Insurance
20 columns
95 rows
8 formats

Policyholder Premium Payment Records

This dataset provides detailed records of insurance premium payments, including payment status, due dates, amounts, and policyholder details. It enables monitoring of timely, late, or missed payments for risk assessment, operational efficiency, and customer management. The dataset is ideal for payment analytics, risk modeling, and improving premium collection processes.

last updated 12 Jul 2025
by GoMask

The brief that made it

Monitor and reduce late or missed premium payments

Sample rows

preview · 8 of 95 rows · all 20 columns

payment_idstring	payment_statusstring	premium_amount_paidfloat	payment_methodstring	policyholder_idstring	policy_idstring	premium_due_datedate	payment_datedate	premium_amount_duefloat	days_lateinteger	collector_idstring	policyholder_namestring	policyholder_emailstring	policyholder_phonestring	policyholder_address_streetstring	policyholder_address_citystring	policyholder_address_statestring	policyholder_address_postal_codestring	policyholder_address_countrystring	notesstring
PMT0001	paid	1125.75	credit_card	PH001	POL1001	2023-01-15	2023-01-13	1125.75	0	AGT21	John Whittaker	jwhittaker@email.com	+1-281-555-0197	100 Maple Ave	Houston	TX	77005	USA	Paid in advance
PMT0002	late	950	bank_transfer	PH002	POL1002	2023-01-20	2023-01-27	950	7	AGT17	Lisa Kim	lisa.kim@fastmail.com	+1-650-555-0113	45 Shady Glen	Palo Alto	CA	94301	USA	Late due to bank holidays
PMT0003	missed	0	blank	PH003	POL1003	2023-02-01	blank	1220.5	0	AGT12	Maria Singh	maria.singh@email.com	+44 20 7946 0998	70 High Street	London	Greater London	SW1A 1AA	UK	No payment received
PMT0004	partial	500	cash	PH004	POL1004	2023-02-10	2023-02-13	715	3	AGT09	Jennifer Muller	jmuller@outlook.com	+49 1512 555555	12 Berliner Str	Berlin	Berlin	10115	Germany	Partial payment, follow up
PMT0005	paid	825.25	online_portal	PH005	POL1005	2023-03-01	2023-03-01	825.25	0	SYS01	David Li	david.li@domain.org	+1-212-555-0183	78 Central Park W	New York	NY	10023	USA	blank
PMT0006	missed	0	blank	PH006	POL1006	2023-03-15	blank	1599.99	0	AGT04	Thomas Lefevre	thomas.lefevre@frmail.fr	+33 1 22 33 44 55	23 Rue de Rivoli	Paris	Île-de-France	75004	France	Second missed payment
PMT0007	paid	980	bank_transfer	PH007	POL1007	2023-01-31	2023-01-30	980	0	AGT23	Anna Kowalski	anna.kowalski@wp.pl	+48 12 345 6789	5 Nowa	Krakow	Malopolskie	30-001	Poland	blank
PMT0008	late	1300	check	PH008	POL1008	2023-04-01	2023-04-09	1300	8	AGT13	Peter Edwards	peter.edwards@gmail.com	+1-415-555-0118	88 Lombard St	San Francisco	CA	94133	USA	Delay due to check clearance
PMT0009	missed	0	blank	PH009	POL1009	2023-02-15	blank	1050	0	AGT07	Omar Al-Farsi	o.alfarsi@arabmail.ae	+971 50 123 4567	15 Sheikh Zayed Rd	Dubai	Dubai	00000	UAE	No response from client
PMT0010	late	950	online_portal	PH002	POL1002	2023-02-20	2023-02-24	950	4	SYS01	Lisa Kim	lisa.kim@fastmail.com	+1-650-555-0113	45 Shady Glen	Palo Alto	CA	94301	USA	blank
PMT0011	paid	765.5	credit_card	PH010	POL1010	2023-03-22	2023-03-22	765.5	0	AGT18	Samantha Klein	s.klein@webmail.de	+49 160 5555555	2 Hauptstrasse	Munich	Bavaria	80331	Germany	blank
PMT0012	missed	0	blank	PH011	POL1011	2023-02-18	blank	490	0	SYS01	Elena Rossi	e.rossi@italymail.it	+39 055 1234567	Via Roma 10	Florence	Tuscany	50123	Italy	Awaiting client update
PMT0013	paid	1100	bank_transfer	PH012	POL1012	2023-04-12	2023-04-12	1100	0	AGT28	Miguel Torres	miguel.torres@correo.mx	+52 55 5555 5555	Calle Reforma 200	Mexico City	CDMX	06600	Mexico	blank
PMT0014	late	1425	check	PH013	POL1013	2023-03-14	2023-03-20	1425	6	AGT19	Aisha Patel	aisha.patel@india.com	+91 22 2345 6789	55 MG Road	Mumbai	Maharashtra	400001	India	blank
PMT0015	missed	0	blank	PH014	POL1014	2023-04-05	blank	730	0	AGT15	Rachel Evans	r.evans@btinternet.com	+44 1632 960123	14 King St	Manchester	Greater Manchester	M1 1AA	UK	Policyholder contacted for payment
PMT0016	paid	905.5	online_portal	PH015	POL1015	2023-03-10	2023-03-10	905.5	0	SYS02	Lucas Martin	lucas.martin@frmail.fr	+33 6 12 34 56 78	8 Rue des Fleurs	Lyon	Auvergne-Rhône-Alpes	69001	France	blank
PMT0017	missed	0	blank	PH002	POL1002	2023-03-20	blank	950	0	AGT17	Lisa Kim	lisa.kim@fastmail.com	+1-650-555-0113	45 Shady Glen	Palo Alto	CA	94301	USA	3rd attempt pending
PMT0018	paid	675	cash	PH016	POL1016	2023-03-12	2023-03-12	675	0	AGT11	Sophia Zhang	sophia.zhang@domain.cn	+86 10 1234 5678	22 Dongzhimen	Beijing	Beijing	100007	China	blank
PMT0019	late	825	credit_card	PH017	POL1017	2023-04-08	2023-04-14	825	6	AGT21	William Brown	will.brown@usmail.com	+1-917-555-0101	99 5th Ave	Brooklyn	NY	11215	USA	Paid after reminder
PMT0020	missed	0	blank	PH018	POL1018	2023-04-22	blank	1200	0	AGT02	Natalia Petrova	n.petrova@mail.ru	+7 495 123-45-67	60 Nevsky Prospect	St Petersburg	Saint Petersburg	190000	Russia	Client unreachable

