Insurance
30 columns
200 rows
8 formats

Insurance Risk Factor Simulation Table

This dataset simulates comprehensive insurance policyholder profiles, including synthetic risk factors, detailed claim histories, and calculated risk scores for pricing and underwriting. It enables advanced analytics for risk modeling, claims analysis, and premium optimization across multiple insurance product lines.

last updated 21 Jul 2025
by GoMask

The brief that made it

Developing and validating insurance risk scoring models

Sample rows

preview · 8 of 200 rows · all 30 columns

policyholder_idstring	policy_typestring	risk_scorefloat	risk_factors_smokerboolean	risk_factors_vehicle_typestring	first_namestring	last_namestring	date_of_birthdate	genderstring	marital_statusstring	occupationstring	annual_incomefloat	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	policy_idstring	policy_start_datedate	policy_end_datedate	premium_amountfloat	risk_factors_ageinteger	risk_factors_credit_scoreinteger	risk_factors_property_valuefloat	claim_idstring	claim_datedate	claim_amountfloat	claim_statusstring	num_prior_claimsinteger	last_claim_datedate
PH00001	life	0.84	true	blank	Carla	Muller	1961-11-09	female	widowed	Retired	12500	14 Ludwigstrasse	Munich	blank	80333	Germany	PO00001	2020-10-01	blank	1850.25	59	612	blank	blank	blank	blank	blank	2	2017-07-12
PH00002	auto	0.31	false	sedan	Liam	Nguyen	1990-04-28	male	single	Software Engineer	87000	25 Market St	Melbourne	VIC	3000	Australia	PO00002	2022-05-01	blank	1450.9	32	798	blank	blank	blank	blank	blank	0	blank
PH00003	home	0.28	false	blank	Sofia	Garcia	1988-01-15	female	married	Manager	98000	110 Calle Mayor	Madrid	M	28013	Spain	PO00003	2021-09-15	blank	2078.5	33	824	425000	blank	blank	blank	blank	1	2019-11-19
PH00004	health	0.91	true	blank	Yusuf	Demir	1954-05-30	male	married	Retired	10000	65 Istiklal Cd	Istanbul	blank	34433	Turkey	PO00004	2021-01-10	blank	2175.3	66	567	blank	CL00001	2022-04-17	4500	approved	3	2021-11-12
PH00005	health	0.52	false	blank	Emma	Johnson	1972-07-23	female	divorced	Nurse	46800	891 Pine Ave	Seattle	WA	98101	USA	PO00005	2020-03-01	2023-03-01	1135	47	711	blank	CL00002	2021-10-05	3100	submitted	1	2019-06-27
PH00006	home	0.23	false	blank	Noah	Baker	1983-09-12	male	married	Attorney	152000	3245 Woodhaven Rd	Toronto	ON	M4T 1A2	Canada	PO00006	2022-02-18	blank	3855	38	832	975000	blank	blank	blank	blank	0	blank
PH00007	health	0.56	false	blank	Chloe	Dubois	1999-02-25	female	single	Student	3500	9 Rue de Lyon	Paris	blank	75012	France	PO00007	2019-09-01	blank	125	20	650	blank	blank	blank	blank	blank	0	blank
PH00008	health	0.47	false	blank	Lucas	Kim	2008-06-10	male	single	Child	0	88 Mapo-daero	Seoul	blank	04144	South Korea	PO00008	2021-03-01	blank	0	12	350	blank	blank	blank	blank	blank	0	blank
PH00009	home	0.38	false	blank	Olga	Petrova	1978-10-03	female	married	Architect	74000	11 Prospekt Mira	Moscow	blank	129090	Russia	PO00009	2022-06-12	blank	1600	43	728	650000	blank	blank	blank	blank	1	2021-03-22
PH00010	auto	0.22	false	hatchback	Priya	Singh	1985-12-11	female	single	Entrepreneur	122000	5 Connaught Place	Delhi	DL	110001	India	PO00010	2021-12-01	blank	1766.4	35	819	blank	blank	blank	blank	blank	0	blank
PH00011	auto	0.42	false	coupe	Ethan	Smith	1996-07-16	male	single	Graphic Designer	41000	77 Main St	Dublin	blank	D02 Y006	Ireland	PO00011	2020-07-02	blank	910.5	24	721	blank	blank	blank	blank	blank	0	blank
PH00012	life	0.75	false	blank	Wei	Zhang	1967-10-21	male	married	Professor	56000	300 Xinhua Lu	Shanghai	blank	200003	China	PO00012	2019-05-30	2022-05-30	2100	51	685	blank	blank	blank	blank	blank	2	2018-11-12
PH00013	home	0.49	false	blank	Anna	Kowalski	1982-02-22	female	divorced	Teacher	34000	19 Ulica Warszawska	Krakow	blank	31-155	Poland	PO00013	2021-04-01	blank	1125	39	702	210000	blank	blank	blank	blank	1	2017-04-01
PH00014	life	0.96	true	blank	David	Brown	1959-08-19	male	widowed	Retired	9500	82 Oxford Rd	Manchester	blank	M13 9WD	UK	PO00014	2018-04-01	blank	1995	58	489	blank	CL00003	2019-10-12	50000	approved	4	2017-08-23
PH00015	auto	0.27	false	SUV	Mateo	Rossi	1989-03-04	male	married	Sales Manager	69000	35 Via Roma	Milan	MI	20121	Italy	PO00015	2022-01-15	blank	1475	32	802	blank	blank	blank	blank	blank	0	blank
PH00016	home	0.43	false	blank	Fatima	Al-Farsi	1975-06-18	female	married	Accountant	51000	12 Al Qasr St	Muscat	blank	112	Oman	PO00016	2021-10-01	blank	1180	46	765	235000	blank	blank	blank	blank	1	2021-02-01
PH00017	health	0.35	false	blank	Samuel	Adebayo	1965-12-05	male	married	Doctor	130000	51 Victoria Island	Lagos	blank	101241	Nigeria	PO00017	2019-12-31	2023-12-31	2510	54	796	blank	blank	blank	blank	blank	0	blank
PH00018	home	0.38	false	blank	Haruto	Tanaka	1970-11-11	male	divorced	Project Manager	61000	14-8 Shibuya	Tokyo	blank	150-0002	Japan	PO00018	2021-02-10	blank	1725	50	734	395000	blank	blank	blank	blank	1	2018-08-16
PH00019	auto	0.53	false	compact	Isabella	Martinez	2002-05-14	female	single	Student	2000	12 Calle 60	Merida	YUC	97000	Mexico	PO00019	2020-06-01	blank	320	18	577	blank	blank	blank	blank	blank	0	blank
PH00020	home	0.21	false	blank	Ava	Wilson	1980-09-19	female	married	Marketing Director	98000	56 Elm St	New York	NY	10001	USA	PO00020	2021-11-20	blank	2765	41	828	720000	blank	blank	blank	blank	0	blank

