Insurance
26 columns
200 rows
8 formats

Insurance Underwriting Risk Analytics

This dataset provides a unified view of insurance applicants, their policies, and claims, enabling advanced risk modeling and underwriting decision support. It includes detailed demographic, policy, and claim information, making it ideal for predictive analytics, fraud detection, and portfolio risk assessment.

opened 9 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Automated risk scoring and underwriting decision support

Sample rows

preview · 8 of 200 rows · all 26 columns

applicant_idstring	policy_statusstring	risk_scorefloat	claim_typestring	first_namestring	last_namestring	date_of_birthdate	genderstring	emailstring	phone_numberstring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	policy_idstring	policy_typestring	policy_start_datedate	policy_end_datedate	premium_amountfloat	coverage_amountfloat	claim_idstring	claim_datedate	claim_statusstring	claim_amountfloat	underwriting_decisionstring
A1001	active	0.42	accident	Liam	Nguyen	1991-06-14	male	liam.nguyen@example.com	+1-312-555-1298	1823 Wabash Ave	Chicago	IL	60616	US	P2001	auto	2023-07-01	2024-07-01	750.45	25000	C3001	2023-11-17	approved	5800.2	approved
A1002	active	0.13	blank	Sofia	Martinez	1969-01-19	female	sofia.martinez@correo.mx	+52 55 1298 7765	121 Calle Juarez	Mexico City	CDMX	04000	Mexico	P2002	life	2022-04-10	2027-04-09	3050.25	250000	blank	blank	blank	blank	approved
A1003	active	0.18	fire	Olivia	Schmidt	1985-09-03	female	olivia.schmidt@web.de	+49 30 9142 8812	52 Alexanderplatz	Berlin	Berlin	10178	Germany	P2003	home	2022-08-15	2025-08-14	2045.1	320000	C3002	2023-01-23	filed	24000.8	approved
A1004	active	0.33	health	Mohamed	Abdi	1995-04-21	male	m.abdi@africamail.com	+254 722 555 110	14 Moi Ave	Nairobi	Nairobi	00100	Kenya	P2004	health	2023-03-01	2024-03-01	510	18000	C3003	2023-08-12	closed	1250.5	approved
A1005	pending	0.41	blank	Zara	Patel	1998-12-08	female	zara.patel@gmail.com	+91 98 1298 6655	4 Marine Drive	Mumbai	MH	400001	India	P2005	travel	2024-05-15	2024-06-15	38.5	1200	blank	blank	blank	blank	needs_review
A1006	active	0.16	health	Lucas	Kim	1978-02-27	male	lucas.kim@naver.kr	+82 10 4312 8888	81 Gangnam-daero	Seoul	Seoul	06236	South Korea	P2006	life	2021-11-01	2026-10-31	4200.75	300000	C3004	2022-12-14	approved	15000	approved
A1007	active	0.25	blank	Emilia	Johansson	1988-11-15	female	emilia.johansson@live.se	+46 70 812 3321	34 Drottninggatan	Stockholm	Stockholm	11151	Sweden	P2007	health	2023-09-01	2024-09-01	670.8	19500	blank	blank	blank	blank	approved
A1008	expired	0.14	blank	Victor	Duarte	1970-07-28	male	victor.duarte@uol.br	+55 21 9988 5544	882 Av Atlantica	Rio de Janeiro	RJ	22070-011	Brazil	P2008	home	2020-09-15	2023-09-14	3800	180000	blank	blank	blank	blank	approved
A1009	active	0.66	health	Ava	Wilson	2002-03-22	female	ava.wilson@outlook.com	+1-425-555-1190	711 Pine St	Seattle	WA	98101	US	P2009	health	2023-10-01	2024-10-01	245	8000	C3005	2024-03-15	approved	500	needs_review
A1010	expired	0.08	blank	Yusuf	Farah	1956-05-02	male	yusuf.farah@somail.com	blank	21 Kismaayo Rd	Mogadishu	Benadir	25215	Somalia	P2010	life	2019-06-01	2024-06-01	3100.6	120000	blank	blank	blank	blank	approved
A1011	active	0.31	blank	Alice	Ng	1993-10-12	female	alice.ng@singnet.sg	+65 9123 5544	5 Orchard Rd	Singapore	SG	238839	Singapore	P2011	auto	2023-02-15	2024-02-15	875.99	28000	blank	blank	blank	blank	approved
A1012	active	0.39	blank	Maya	Bose	1983-12-30	female	maya.bose@indiamail.in	+91 80 5555 9910	99 MG Road	Bangalore	KA	560001	India	P2012	health	2023-06-10	2024-06-10	620	17200	blank	blank	blank	blank	approved
A1013	active	0.12	blank	Wei	Zhang	1976-08-22	male	wei.zhang@163.com	+86 21 8888 2200	88 Nanjing Road	Shanghai	SH	200001	China	P2013	life	2022-02-01	2027-02-01	3900	220000	blank	blank	blank	blank	approved
A1014	expired	0.21	theft	Helen	Brown	1989-05-27	female	h.brown@outlook.co.uk	+44 20 7123 8765	27 Baker St	London	ENG	W1U 8EQ	UK	P2014	home	2022-12-20	2023-12-20	1800	140000	C3006	2023-10-21	filed	8000	approved
A1015	active	0.28	blank	Samuel	Jones	1994-02-07	male	sam.jones@icloud.com	+1-415-555-2233	100 Market St	San Francisco	CA	94105	US	P2015	auto	2023-09-01	2024-09-01	895.6	26000	blank	blank	blank	blank	approved
A1016	pending	0.36	blank	Chloe	Dubois	1999-04-17	female	chloe.dubois@mail.fr	blank	22 Rue de Rivoli	Paris	IDF	75004	France	P2016	travel	2024-07-01	2024-08-01	25	1000	blank	blank	blank	blank	needs_review
A1017	active	0.23	blank	Kofi	Mensah	1982-02-11	male	kofi.mensah@ghana.com	+233 24 555 8888	10 Independence Ave	Accra	Greater Accra	GA123	Ghana	P2017	health	2023-04-01	2024-04-01	585	15000	blank	blank	blank	blank	approved
A1018	pending	0.38	blank	Fatima	Al-Farsi	1997-07-03	female	fatima.alfarsi@ommail.om	+968 9921 5544	77 Al Khuwair	Muscat	Muscat	113	Oman	P2018	travel	2024-06-01	2024-07-01	44.4	1800	blank	blank	blank	blank	needs_review
A1019	active	0.09	blank	Arjun	Singh	1974-03-08	male	arjun.singh@rediffmail.com	+91 11 6666 8899	42 Connaught Place	Delhi	DL	110001	India	P2019	life	2022-01-01	2027-01-01	4800	400000	blank	blank	blank	blank	approved
A1020	expired	0.11	fire	Anna	Kuznetsova	1965-11-05	female	anna.kuznetsova@mail.ru	+7 812 555 3344	12 Nevsky Prospekt	Saint Petersburg	SPB	191025	Russia	P2020	home	2019-04-01	2022-04-01	2650	110000	C3007	2021-02-16	closed	9000	approved

