Microloan Repayment Performance Records
This dataset provides granular microloan repayment histories, including payment timing, amounts, borrower demographics, and loan characteristics, across emerging markets and fintech platforms. It is ideal for developing and benchmarking credit scoring models, analyzing repayment patterns, and understanding risk factors in microfinance. The dataset supports advanced analytics for financial inclusion and responsible lending.
Sample rows
preview · 8 of 200 rows · all 21 columns| repayment_idstring | repayment_statusstring | days_lateinteger | is_final_repaymentboolean | currencystring | loan_idstring | borrower_idstring | repayment_datedate | scheduled_repayment_datedate | repayment_amountfloat | scheduled_repayment_amountfloat | loan_issue_datedate | loan_amountfloat | loan_term_monthsinteger | interest_ratefloat | borrower_countrystring | borrower_genderstring | borrower_birth_yearinteger | borrower_incomefloat | borrower_employment_statusstring | lender_idstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| R0001 | on_time | 0 | false | USD | L00001 | B00001 | 2023-01-15 | 2023-01-10 | 520.75 | 520.75 | 2022-12-01 | 10000 | 24 | 7.9 | US | male | 1992 | 8200 | employed | LN001 |
| R0002 | on_time | 0 | false | USD | L00001 | B00001 | 2023-02-12 | 2023-02-10 | 520.75 | 520.75 | 2022-12-01 | 10000 | 24 | 7.9 | US | male | 1992 | 8200 | employed | LN001 |
| R0003 | on_time | 0 | false | USD | L00001 | B00001 | 2023-03-12 | 2023-03-10 | 520.75 | 520.75 | 2022-12-01 | 10000 | 24 | 7.9 | US | male | 1992 | 8200 | employed | LN001 |
| R0004 | partial | 2 | false | INR | L00002 | B00002 | 2023-07-18 | 2023-07-12 | 105.5 | 120 | 2023-06-01 | 2000 | 12 | 15.2 | IN | female | 1997 | 4000 | self_employed | LN002 |
| R0005 | missed | 8 | false | INR | L00002 | B00002 | 2023-08-14 | 2023-08-12 | 0 | 120 | 2023-06-01 | 2000 | 12 | 15.2 | IN | female | 1997 | 4000 | self_employed | LN002 |
| R0006 | on_time | 0 | false | GBP | L00003 | B00003 | 2023-03-12 | 2023-03-10 | 260.5 | 260.5 | 2023-01-01 | 3000 | 12 | 5.5 | GB | female | 1987 | 9000 | employed | LN003 |
| R0007 | on_time | 0 | false | GBP | L00003 | B00003 | 2023-04-13 | 2023-04-10 | 260.5 | 260.5 | 2023-01-01 | 3000 | 12 | 5.5 | GB | female | 1987 | 9000 | employed | LN003 |
| R0008 | on_time | 0 | false | GBP | L00003 | B00003 | 2023-05-10 | 2023-05-10 | 260.5 | 260.5 | 2023-01-01 | 3000 | 12 | 5.5 | GB | female | 1987 | 9000 | employed | LN003 |
| R0009 | on_time | 0 | false | EUR | L00004 | B00004 | 2022-12-05 | 2022-12-01 | 1200.5 | 1200.5 | 2022-10-01 | 15000 | 12 | 6.1 | DE | male | 1980 | 13000 | employed | LN004 |
| R0010 | on_time | 0 | false | EUR | L00004 | B00004 | 2023-01-02 | 2023-01-01 | 1200.5 | 1200.5 | 2022-10-01 | 15000 | 12 | 6.1 | DE | male | 1980 | 13000 | employed | LN004 |
| R0011 | on_time | 0 | false | KES | L00005 | B00005 | 2023-02-15 | 2023-02-10 | 800 | 800 | 2022-12-01 | 16000 | 24 | 19.8 | KE | female | 1989 | 7800 | employed | LN005 |
| R0012 | partial | 3 | false | BRL | L00006 | B00006 | 2023-01-13 | 2023-01-10 | 350 | 400 | 2022-12-01 | 4000 | 12 | 22.4 | BR | male | 1995 | 3500 | self_employed | LN006 |
| R0013 | missed | 10 | false | ZAR | L00007 | B00007 | 2023-03-12 | 2023-03-10 | 0 | 320 | 2022-12-01 | 6400 | 12 | 17.7 | ZA | female | 1984 | 2000 | unemployed | LN007 |
| R0014 | early | 0 | false | CAD | L00008 | B00008 | 2023-02-09 | 2023-02-12 | 230 | 230 | 2022-12-01 | 2760 | 12 | 5.6 | CA | female | 1999 | 2500 | student | LN008 |
| R0015 | late | 6 | false | NGN | L00009 | B00009 | 2023-01-16 | 2023-01-10 | 700 | 700 | 2022-12-01 | 14000 | 12 | 24.2 | NG | male | 1990 | 5000 | employed | LN009 |
| R0016 | partial | 5 | false | MXN | L00010 | B00010 | 2022-12-15 | 2022-12-10 | 100 | 120 | 2022-11-01 | 1200 | 12 | 18.7 | MX | female | 1994 | 2500 | self_employed | LN010 |
| R0017 | missed | 12 | false | USD | L00011 | B00011 | 2023-03-09 | 2023-03-09 | 0 | 1000 | 2022-12-01 | 12000 | 12 | 8.2 | US | unknown | 1976 | 0 | unknown | LN011 |