What the 200 rows show

from the 200-row sample

Life (policy type) stands out: mean risk_score is 0.89, against 0.33 for the rest.

13%risk_factors_smoker = true
0.35median risk_score
4genders
5marital statuses
5claim statuses
56address countries

Mean risk_score by policy_type200 rows

risk_score200 rows, in bands of 0.1

Median 0.35, from 0.11 to 1.0.

risk_factors_vehicle_type66 rows with a value · 134 left blank

sedan15
hatchback9
SUV9
compact6
Hatchback5
Sedan5
coupe4
suv3
convertible2
Coupe2

30 columns by typefrom the column list below

string 16
integer 3
float 5
date 5
boolean 1

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 16 columns
`policyholder_id`	string	Unique identifier for each insurance policyholderunique	PH00001
`first_name`	string	Policyholder's first name	Carla
`last_name`	string	Policyholder's last name	Muller
`gender`	string	Policyholder's gendermale · female · other · prefer_not_to_say · optional	female
`marital_status`	string	Policyholder's marital statussingle · married · divorced · widowed · other · optional	widowed
`occupation`	string	Policyholder's occupation or job titleoptional	Retired
`address_street`	string	Policyholder's street addressoptional	14 Ludwigstrasse
`address_city`	string	Policyholder's cityoptional	Munich
`address_state`	string	Policyholder's state or provinceoptional	VIC
`address_postal_code`	string	Policyholder's postal or ZIP codeoptional	80333
`address_country`	string	Policyholder's countryoptional	Germany
`policy_id`	string	Unique identifier for the insurance policyunique	PO00001
`policy_type`	string	Type of insurance policy (e.g., auto, home, life, health)auto · home · life · health · other	life
`risk_factors_vehicle_type`	string	Risk factor: Type of vehicle insured (if applicable)optional	sedan
`claim_id`	string	Unique identifier for a claim (if any); blank if no claimunique · optional	CL00001
`claim_status`	string	Status of the claim (if any)submitted · approved · rejected · pending · closed · optional	approved
Numbers 8 columns
`annual_income`	float	Policyholder's annual income in local currency0 or more · optional	12500
`premium_amount`	float	Annual premium amount for the policy0 or more	1850.25
`risk_factors_age`	integer	Risk factor: Age of the policyholder at policy start0 or more	59
`risk_factors_credit_score`	integer	Risk factor: Policyholder's credit score300 to 850 · optional	612
`risk_factors_property_value`	float	Risk factor: Value of insured property (if applicable)0 or more · optional	425000
`risk_score`	float	Overall risk score assigned to the policyholder for underwriting/pricing (0-1 scale)0 to 1	0.84
`claim_amount`	float	Amount claimed (if any)0 or more · optional	4500
`num_prior_claims`	integer	Number of prior claims made by the policyholder0 or more · optional	2
Dates and times 5 columns
`date_of_birth`	date	Policyholder's date of birth	1961-11-09
`policy_start_date`	date	Date when the policy became active	2020-10-01
`policy_end_date`	date	Date when the policy expires or was terminatedoptional	2023-03-01
`claim_date`	date	Date of claim submission (if any)optional	2022-04-17
`last_claim_date`	date	Date of the most recent prior claim (if any)optional	2017-07-12
True or false 1 column
`risk_factors_smoker`	boolean	Risk factor: Whether the policyholder is a smokeroptional	true

Use it for

An insurance dashboard
The risk_factors_smoker rate, risk_score by policy_type and a breakdown of risk_factors_vehicle_type. Excel, Power BI or Tableau.
Why do the 32 life rows have a mean risk_score of 0.89?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Policyholders200
PH000010.84life
PH000020.31auto
PH000040.91health
A software demo
Believable policyholders with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · insurance-risk-factor-simulation-table

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 30 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

Policyholder demographics and risk features
Claims count and severity over policy term
Assign composite risk scores
Policy type and region fields
Flag for high vs. low risk segments

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-risk-factor-simulation-table

What should your data show?

No signup. No card.

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?