Finance
14 columns
200 rows
8 formats

Consumer Finance Late Payment Events

This dataset provides detailed records of overdue consumer finance payments, including payment amounts, overdue durations, categorized reasons for lateness, and collections process status. It enables comprehensive risk modeling, collections workflow optimization, and regulatory compliance analysis for financial institutions managing consumer credit portfolios.

last updated 21 Jul 2025
by GoMask

The brief that made it

Credit risk modeling and customer segmentation

Sample rows

preview · 8 of 200 rows · all 14 columns

event_idstring	collection_stagestring	amount_duefloat	contacted_customerboolean	late_payment_reasonstring	customer_idstring	account_idstring	event_datedate	due_datedate	days_overdueinteger	amount_paidfloat	payment_statusstring	customer_risk_scorefloat	notesstring
EVT00001	none	115.75	false	forgot_to_pay	CUST001	ACCT001	2024-03-15	2024-03-12	3	115.75	paid	15.2	Customer forgot to pay, resolved quickly with no reminder.
EVT00002	reminder_sent	824.9	true	insufficient_funds	CUST002	ACCT002	2023-07-05	2023-06-22	13	0	unpaid	56.8	Customer contacted regarding insufficient funds, payment plan discussed.
EVT00003	resolved	49	false	technical_issue	CUST003	ACCT003	2022-09-19	2022-09-17	2	49	paid	7.5	Payment delayed due to app technical issue; resolved automatically.
EVT00004	reminder_sent	1250	true	medical_emergency	CUST004	ACCT004	2021-12-11	2021-11-11	30	300	partially_paid	62.4	Partial payment after medical emergency; customer expects to pay balance soon.
EVT00005	in_collections	3800	true	lost_job	CUST002	ACCT006	2022-02-22	2021-12-10	74	0	in_collections	95.7	Escalated to collections due to unemployment; no payments made.
EVT00006	none	120.06	false	forgot_to_pay	CUST005	ACCT005	2023-04-17	2023-04-12	5	120.06	paid	10.9	Customer forgot to pay; paid in full after email reminder.
EVT00007	reminder_sent	650.5	true	medical_emergency	CUST006	ACCT006	2021-05-04	2021-04-04	30	0	unpaid	51	Medical emergency reported; awaiting customer response.
EVT00008	reminder_sent	210	true	insufficient_funds	CUST007	ACCT007	2023-06-20	2023-06-10	10	84	partially_paid	46.4	Partial payment made due to insufficient funds; follow-up scheduled.
EVT00009	legal_action	2200	true	lost_job	CUST008	ACCT008	2022-11-30	2022-09-30	61	0	in_collections	98.4	No payment after 2 months overdue and job loss; legal action initiated.
EVT00010	reminder_sent	900	true	dispute	CUST009	ACCT009	2023-01-12	2022-12-20	23	0	unpaid	68.5	Payment disputed by customer; under investigation.
EVT00011	resolved	185	false	forgot_to_pay	CUST010	ACCT010	2024-04-02	2024-03-28	5	185	paid	17.4	Quick payment after reminder; no further action required.
EVT00012	resolved	5400	true	lost_job	CUST011	ACCT011	2022-08-08	2022-06-10	59	0	written_off	100	Debt written off after long-term non-payment due to unemployment.
EVT00013	reminder_sent	760	true	dispute	CUST011	ACCT012	2021-09-02	2021-08-15	18	304	partially_paid	54.6	Partial payment; dispute in progress regarding additional charges.
EVT00014	none	67.5	false	technical_issue	CUST012	ACCT013	2023-11-27	2023-11-20	7	67.5	paid	8.2	Auto-pay technical issue resolved, payment processed.
EVT00015	none	225	false	other	CUST013	ACCT014	2022-03-19	2022-03-04	15	0	unpaid	0	Edge case: customer never paid small balance, no further action.
EVT00016	resolved	140	false	forgot_to_pay	CUST014	ACCT015	2021-08-16	2021-08-10	6	140	paid	22.5	Customer forgot due date, paid after notification.
EVT00017	reminder_sent	980	true	insufficient_funds	CUST003	ACCT016	2023-04-29	2023-03-29	31	0	unpaid	55.9	Customer notified of outstanding balance; insufficient funds.
EVT00018	reminder_sent	1120	true	medical_emergency	CUST015	ACCT017	2024-01-17	2023-12-17	31	224	partially_paid	64.2	Customer made partial payment after hospitalization.
EVT00019	reminder_sent	1350	true	insufficient_funds	CUST016	ACCT018	2022-10-25	2022-09-25	30	405	partially_paid	49.8	Customer paid 30% of balance, financial hardship claimed.
EVT00020	none	98	false	forgot_to_pay	CUST017	ACCT019	2023-08-08	2023-07-31	8	98	paid	6.7	Quick payment after SMS reminder.

What the 200 rows show

from the 200-row sample

Legal_action (collection stage) stands out: mean amount_due is 10,850, against 792.0 for the rest.

57%contacted_customer = true
323.0median amount_due
5payment statuses
13.5median days_overdue
45.0median amount_paid
48.0median customer_risk_score

Mean amount_due by collection_stage200 rows

amount_due200 rows, in bands of 2,500

Median 323.0, from 0.0 to 22,650.

late_payment_reason200 rows · 7 values

forgot_to_pay49
insufficient_funds33
lost_job30
dispute30
technical_issue26
medical_emergency21
other11

14 columns by typefrom the column list below

string 7
integer 1
float 3
date 2
boolean 1

Columns

14 columns in four groups

blueprint · 14 columns
column	type	description	example
Text 7 columns
`event_id`	string	Unique identifier for each late payment eventunique	EVT00001
`customer_id`	string	Unique identifier for the customer associated with the late payment event	CUST001
`account_id`	string	Unique identifier for the financial account related to the late payment	ACCT001
`payment_status`	string	Current status of the overdue payment5 values	paid
`late_payment_reason`	string	Categorized reason for the late payment7 values	forgot_to_pay
`collection_stage`	string	Stage of the collections process for the overdue payment5 values · optional	none
`notes`	string	Additional notes or comments regarding the late payment eventoptional	Customer forgot to pay, r…
Numbers 4 columns
`days_overdue`	integer	Number of days the payment is overdue as of the event date1 or more	3
`amount_due`	float	Total amount that was due on the payment date0 or more	115.75
`amount_paid`	float	Amount paid by the customer as of the event date (may be zero)0 or more	115.75
`customer_risk_score`	float	Risk score assigned to the customer at the time of the event (e.g., 0-1 or 0-100 scale)0 to 100 · optional	15.2
Dates and times 2 columns
`event_date`	date	Date when the late payment event was recorded	2024-03-15
`due_date`	date	Original due date for the payment	2024-03-12
True or false 1 column
`contacted_customer`	boolean	Indicates whether the customer has been contacted regarding the overdue payment	false

Use it for

A finance dashboard
The contacted_customer rate, amount_due by collection_stage and a breakdown of late_payment_reason. Excel, Power BI or Tableau.
Why do the 16 legal_action rows have a mean amount_due of 10,850?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Events200
EVT00001115.75none
EVT00002824.9reminder…
EVT000041250reminder…
A software demo
Believable events with customer_id, account_id and event_date to fill a screen in front of a buyer.

This dataset200 rows14 columns

Yours1,000 rows14 columns

blueprint · consumer-finance-late-payment-events

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 14 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

Account ID and payment date.
Days late and late fee.
Reason for delay coded.
Resolution status recorded.

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: consumer-finance-late-payment-events

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A finance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Finance

What should your data show?