USA	POL109823	C193847E	2020-12-02	2020-11-27	closed	1	59	0.06	120	2021-04-02	300000	ADJ7014	Miami	FL	33130
AUTO0031K	auto	800	USA	POL912483	C81293NM	2023-04-18	2023-04-14	in_review	0.16	26	0.35	blank	blank	blank	ADJ1220	Los Angeles	CA	90017
PROP-0329	property	120990	Switzerland	POL309812	P198374X	2022-07-20	2022-07-17	open	0.52	45	0.41	blank	blank	blank	ADJ1082	Zurich	ZH	8001
AUTO0045J	auto	20000	USA	POL482910	C82937BP	2021-04-09	2021-04-07	closed	0.89	38	0.81	4	2021-04-13	20000	ADJ4019	Chicago	IL	60616
HEALTH8835	health	8700	Switzerland	POL843021	E192830S	2023-06-14	2023-06-12	closed	0.69	60	0.62	14	2023-06-28	8700	ADJ9837	Geneva	GE	1206
LIABILITY110	liability	5000	Canada	POL675892	EDG19283	2022-05-21	2022-05-18	pending	0.07	28	0.21	blank	blank	blank	blank	Montreal	QC	H2Y 1C6
PROP-0341	property	302000	Spain	POL782103	P298374N	2023-09-17	2023-09-15	closed	0.73	54	0.57	16	2023-10-03	302000	ADJ9812	Madrid	MD	28013
OTHER-0012	other	2200	Japan	POL109821	F193847O	2021-02-22	2021-02-19	closed	0.65	35	0.29	9	2021-03-03	2190	ADJ2139	Osaka	OS	530-0001
AUTO0053Q	auto	3000	USA	POL301928	C98123PO	2022-08-14	2022-08-13	open	0.14	23	0.19	blank	blank	blank	ADJ4018	Houston	TX	77002
HEALTH8899	health	4900	India	POL812983	E284192M	2022-12-04	2022-11-30	closed	0.75	51	0.26	7	2022-12-11	4900	ADJ9192	Mumbai	MH	400001
AUTO0067F	auto	6500	USA	POL402918	C21984XM	2023-11-20	2023-11-18	closed	0.25	29	0.22	3	2023-11-23	6350	ADJ4521	New York	NY	10001
LI-005D31	life	350000	USA	POL391827	C918274T	2022-02-02	2022-01-29	closed	1	58	0.19	31	2022-03-05	350000	ADJ8102	Seattle	WA	98101

What the 200 rows show

from the 200-row sample

Life (claim type) stands out: mean feature_1 is 485,770, against 46,229 for the rest.

17,637median feature_1
5claim statuses
53location states
60location cities
0.52median severity_score
40median feature_2

Mean feature_1 by claim_type200 rows

feature_1200 rows, in bands of 100k

Median 17,637, from 350.0 to 980,000.

location_country200 rows · top 10 of 16 values

USA61
Canada18
Switzerland15
UK12
Spain12
Germany11
Australia11
Japan11
UAE10
Ireland10

19 columns by typefrom the column list below

string 10
integer 1
float 5
date 3

Columns

19 columns in three groups

blueprint · 19 columns
column	type	description	example
Text 10 columns
`claim_id`	string	Unique identifier for each insurance claim.unique	AUTO0016M
`policy_id`	string	Unique identifier for the insurance policy associated with the claim.	POL892134
`claimant_id`	string	Unique identifier for the individual or entity submitting the claim.	C72841FG
`claim_type`	string	Type of insurance claim (e.g., auto, property, health).6 values	auto
`claim_status`	string	Current status of the claim (e.g., open, closed, pending, denied).open · closed · pending · denied · in_review	closed
`adjuster_id`	string	Unique identifier for the claims adjuster handling the claim.optional	ADJ0024
`location_city`	string	City where the incident occurred.optional	San Diego
`location_state`	string	State where the incident occurred.optional	CA
`location_postal_code`	string	Postal code of the incident location.optional	92103
`location_country`	string	Country where the incident occurred.optional	USA
Numbers 6 columns
`severity_score`	float	Numerical score indicating the severity of the claim, typically between 0 and 1.0 to 1	0.37
`feature_1`	float	First feature used in severity scoring (e.g., claim amount, damage estimate).optional	8750.55
`feature_2`	float	Second feature used in severity scoring (e.g., age of claimant, vehicle age).optional	28
`feature_3`	float	Third feature used in severity scoring (e.g., location risk factor).optional	0.42
`resolution_time_days`	integer	Number of days taken to resolve the claim.0 or more · optional	7
`payout_amount`	float	Total amount paid out for the claim.0 or more · optional	8400
Dates and times 3 columns
`claim_date`	date	Date when the claim was filed.	2022-03-12
`incident_date`	date	Date when the incident leading to the claim occurred.	2022-03-09
`resolved_date`	date	Date when the claim was resolved or closed.optional	2022-03-19

Use it for

An insurance dashboard
Feature_1 by claim_type and a breakdown of location_country. Excel, Power BI or Tableau.
Why do the 33 life rows have a mean feature_1 of 485,770?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Claims200
AUTO0016M8750.55auto
CLM-09ZZQ134600property
LI-001A21420000life
A software demo
Believable claims with policy_id, claimant_id and claim_date to fill a screen in front of a buyer.

This dataset200 rows19 columns

Yours1,000 rows19 columnslocation_city: UK only

blueprint · insurance-claim-severity-scores

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 19 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

Claim ID and type logged
Severity score assigned using business rules
Resolution time tracked
Policyholder demographic included

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: insurance-claim-severity-scores

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

An insurance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Insurance

What should your data show?