Finance
27 columns
200 rows
8 formats

Microloan Repayment Behavior Sequences

This dataset provides detailed microloan repayment behavior sequences, including payment timing, amounts, and loan outcomes, linked to customer demographics and loan characteristics. It is designed to support credit risk modeling, repayment pattern analysis, and the development of responsible lending strategies for fintech providers. The flat structure enables easy integration with analytics platforms and machine learning workflows.

last updated 21 Jul 2025
by GoMask

The brief that made it

Credit risk modeling and scorecard development

Sample rows

preview · 8 of 200 rows · all 27 columns

repayment_sequence_idstring	final_loan_statusstring	days_in_arrearsinteger	defaultedboolean	repayment_schedule_typestring	customer_idstring	loan_idstring	loan_issue_datedate	loan_amountfloat	loan_term_daysinteger	interest_ratefloat	scheduled_repayment_countinteger	actual_repayment_countinteger	repayment_sequencestring	first_repayment_datedate	last_repayment_datedate	total_repaid_amountfloat	num_on_time_paymentsinteger	num_late_paymentsinteger	num_partial_paymentsinteger	num_missed_paymentsinteger	customer_ageinteger	customer_genderstring	customer_incomefloat	customer_location_citystring	customer_location_statestring	customer_location_countrystring
RSQ0001	repaid	0	false	weekly	CUST001	LN001	2023-01-15	320.5	28	18.5	4	4	OOOO	2023-01-22	2023-02-12	320.5	4	0	0	0	27	female	850	Atlanta	GA	USA
RSQ0002	repaid	7	false	monthly	CUST002	LN002	2022-11-05	1125	180	15.9	6	6	OOLOOO	2022-12-05	2023-05-05	1125	5	1	0	0	33	male	1900	Miami	FL	USA
RSQ0003	defaulted	76	true	monthly	CUST003	LN003	2022-07-18	5500	360	12	12	10	LOOLOOLLOOMM	2022-08-18	2023-06-18	4890	6	2	2	2	58	male	4100	San Diego	CA	USA
RSQ0004	active	0	false	lump-sum	CUST004	LN004	2023-02-10	180	14	29.5	1	1	P	2023-02-22	2023-02-22	90	0	0	1	0	21	female	170	Nairobi	Nairobi	Kenya
RSQ0005	repaid	5	false	monthly	CUST005	LN005	2022-06-01	2500	120	10	4	4	OLOO	2022-07-01	2022-10-01	2500	3	1	0	0	44	male	1550	Kampala	Central	Uganda
RSQ0006	written-off	7	true	lump-sum	CUST006	LN006	2023-03-14	55	7	35.7	1	0	M	blank	blank	0	0	0	0	1	19	male	0	Ibadan	Oyo	Nigeria
RSQ0007	repaid	15	false	monthly	CUST007	LN007	2022-09-20	6700	365	9	12	12	OOOOOLOLOOOO	2022-10-20	2023-09-20	6700	9	3	0	0	62	female	3150	Chicago	IL	USA
RSQ0008	defaulted	14	true	biweekly	CUST008	LN008	2023-04-01	275	30	25	2	1	OM	2023-04-15	2023-04-15	137.5	1	0	0	1	22	unknown	230	Lagos	Lagos	Nigeria
RSQ0009	repaid	0	false	monthly	CUST009	LN009	2022-05-15	1800	90	19.2	3	3	OOP	2022-06-15	2022-08-15	1700	2	0	1	0	29	female	1200	Phoenix	AZ	USA
RSQ0010	defaulted	14	true	weekly	CUST010	LN010	2023-05-10	72	14	31.5	2	1	ML	2023-05-17	2023-05-17	36	0	1	0	1	20	other	95	Mombasa	Coast	Kenya
RSQ0011	repaid	0	false	lump-sum	CUST011	LN011	2022-10-10	250	30	22	1	1	O	2022-11-09	2022-11-09	250	1	0	0	0	24	unknown	300	Addis Ababa	Addis Ababa	Ethiopia
RSQ0012	repaid	8	false	monthly	CUST012	LN012	2022-03-22	3100	90	13.7	3	3	OLO	2022-04-22	2022-06-22	3100	2	1	0	0	31	male	1750	Cape Town	Western Cape	South Africa
RSQ0013	written-off	80	true	monthly	CUST013	LN013	2021-12-11	12800	360	8.7	12	10	OOLLLOLOOMM	2022-01-11	2022-10-11	10950	5	4	1	2	61	male	3800	Dallas	TX	USA
RSQ0014	repaid	3	false	weekly	CUST014	LN014	2023-01-07	420	28	19	4	4	OLOO	2023-01-14	2023-02-04	420	3	1	0	0	26	female	980	Johannesburg	Gauteng	South Africa
RSQ0015	repaid	12	false	monthly	CUST015	LN015	2022-08-01	8750	365	9.2	12	12	OOOOOLOOOOLO	2022-09-01	2023-08-01	8750	10	2	0	0	55	female	2700	New York	NY	USA
RSQ0016	restructured	22	true	biweekly	CUST016	LN016	2023-02-14	860	60	17.5	4	3	OLPM	2023-02-28	2023-04-11	645	1	1	1	1	38	male	1200	Nairobi	Nairobi	Kenya
RSQ0017	repaid	10	false	monthly	CUST017	LN017	2022-04-25	3050	180	13	6	6	OOOLOL	2022-05-25	2022-10-25	3050	4	2	0	0	47	female	1680	Lusaka	Lusaka	Zambia
RSQ0018	defaulted	15	true	lump-sum	CUST018	LN018	2022-10-30	65	15	32	1	0	M	blank	blank	0	0	0	0	1	18	female	0	Mbale	Eastern	Uganda
RSQ0019	repaid	0	false	monthly	CUST019	LN019	2023-03-01	2100	90	14.5	3	3	OOP	2023-04-01	2023-06-01	2100	2	0	1	0	34	male	1480	Nairobi	Nairobi	Kenya
RSQ0020	restructured	19	true	biweekly	CUST020	LN020	2022-07-30	570	60	16.8	4	3	OLPM	2022-08-13	2022-09-24	410	1	1	1	1	28	female	840	Pretoria	Gauteng	South Africa