| R0018 | on_time | 0 | false | INR | L00012 | B00012 | 2022-11-12 | 2022-11-10 | 200 | 200 | 2022-10-05 | 2400 | 12 | 19.1 | IN | female | 1982 | 6000 | employed | LN012 |
| R0019 | partial | 1 | false | USD | L00013 | B00013 | 2023-04-11 | 2023-04-10 | 110 | 120 | 2023-02-01 | 2400 | 12 | 6.5 | US | male | 2003 | 1300 | student | LN013 |
| R0020 | missed | 3 | false | KES | L00014 | B00014 | 2023-05-13 | 2023-05-10 | 0 | 150 | 2023-01-01 | 1800 | 12 | 21.9 | KE | other | 1999 | 800 | unemployed | LN014 |
What the 200 rows show
from the 200-row sampleMissed (repayment status) stands out: mean days_
- 28%is_
final_ repayment = true - 0median days_
late - 4borrower genders
- 6borrower employment statuses
- 12borrower countries
- 462.5median repayment_
amount
Median 0, from 0 to 12.
- string 9
- integer 3
- float 5
- date 3
- boolean 1
Columns
21 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
repayment_id | string | Unique identifier for each repayment record.unique | R0001 |
loan_id | string | Unique identifier for the loan associated with this repayment. | L00001 |
borrower_id | string | Unique identifier for the borrower. | B00001 |
currency | string | Currency code (ISO 4217) for the repayment (e.g., USD, KES, INR). | USD |
repayment_status | string | Status of the repayment (e.g., on_time, late, missed, partial, early).on_time · late · missed · partial · early | on_time |
borrower_country | string | Country of the borrower (ISO 3166-1 alpha-2 code).12 countries | US |
borrower_gender | string | Gender of the borrower.male · female · other · unknown · optional | male |
borrower_employment_status | string | Employment status of the borrower.6 values · optional | employed |
lender_id | string | Unique identifier for the lender or institution.optional | LN001 |
| Numbers 8 columns | |||
repayment_amount | float | Amount paid in this repayment transaction.0 or more | 520.75 |
scheduled_repayment_amount | float | Amount that was scheduled to be paid for this installment.0 or more | 520.75 |
days_late | integer | Number of days the repayment was late (0 if on time or early).0 or more | 0 |
loan_amount | float | Total principal amount of the loan.0 or more | 10000 |
loan_term_months | integer | Original term of the loan in months.1 or more | 24 |
interest_rate | float | Annual interest rate for the loan (as a percentage, e.g., 12.5 for 12.5%).0 to 100 | 7.9 |
borrower_birth_year | integer | Year of birth of the borrower.1,900 to 2,024 · optional | 1992 |
borrower_income | float | Self-reported or estimated monthly income of the borrower in local currency.0 or more · optional | 8200 |
| Dates and times 3 columns | |||
repayment_date | date | Date the repayment was made. | 2023-01-15 |
scheduled_repayment_date | date | Date the repayment was originally scheduled. | 2023-01-10 |
loan_issue_date | date | Date the loan was issued to the borrower. | 2022-12-01 |
| True or false 1 column | |||
is_final_repayment | boolean | Indicates if this repayment completes the loan. | false |
Use it for
A finance dashboard
The is_
final_ repayment rate, days_ late by repayment_ status and a breakdown of currency. Excel, Power BI or Tableau. Why do the 39 missed rows have a mean days_
late of 6.5? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Repayments200R00010on_timeR00220on_timeR00260on_time
A software demo
Believable repayments with loan_
id, borrower_ id and repayment_ date to fill a screen in front of a buyer.
This dataset200 rows21 columns
Yours1,000 rows21 columnsborrower_country: UK only
blueprint · microloan-repayment-performance-records
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 21 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 scheduled and actual payments
- Include borrower demographics
- Identify partial and non-payments
- Flag loans in default
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-performance-records