What the 200 rows show

from the 200-row sample

Pending (policy status) stands out: mean risk_score is 0.74, against 0.26 for the rest.

0.29median risk_score
3underwriting decisions
4genders
4claim statuses
6policy types
43address countries

Mean risk_score by policy_status200 rows

risk_score200 rows, in bands of 0.1

Median 0.29, from 0.01 to 0.99.

claim_type64 rows with a value · 136 left blank

health22
accident20
fire13
liability6
theft3

26 columns by typefrom the column list below

string 18
float 4
date 4

Columns

26 columns in three groups

blueprint · 26 columns
column	type	description	example
Text 18 columns
`applicant_id`	string	Unique identifier for each insurance applicantunique	A1001
`first_name`	string	Applicant's first name	Liam
`last_name`	string	Applicant's last name	Nguyen
`gender`	string	Applicant's gendermale · female · other · prefer_not_to_say · optional	male
`email`	string	Applicant's email addressunique	liam.nguyen@example.com
`phone_number`	string	Applicant's phone numberoptional	+1-312-555-1298
`address_street`	string	Applicant's street addressoptional	1823 Wabash Ave
`address_city`	string	Applicant's cityoptional	Chicago
`address_state`	string	Applicant's state or provinceoptional	IL
`address_postal_code`	string	Applicant's postal codeoptional	60616
`address_country`	string	Applicant's countryoptional	US
`policy_id`	string	Unique identifier for each insurance policyunique	P2001
`policy_type`	string	Type of insurance policy (e.g., auto, home, life, health)6 values	auto
`policy_status`	string	Current status of the policyactive · expired · cancelled · pending	active
`claim_id`	string	Unique identifier for each insurance claimunique · optional	C3001
`claim_type`	string	Type of claim (e.g., accident, theft, fire, health)6 values · optional	accident
`claim_status`	string	Current status of the claimfiled · approved · rejected · pending · closed · optional	approved
`underwriting_decision`	string	Final underwriting decision for the policyapproved · rejected · pending · needs_review · optional	approved
Numbers 4 columns
`premium_amount`	float	Amount paid for the policy premium (annualized)0 or more	750.45
`coverage_amount`	float	Total coverage amount provided by the policy0 or more	25000
`claim_amount`	float	Amount requested or paid for the claim0 or more · optional	5800.2
`risk_score`	float	Calculated risk score for the applicant/policy (0-1 scale)0 to 1 · optional	0.42
Dates and times 4 columns
`date_of_birth`	date	Applicant's date of birth	1991-06-14
`policy_start_date`	date	Date when the policy coverage begins	2023-07-01
`policy_end_date`	date	Date when the policy coverage ends	2024-07-01
`claim_date`	date	Date the claim was filedoptional	2023-11-17

Use it for

An insurance dashboard
Risk_score by policy_status and a breakdown of claim_type. Excel, Power BI or Tableau.
Why do the 21 pending rows have a mean risk_score of 0.74?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Applicants200
A10010.42active
A10020.13active
A10030.18active
A software demo
Believable applicants with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows26 columns

Yours1,000 rows26 columnsaddress_street: UK only

blueprint · insurance-underwriting-risk-analytics

Behind this dataset

Same schema. As many rows as you need.

These 200 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

Include applicant demographic and health data
Log policy attributes and claims history
Flag high-risk applicants
Aggregate by product and region
Link underwriting decision outcomes

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-underwriting-risk-analytics

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?