Finance Loan Default Prediction

This dataset provides comprehensive, flat-structured records of individual loans and borrower profiles, including demographic, financial, and credit information, as well as loan terms, status, and default indicators. It is ideal for building predictive models for loan default risk, optimizing lending portfolios, and conducting borrower risk analysis in financial services.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Predicting loan default risk using machine learning models

Sample rows

preview · 8 of 200 rows · all 29 columns
loan_idstringloan_typestringloan_term_monthsintegerloan_defaultedbooleanborrower_address_countrystringborrower_idstringborrower_namestringborrower_ageintegerborrower_genderstringborrower_employment_statusstringborrower_annual_incomefloatborrower_credit_scoreintegerborrower_address_streetstringborrower_address_citystringborrower_address_statestringborrower_address_postal_codestringloan_amountfloatloan_interest_ratefloatloan_application_datedateloan_approval_datedateloan_statusstringloan_default_datedateloan_purposestringnumber_of_previous_loansintegernumber_of_previous_defaultsintegerborrower_employer_namestringborrower_years_with_employerfloatloan_collateral_typestringloan_collateral_valuefloat
A1C8D9FQauto48falseUSAB8ZT3HEmma Rodriguez35femaleemployed54000.756611245 Willow AveDenverCO80204250007.22023-03-142023-03-18activeblankPurchase of new vehicle20Greenway Motors6.5vehicle27000
K9E4L2RPbusiness60falseUSAC2MK7DNoah Patel41maleself-employed88000712963 South StreetAustinTX78701500008.52022-09-102022-09-15approvedblankBusiness expansion capital41Patel Consulting LLC10.8property65000
Z5X8J7NQpersonal36falseUSAH4VP9ELiam Kim28maleemployed620006787801 Oak LaneSan JoseCA95123150009.12023-01-202023-01-24activeblankMedical expenses10TechNova Inc2.3none0
R2G7S4WYmortgage180falseUSAN1BF6LOlivia Martin50femaleretired250007049052 Maple DrOrlandoFL3280112000052021-05-082021-05-13activeblankHome purchase51blankblankproperty140000
F6U2V5BJeducation60falseUSAS3QN4MLucas White23malestudent12000595480 College RdAnn ArborMI48104180006.92023-08-122023-08-17approvedblankCollege tuition fees00blankblankguarantor20000
P7T3B1XYauto72falseUSAL8AR5QSophia Brown30femaleemployed47000642284 Cedar StCincinnatiOH45202280006.52022-06-192022-06-23activeblankCar replacement20MediTech Solutions4.7vehicle31000
E6W9U2MGbusiness120falseUSAK3XJ2CCharlotte Nguyen45femaleself-employed950007551608 Pinecrest BlvdSeattleWA98115900005.82021-11-042021-11-09activeblankStore renovation62Nguyen Design Studio13.2property120000
J5H7Z8NCauto60falseUSAP6GL2FMason Clark32maleemployed530006334012 Birchwood AveSalt Lake CityUT84111350007.32022-02-272022-03-02approvedblankSUV purchase10Clark & Sons3.1vehicle37000

What the 200 rows show

from the 200-row sample

Mortgage (loan type) stands out: mean loan_term_months is 199.1, against 42.4 for the rest.

  • 2%loan_defaulted = true
  • 39median loan_term_months
  • 3borrower genders
  • 4borrower employment statuses
  • 5loan statuses
  • 5loan collateral types
Mean loan_term_months by loan_type200 rows
010020029.8person…75 rows48.0auto48 rows199.1mortga…22 rows63.8busine…32 rows42.0educat…23 rows
loan_term_months200 rows, in bands of 50
0651301304751060110200400loan_term_months →

Median 39, from 12 to 360.

borrower_address_country200 rows · top 10 of 23 values
  1. USA144
  2. UK10
  3. Australia6
  4. US6
  5. Canada3
  6. Ireland3
  7. India3
  8. Switzerland3
  9. DE3
  10. Portugal2
29 columns by typefrom the column list below
  • string 15
  • integer 5
  • float 5
  • date 3
  • boolean 1

Columns

29 columns in four groups
blueprint · 29 columns
columntypedescriptionexample
Text 15 columns
loan_idstringUnique identifier for each loan applicationuniqueA1C8D9FQ
borrower_idstringUnique identifier for each borrowerB8ZT3H
borrower_namestringFull name of the borrowerEmma Rodriguez
borrower_genderstringGender of the borrowermale · female · other · prefer_not_to_say · optionalfemale
borrower_employment_statusstringEmployment status of the borroweremployed · self-employed · unemployed · retired · studentemployed
borrower_address_streetstringStreet address of the borroweroptional1245 Willow Ave
borrower_address_citystringCity of the borroweroptionalDenver
borrower_address_statestringState or province of the borroweroptionalCO
borrower_address_postal_codestringPostal code of the borroweroptional80204
borrower_address_countrystringCountry of the borroweroptionalUSA
loan_typestringType of loan issued6 valuesauto
loan_statusstringCurrent status of the loan6 valuesactive
loan_purposestringPurpose for which the loan was takenoptionalPurchase of new vehicle
borrower_employer_namestringName of the borrower's employer (if employed)optionalGreenway Motors
loan_collateral_typestringType of collateral provided for the loan (if any)6 values · optionalvehicle
Numbers 10 columns
borrower_ageintegerAge of the borrower at loan application18 to 10035
borrower_annual_incomefloatAnnual income of the borrower in local currency0 or more54000.75
borrower_credit_scoreintegerCredit score of the borrower at the time of loan application300 to 900661
loan_amountfloatTotal amount of the loan issued0 or more25000
loan_term_monthsintegerDuration of the loan in months1 to 36048
loan_interest_ratefloatAnnual interest rate applied to the loan (percentage)0 to 1007.2
number_of_previous_loansintegerNumber of previous loans taken by the borrower0 or more · optional2
number_of_previous_defaultsintegerNumber of previous loans that were defaulted by the borrower0 or more · optional0
borrower_years_with_employerfloatNumber of years borrower has been with current employer0 or more · optional6.5
loan_collateral_valuefloatEstimated value of the collateral (if any)0 or more · optional27000
Dates and times 3 columns
loan_application_datedateDate when the loan application was submitted2023-03-14
loan_approval_datedateDate when the loan was approvedoptional2023-03-18
loan_default_datedateDate when the loan was marked as defaulted (if applicable)optional2024-03-12
True or false 1 column
loan_defaultedbooleanIndicates if the loan has defaulted (true/false)false

Use it for

  • loan defaulted2%3 of 200 rowsmean loan term months…29.8pers…48.0auto199.1mort…63.8busi…

    A finance dashboard

    The loan_defaulted rate, loan_term_months by loan_type and a breakdown of borrower_address_country. Excel, Power BI or Tableau.

  • Why do the 22 mortgage rows have a mean loan_term_months of 199.1?

    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, borrower_name and borrower_age to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 29 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 rows29 columns
Yours1,000 rows29 columnsborrower_address_street: UK only

blueprint · finance-loan-default-prediction

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 29 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
  • Loan type and purpose provided
  • Borrower credit scores included
  • Default status accurately flagged
  • Payment histories complete
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
finance-loan-default-prediction

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