Synthetic Microloan Performance Dataset

This dataset provides detailed, synthetic records of small business and personal microloans, including origination, borrower demographics, repayment schedules, delinquency events, and recovery outcomes. It is ideal for developing and benchmarking credit risk models, analyzing financial inclusion, and studying loan performance patterns across diverse borrower segments.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Credit risk modeling and scorecard development

Sample rows

preview · 8 of 200 rows · all 30 columns
loan_idstringdelinquency_statusstringdays_past_dueintegerguarantor_presentbooleanpurposestringborrower_idstringloan_typestringloan_amountfloatcurrencystringinterest_ratefloatterm_monthsintegerissue_datedatematurity_datedaterepayment_frequencystringborrower_genderstringborrower_ageintegerborrower_employment_statusstringborrower_incomefloatborrower_countrystringborrower_citystringinstallment_amountfloattotal_paidfloatnum_payments_madeintegernum_payments_dueintegerdate_of_defaultdaterecovery_amountfloatrecovery_datedateclosed_datedatewrite_off_amountfloatcollateral_typestring
LN000001delinquent35falseemergency medicalBR1001personal800USD22.5122021-03-142022-03-14weeklyfemale23student3500USChicago173402052blank0blankblank0blank
LN000002current0trueinventory purchaseBR1002small_business25000EUR14.8362020-07-082023-07-08monthlymale41self_employed82000DEBerlin870278403236blank0blankblank0equipment
LN000003defaulted60falseappliance purchaseBR1003personal2000KES2862022-01-112022-07-11weeklyother21unemployed0KENairobi9099011262022-05-100blankblank1110blank
LN000004closed0truemachinery acquisitionBR1004small_business120000USD10.5242019-09-012021-09-01monthlymale52self_employed240000USDallas57001368002424blank0blank2021-09-010machinery
LN000005delinquent21falsetravelBR1005personal450NGN3532022-10-102023-01-10weeklyfemale19student1200NGLagos41164413blank0blankblank0blank
LN000006current0trueexpansion capitalBR1006small_business33000INR19.5182021-12-012023-06-01monthlymale38self_employed160000INMumbai2050246001218blank0blankblank0inventory
LN000007delinquent19falsedebt consolidationBR1007personal1200USD17122021-06-202022-06-20monthlyprefer_not_to_say29unemployed0USSeattle108756712blank0blankblank0blank
LN000008closed0trueequipment purchaseBR1008small_business88000GBP9.2482018-02-022022-02-02monthlymale45employed180000GBLondon22001056004848blank0blank2022-02-020equipment

What the 200 rows show

from the 200-row sample

Defaulted (delinquency status) stands out: mean days_past_due is 49.8, against 8.0 for the rest.

  • 44%guarantor_present = true
  • 0median days_past_due
  • 2loan types
  • 3repayment frequencies
  • 4borrower genders
  • 6borrower employment statuses
Mean days_past_due by delinquency_status200 rows
040800.0current66 rows18.0delinque…81 rows49.8defaulted17 rows0.0closed36 rows
days_past_due200 rows, in bands of 10
060120119381611644204080days_past_due →

Median 0, from 0 to 78.

purpose196 rows with a value · 4 left blank
  1. travel24
  2. appliance purchase21
  3. home improvement17
  4. education17
  5. inventory15
  6. inventory purchase10
  7. machinery acquisition10
  8. expansion capital10
  9. business expansion9
  10. emergency9
30 columns by typefrom the column list below
  • string 12
  • integer 5
  • float 7
  • date 5
  • boolean 1

Columns

30 columns in four groups
blueprint · 30 columns
columntypedescriptionexample
Text 12 columns
loan_idstringUnique identifier for each loan recorduniqueLN000001
borrower_idstringUnique identifier for the borrowerBR1001
loan_typestringType of loan issued (e.g., small business, personal)small_business · personalpersonal
currencystringCurrency code for the loan amount (ISO 4217 format)12 currenciesUSD
repayment_frequencystringFrequency of scheduled repaymentsmonthly · biweekly · weeklyweekly
purposestringStated purpose for the loan (e.g., working capital, education, home improvement)optionalemergency medical
borrower_genderstringGender of the borrowermale · female · other · prefer_not_to_say · optionalfemale
borrower_employment_statusstringEmployment status of the borrower at origination6 values · optionalstudent
borrower_countrystringCountry of residence of the borrower (ISO 3166-1 alpha-2 code)US
borrower_citystringCity of residence of the borroweroptionalChicago
delinquency_statusstringCurrent delinquency status of the loancurrent · delinquent · defaulted · recovered · closeddelinquent
collateral_typestringType of collateral pledged (if any)8 types · optionalequipment
Numbers 12 columns
loan_amountfloatPrincipal amount of the loan issued0 or more800
interest_ratefloatAnnual interest rate applied to the loan (percentage, e.g., 12.5)0 to 10022.5
term_monthsintegerOriginal loan term in months1 or more12
borrower_ageintegerAge of the borrower at loan origination18 or more · optional23
borrower_incomefloatAnnual income of the borrower at origination (in loan currency)0 or more · optional3500
installment_amountfloatScheduled installment payment amount0 or more17
total_paidfloatTotal amount paid by the borrower to date0 or more340
num_payments_madeintegerNumber of scheduled payments made0 or more20
num_payments_dueintegerNumber of scheduled payments due up to current date0 or more52
days_past_dueintegerNumber of days past due for the loan (0 if current)0 or more35
recovery_amountfloatAmount recovered after default (0 if not defaulted or no recovery)0 or more0
write_off_amountfloatAmount written off after default (0 if not written off)0 or more0
Dates and times 5 columns
issue_datedateDate when the loan was issued2021-03-14
maturity_datedateScheduled date for loan maturity2022-03-14
date_of_defaultdateDate when the loan was classified as defaulted (null if not defaulted)optional2022-05-10
recovery_datedateDate of recovery payment (null if not recovered)optional
closed_datedateDate when the loan was fully closed (null if still active)optional2021-09-01
True or false 1 column
guarantor_presentbooleanIndicates if a guarantor was present for the loanoptionalfalse

Use it for

  • guarantor pres…44%87 of 200 rowsmean days past due by…0.0curr…18.0deli…49.8defa…0.0clos…

    A finance dashboard

    The guarantor_present rate, days_past_due by delinquency_status and a breakdown of purpose. Excel, Power BI or Tableau.

  • Why do the 17 defaulted rows have a mean days_past_due of 49.8?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable loans with borrower_id, loan_type and loan_amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 30 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows30 columns
Yours1,000 rows30 columnsborrower_country: UK only

blueprint · synthetic-microloan-performance-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 30 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
  • Repayment schedules vary by loan type
  • Early repayment penalties modeled
  • Loan purpose impacts default rate
  • Demographics linked to loan portfolio
  • Delinquent loans tracked over time
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
synthetic-microloan-performance-dataset

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