What the 95 rows show

from the 95-row sample

Missed (payment status) stands out: mean premium_amount_paid is 0.0, against 658.0 for the rest.

410.5median premium_amount_paid
678.8median premium_amount_due
0median days_late

Mean premium_amount_paid by payment_status95 rows

premium_amount_paid95 rows, in bands of 200

Median 410.5, from 0.0 to 1,430.

payment_method70 rows with a value · 25 left blank

bank_transfer16
credit_card15
online_portal14
check13
cash12

20 columns by typefrom the column list below

string 15
integer 1
float 2
date 2

Columns

20 columns in three groups

blueprint · 20 columns
column	type	description	example
Text 15 columns
`payment_id`	string	Unique identifier for each premium payment recordunique	PMT0001
`policyholder_id`	string	Unique identifier for the policyholder	PH001
`policy_id`	string	Unique identifier for the insurance policy	POL1001
`payment_status`	string	Status of the payment (e.g., paid, late, missed, partial)paid · late · missed · partial	paid
`payment_method`	string	Method used for payment (e.g., bank_transfer, credit_card, cash, check, online_portal)bank_transfer · credit_card · cash · check · online_portal · optional	credit_card
`collector_id`	string	Identifier for the agent or system that collected the paymentoptional	AGT21
`policyholder_name`	string	Full name of the policyholderoptional	John Whittaker
`policyholder_email`	string	Email address of the policyholderoptional	jwhittaker@email.com
`policyholder_phone`	string	Phone number of the policyholderoptional	+1-281-555-0197
`policyholder_address_street`	string	Street address of the policyholderoptional	100 Maple Ave
`policyholder_address_city`	string	City of the policyholder's addressoptional	Houston
`policyholder_address_state`	string	State or province of the policyholder's addressoptional	TX
`policyholder_address_postal_code`	string	Postal code of the policyholder's addressoptional	77005
`policyholder_address_country`	string	Country of the policyholder's addressoptional	USA
`notes`	string	Additional notes or comments about the payment recordoptional	Paid in advance
Numbers 3 columns
`premium_amount_due`	float	Amount of premium due for the payment period0 or more	1125.75
`premium_amount_paid`	float	Amount of premium actually paid by the policyholder0 or more · optional	1125.75
`days_late`	integer	Number of days the payment was late (0 if on time or missed entirely)0 or more · optional	0
Dates and times 2 columns
`premium_due_date`	date	Date when the premium payment is due	2023-01-15
`payment_date`	date	Date when the premium payment was madeoptional	2023-01-13

Use it for

An insurance dashboard
Premium_amount_paid by payment_status and a breakdown of payment_method. Excel, Power BI or Tableau.
Why do the 25 missed rows have a mean premium_amount_paid of 0.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Payments95
PMT00011125.75paid
PMT0002950late
PMT00030missed
A software demo
Believable payments with policyholder_id, policy_id and premium_due_date to fill a screen in front of a buyer.

This dataset95 rows20 columns

Yours1,000 rows20 columnspolicyholder_address_street: UK only

blueprint · policyholder-premium-payment-records

Behind this dataset

Same schema. As many rows as you need.

These 95 rows came out of a blueprint — 20 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

Log payment date and amount
Flag overdue payments
Calculate days late if applicable
Aggregate by policy type
Alert for consecutive missed payments

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: policyholder-premium-payment-records

What should your data show?

No signup. No card.

Sample rows

What the 95 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?