Insurance
25 columns
200 rows
8 formats

Healthcare Insurance Claim Scenarios

This dataset provides detailed records of healthcare insurance claim submissions, including patient demographics, provider information, claim amounts, reimbursement outcomes, denial reasons, and fraud indicators. It is ideal for operational analytics, process automation, and advanced fraud detection in healthcare insurance workflows.

last updated 21 Jul 2025
by GoMask

The brief that made it

Automated claims processing and workflow optimization

Sample rows

preview · 8 of 200 rows · all 25 columns

claim_idstring	patient_genderstring	claim_amountfloat	fraud_flagboolean	patient_countrystring	patient_idstring	provider_idstring	policy_idstring	claim_submission_datedate	service_datedate	reimbursed_amountfloat	claim_statusstring	denial_reason_codestring	denial_reason_descriptionstring	procedure_codestring	diagnosis_codestring	reviewed_bystring	review_datedate	patient_first_namestring	patient_last_namestring	patient_date_of_birthdate	patient_street_addressstring	patient_citystring	patient_statestring	patient_postal_codestring
CLM0000001	female	152.75	false	USA	PAT001	PROV101	POL1001	2024-03-14	2024-03-10	150	approved	blank	blank	99213	J06.9	REV01	2024-03-18	Emily	Turner	1992-01-23	452 Oakwood Ave	New York	NY	10013
CLM0000002	male	85	false	IND	PAT002	PROV102	POL1002	2024-03-23	2024-03-20	85	approved	blank	blank	90471	Z23	REV02	2024-03-25	Arjun	Menon	2016-11-12	B-12, Green Park	Bangalore	KA	560025
CLM0000003	male	2350	false	DEU	PAT003	PROV103	POL1003	2024-02-14	2024-02-12	2100	approved	blank	blank	27130	M16.9	REV03	2024-02-20	Hans	Schneider	1950-07-12	Königsallee 17	Düsseldorf	NW	40212
CLM0000004	female	67.99	false	USA	PAT004	PROV104	POL1004	2024-03-29	2024-03-27	60	approved	blank	blank	80053	E78.5	REV04	2024-03-31	Mia	Clark	1985-08-30	234 Pine St	San Francisco	CA	94111
CLM0000005	female	1295	false	USA	PAT005	PROV105	POL1005	2024-01-16	2024-01-15	0	denied	CO45	Charge exceeds fee schedule	45378	K63.5	REV05	2024-01-20	Linda	Martinez	1972-04-18	799 West End Ave	Chicago	IL	60614
CLM0000006	female	523.2	false	FRA	PAT006	PROV106	POL1006	2024-03-11	2024-03-10	480	approved	blank	blank	93000	I10	REV06	2024-03-15	Sophie	Dupont	1964-09-14	12 Rue de Lyon	Paris	IDF	75012
CLM0000007	male	305.5	false	USA	PAT007	PROV107	POL1007	2024-02-24	2024-02-23	305.5	approved	blank	blank	81001	N39.0	REV07	2024-02-27	James	Baker	1988-05-09	185 Main St	Boston	MA	02116
CLM0000008	female	112	false	IND	PAT008	PROV108	POL1008	2024-03-08	2024-03-08	112	approved	blank	blank	87635	J11.1	REV08	2024-03-10	Priya	Singh	1996-12-07	Flat 402, Lotus Towers	Mumbai	MH	400001
CLM0000009	male	108.5	false	ESP	PAT009	PROV109	POL1009	2024-02-17	2024-02-17	0	denied	CO50	Non-covered service	99214	R51	REV09	2024-02-21	Mateo	Lopez	2007-06-03	Calle Mayor 8	Madrid	MD	28013
CLM0000010	female	1487	false	ITA	PAT010	PROV110	POL1010	2024-03-20	2024-03-18	0	denied	PR96	Invalid diagnosis code	30140	J34.2	REV10	2024-03-24	Giulia	Romano	1958-11-22	Via Roma 24	Rome	LAZ	00184
CLM0000011	female	225.9	false	SGP	PAT011	PROV111	POL1011	2024-01-25	2024-01-24	200	approved	blank	blank	85025	D50.9	REV11	2024-01-28	Sarah	Ng	1989-03-12	18 Scotts Rd	Singapore	SG	228209
CLM0000012	male	95	false	CAN	PAT012	PROV112	POL1012	2024-03-05	2024-03-03	95	approved	blank	blank	90460	Z23	REV12	2024-03-07	Lucas	Parker	2012-05-01	600 Eglinton Ave	Toronto	ON	M4P1A6
CLM0000013	male	1760	false	GBR	PAT013	PROV113	POL1013	2024-02-13	2024-02-11	1760	approved	blank	blank	27447	M17.11	REV13	2024-02-18	William	Evans	1948-10-15	19 The Avenue	London	LND	W1D4QF
CLM0000014	male	5000	false	ARE	PAT014	PROV114	POL1014	2024-02-10	2024-02-09	0	denied	CO16	Claim lacks information	99285	R07.9	REV14	2024-02-15	Ahmed	Al Nuaimi	1969-12-01	Al Wahda St 10	Dubai	DU	00000
CLM0000015	female	400	false	SGP	PAT015	PROV115	POL1015	2024-03-02	2024-02-28	400	approved	blank	blank	11721	L60.0	REV15	2024-03-05	Chloe	Lam	1994-08-09	22 Nathan Rd	Hong Kong	HK	999077
CLM0000016	female	219.5	false	CAN	PAT016	PROV116	POL1016	2024-03-18	2024-03-15	219.5	approved	blank	blank	96372	E11.9	REV16	2024-03-20	Olivia	Anderson	1981-02-20	88 Queen St	Toronto	ON	M5H2N2
CLM0000017	female	50	false	IND	PAT017	PROV117	POL1017	2024-03-14	2024-03-13	50	approved	blank	blank	90471	Z23	REV17	2024-03-17	Ella	Kumar	2018-07-19	C-16, Juhu Lane	Mumbai	MH	400049
CLM0000018	male	1020.45	false	FRA	PAT018	PROV118	POL1018	2024-02-11	2024-02-10	1020.45	approved	blank	blank	43239	K25.9	REV18	2024-02-13	Jacob	Bernard	1977-05-23	7 Rue Victor Hugo	Paris	IDF	75008
CLM0000019	female	150	false	USA	PAT019	PROV119	POL1019	2024-03-21	2024-03-20	0	denied	CO109	Claim not covered by plan	99283	R42	REV19	2024-03-25	Emma	Johnson	2009-10-11	95 Lake St	Seattle	WA	98101
CLM0000020	male	893	false	DEU	PAT020	PROV120	POL1020	2024-02-26	2024-02-25	893	approved	blank	blank	74177	R10.9	REV20	2024-02-28	Lucas	Schmidt	1990-06-17	Kurfürstenstrasse 4	Berlin	BE	10785

