Loan Default Prediction Records

This dataset provides detailed records of loan applications, borrower demographics, payment histories, and default outcomes, enabling comprehensive credit risk analysis and predictive modeling. It includes key features such as loan terms, payment behavior, applicant financials, and default indicators, making it highly valuable for developing credit scoring systems and risk management tools.

  • opened 7 times
  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Developing machine learning models to predict loan default risk

Sample rows

preview · 8 of 200 rows · all 29 columns
loan_idstringloan_purposestringloan_term_monthsintegerdefault_labelbooleanaddress_statestringcustomer_idstringapplication_datedateloan_amountfloatinterest_ratefloatemployment_statusstringannual_incomefloatcredit_scoreintegerdate_disburseddatefirst_payment_datedatetotal_payments_madeintegertotal_amount_paidfloatlast_payment_datedatecurrent_loan_statusstringdefault_datedateaddress_streetstringaddress_citystringaddress_postal_codestringaddress_countrystringageintegergenderstringmarital_statusstringdependentsintegerexisting_loans_countintegerexisting_loan_amount_totalfloat
LN0001personal36falseMAC0012022-03-121875011.5employed550006782022-03-152022-04-0123125802024-02-01currentblank172 Willow StBoston02110US33femalesingle100
LN0002home240falseNCC0022021-07-192750004.3employed1120007912021-07-232021-08-0531395002024-02-05currentblank84 Oakridge RdCharlotte28202US47malemarried2145000
LN0003car24falseNYC0032023-09-14125008.9employed470006542023-09-172023-10-01421402024-01-01lateblank910 Lincoln AveBuffalo14201US29malesingle000
LN0004home360falseONC0042020-02-214200003.7employed1560008512020-02-252020-03-1047715602024-02-10currentblank342 Lakeview CirTorontoM4V 1A1CA54malemarried2119000
LN0005personal12falseENGC0052023-01-13430021.5unemployed80004832023-01-152023-02-011246802024-01-01paid-offblank22 Primrose StManchesterM13 9PLUK22othersingle000
LN0006education24falseDC0062022-10-081350014.2student65006202022-10-122022-11-011372502023-11-01lateblank12 Rosemount AveDublinD09 F7T2IE19femalesingle000
LN0007home360falseNSWC0072023-05-2018000002.9self-employed4200008202023-05-252023-06-058395002024-01-05currentblank1012 Bridgewater AveSydney2000AU51malemarried32120000
LN0008personal18falseBCC0082021-11-22760019.7unemployed150004972021-11-262021-12-101879002023-05-10paid-offblank2448 Ash StVancouverV6H 1G3CA24femalesingle000

What the 200 rows show

from the 200-row sample

Home (loan purpose) stands out: mean loan_term_months is 336.3, against 35.2 for the rest.

  • 7%default_label = true
  • 36median loan_term_months
  • 4current loan statuses
  • 4genders
  • 5marital statuses
  • 6employment statuses
Mean loan_term_months by loan_purpose200 rows
0200400336.3home48 rows38.7car44 rows21.8educa…18 rows20.2perso…50 rows62.1busin…35 rows13.2other5 rows
loan_term_months200 rows, in bands of 50
0651301262061120228050250500loan_term_months →

Median 36, from 6 to 480.

address_state200 rows · top 10 of 35 values
  1. ENG35
  2. TX16
  3. ON13
  4. AUK13
  5. CA13
  6. NSW12
  7. IL8
  8. VIC8
  9. WGN8
  10. NY7
29 columns by typefrom the column list below
  • string 12
  • integer 6
  • float 5
  • date 5
  • boolean 1

Columns

29 columns in four groups
blueprint · 29 columns
columntypedescriptionexample
Text 12 columns
loan_idstringUnique identifier for each loan application recorduniqueLN0001
customer_idstringUnique identifier for the customer applying for the loanC001
loan_purposestringPurpose for which the loan is being taken (e.g., home, car, education, personal)6 valuespersonal
employment_statusstringCurrent employment status of the applicant6 valuesemployed
current_loan_statusstringCurrent status of the loan (e.g., current, late, defaulted, paid-off, closed)current · late · defaulted · paid-off · closedcurrent
address_streetstringStreet address of the applicant at the time of application172 Willow St
address_citystringCity of the applicant at the time of applicationBoston
address_statestringState or province of the applicant at the time of applicationMA
address_postal_codestringPostal code of the applicant at the time of application02110
address_countrystringCountry of the applicant at the time of application6 countriesUS
genderstringGender of the applicantmale · female · other · prefer_not_to_say · optionalfemale
marital_statusstringMarital status of the applicantsingle · married · divorced · widowed · other · optionalsingle
Numbers 11 columns
loan_amountfloatTotal amount of money requested in the loan application0 or more18750
loan_term_monthsintegerDuration of the loan in months1 or more36
interest_ratefloatAnnual interest rate applied to the loan (percentage)0 to 10011.5
annual_incomefloatApplicant's stated annual income in local currency0 or more55000
credit_scoreintegerApplicant's credit score at the time of application300 to 900678
total_payments_madeintegerTotal number of payments made by the borrower0 or more · optional23
total_amount_paidfloatTotal amount paid by the borrower towards the loan0 or more · optional12580
ageintegerAge of the applicant at the time of loan application18 or more33
dependentsintegerNumber of dependents supported by the applicant0 or more · optional1
existing_loans_countintegerNumber of other active loans held by the applicant at the time of application0 or more · optional0
existing_loan_amount_totalfloatTotal outstanding amount on all other loans held by the applicant0 or more · optional0
Dates and times 5 columns
application_datedateDate when the loan application was submitted2022-03-12
date_disburseddateDate when the loan was disbursed to the applicantoptional2022-03-15
first_payment_datedateDue date for the first scheduled loan paymentoptional2022-04-01
last_payment_datedateDate of the most recent payment made by the borroweroptional2024-02-01
default_datedateDate when the loan was officially marked as defaulted (if applicable)optional2022-04-05
True or false 1 column
default_labelbooleanIndicates whether the loan has defaulted (true) or not (false)false

Use it for

  • default label7%14 of 200 rowsmean loan term months…336.3home38.7car21.8educ…20.2pers…

    A finance dashboard

    The default_label rate, loan_term_months by loan_purpose and a breakdown of address_state. Excel, Power BI or Tableau.

  • Why do the 48 home rows have a mean loan_term_months of 336.3?

    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 customer_id, application_date and loan_amount 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 columnsaddress_street: UK only

blueprint · loan-default-prediction-records

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
  • Loans assigned applicant demographics and risk factors
  • Monthly payment records and status tracked
  • Default outcome labeled
  • Include loan type (mortgage, auto, personal)
  • Repayment history and grace periods simulated
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
loan-default-prediction-records

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