What the 200 rows show

from the 200-row sample

Written-off (final loan status) stands out: mean days_in_arrears is 49.5, against 9.8 for the rest.

36%defaulted = true
7median days_in_arrears
4customer genders
880.0median loan_amount
60median loan_term_days
18.4median interest_rate

Mean days_in_arrears by final_loan_status200 rows

days_in_arrears200 rows, in bands of 20

Median 7, from 0 to 135.

repayment_schedule_type200 rows · 5 values

monthly84
lump-sum43
weekly40
biweekly30
other3

27 columns by typefrom the column list below

string 10
integer 9
float 4
date 3
boolean 1

Columns

27 columns in four groups

blueprint · 27 columns
column	type	description	example
Text 10 columns
`repayment_sequence_id`	string	Unique identifier for each repayment sequence record.unique	RSQ0001
`customer_id`	string	Unique identifier for the microloan customer.	CUST001
`loan_id`	string	Unique identifier for the microloan associated with this repayment sequence.	LN001
`repayment_schedule_type`	string	Type of repayment schedule (e.g., weekly, biweekly, monthly, lump-sum).weekly · biweekly · monthly · lump-sum · other	weekly
`repayment_sequence`	string	Flat string encoding the sequence of repayment events (e.g., 'OLOOPP', where O=On-time, L=Late, O=On-time, P=Partial, etc.).	OOOO
`final_loan_status`	string	Final status of the loan (e.g., repaid, defaulted, written-off, restructured).repaid · defaulted · written-off · restructured · active	repaid
`customer_gender`	string	Gender of the customer.male · female · other · unknown · optional	female
`customer_location_city`	string	City where the customer resides.optional	Atlanta
`customer_location_state`	string	State or region where the customer resides.optional	GA
`customer_location_country`	string	Country where the customer resides.optional	USA
Numbers 13 columns
`loan_amount`	float	Total principal amount of the microloan issued.0 or more	320.5
`loan_term_days`	integer	Number of days for the loan term.1 or more	28
`interest_rate`	float	Annualized interest rate applied to the microloan (as a percentage, e.g., 12.5 for 12.5%).0 to 100	18.5
`scheduled_repayment_count`	integer	Total number of scheduled repayments for the loan.1 or more	4
`actual_repayment_count`	integer	Number of repayments actually made by the customer.0 or more	4
`total_repaid_amount`	float	Total amount repaid by the customer over the loan period.0 or more	320.5
`num_on_time_payments`	integer	Number of repayments made on or before the scheduled due date.0 or more	4
`num_late_payments`	integer	Number of repayments made after the scheduled due date.0 or more	0
`num_partial_payments`	integer	Number of repayments that were less than the scheduled amount.0 or more	0
`num_missed_payments`	integer	Number of scheduled repayments that were missed (no payment made).0 or more	0
`days_in_arrears`	integer	Total number of days the customer was in arrears during the loan period.0 or more · optional	0
`customer_age`	integer	Age of the customer at the time of loan issuance.18 or more · optional	27
`customer_income`	float	Self-reported or verified monthly income of the customer at loan application.0 or more · optional	850
Dates and times 3 columns
`loan_issue_date`	date	Date the microloan was issued to the customer.	2023-01-15
`first_repayment_date`	date	Date of the first repayment made by the customer.optional	2023-01-22
`last_repayment_date`	date	Date of the last repayment made by the customer.optional	2023-02-12
True or false 1 column
`defaulted`	boolean	Indicates whether the loan was defaulted (true) or not (false).	false

Use it for

A finance dashboard
The defaulted rate, days_in_arrears by final_loan_status and a breakdown of repayment_schedule_type. Excel, Power BI or Tableau.
Why do the 22 written-off rows have a mean days_in_arrears of 49.5?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Repayment sequences200
RSQ00010repaid
RSQ000376defaulted
RSQ00067written-…
A software demo
Believable repayment sequences with customer_id, loan_id and loan_issue_date to fill a screen in front of a buyer.

This dataset200 rows27 columns

Yours1,000 rows27 columnscustomer_location_city: UK only

blueprint · microloan-repayment-behavior-sequences

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 27 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

Sequential repayment events per borrower.
Loan terms, missed payments, and prepayments logged.
Borrower demographic attached.
Time to default or closure marked.

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: microloan-repayment-behavior-sequences

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?