What the 200 rows show

from the 200-row sample

9%fraud_flag = true
339.5median claim_amount
5claim statuses
47reviewed_by values
135.0median reimbursed_amount

patient_gender200 rows by value

claim_amount200 rows, in bands of 2,000

Median 339.5, from 50.0 to 10,100.

patient_country200 rows · top 10 of 29 values

USA26
CAN20
DEU19
GBR17
SGP16
ESP15
IND14
FRA11
BRA11
ARE8

25 columns by typefrom the column list below

string 18
float 2
date 4
boolean 1

Columns

25 columns in four groups

blueprint · 25 columns
column	type	description	example
Text 18 columns
`claim_id`	string	Unique identifier for each insurance claimunique	CLM0000001
`patient_id`	string	Unique identifier for the patient submitting the claim	PAT001
`provider_id`	string	Unique identifier for the healthcare provider or facility	PROV101
`policy_id`	string	Unique identifier for the patient's insurance policy	POL1001
`claim_status`	string	Current status of the claim6 values	approved
`denial_reason_code`	string	Code indicating the reason for claim denial, if applicableoptional	CO45
`denial_reason_description`	string	Description of the denial reason, if applicableoptional	Non-covered service
`procedure_code`	string	Code representing the medical procedure or service (e.g., CPT, HCPCS)	99213
`diagnosis_code`	string	Code representing the diagnosis related to the claim (e.g., ICD-10)	J06.9
`reviewed_by`	string	Identifier for the reviewer or claims adjuster, if applicableoptional	REV01
`patient_first_name`	string	Patient's first name	Emily
`patient_last_name`	string	Patient's last name	Turner
`patient_gender`	string	Patient's gendermale · female · other · unknown · optional	female
`patient_street_address`	string	Patient's street addressoptional	452 Oakwood Ave
`patient_city`	string	Patient's city of residenceoptional	New York
`patient_state`	string	Patient's state of residenceoptional	NY
`patient_postal_code`	string	Patient's postal codeoptional	10013
`patient_country`	string	Patient's country of residenceoptional	USA
Numbers 2 columns
`claim_amount`	float	Total amount claimed for reimbursement0 or more	152.75
`reimbursed_amount`	float	Amount reimbursed by the insurance provider0 or more · optional	150
Dates and times 4 columns
`claim_submission_date`	date	Date the claim was submitted	2024-03-14
`service_date`	date	Date the healthcare service was provided	2024-03-10
`review_date`	date	Date the claim was reviewed or adjudicatedoptional	2024-03-18
`patient_date_of_birth`	date	Patient's date of birth	1992-01-23
True or false 1 column
`fraud_flag`	boolean	Indicates if the claim is flagged for potential fraud	false

Use it for

An insurance dashboard
The fraud_flag rate, claim_amount by patient_gender and a breakdown of patient_country. Excel, Power BI or Tableau.
Why do 18 of 200 rows have fraud_flag = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
Claims200
CLM0000001152.75female
CLM00000288600female
CLM00000301000male
A software demo
Believable claims with patient_id, provider_id and policy_id to fill a screen in front of a buyer.

This dataset200 rows25 columns

Yours1,000 rows25 columnspatient_street_address: UK only

blueprint · healthcare-insurance-claim-scenarios

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 25 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

Claims linked to patient, procedure, and provider profiles
Denials based on policy, coding, or eligibility
Payout amounts computed per plan and procedure
Include appeals and resubmissions
Sensitive identifiers replaced with synthetic equivalents

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

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: healthcare-insurance-claim-scenarios

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

An insurance dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Insurance

What should your data show?