Loan Application Decision Records

This dataset provides detailed, anonymized records of loan applications and their automated approval or rejection outcomes, including applicant demographics, financial information, loan terms, and decision rationale. It is ideal for credit risk modeling, regulatory compliance auditing, and fairness analysis of lending algorithms.

  • last updated 29 Jul 2025
  • by GoMask
The brief that made it

Credit risk modeling and predictive analytics

Sample rows

preview · 8 of 200 rows · all 20 columns
application_idstringapplicant_employment_statusstringapplicant_credit_scorefloatis_existing_customerbooleanproperty_statestringapplication_datedatedecision_datetimedatetimeloan_amountfloatloan_term_monthsintegerloan_purposestringapplicant_incomefloatapplicant_ageintegerapplicant_genderstringapplicant_marital_statusstringapplicant_dependentsintegerproperty_postal_codestringdecisionstringdecision_reason_codestringinterest_ratefloatmonthly_paymentfloat
APP2021-0000001employed702.5falseCA2021-03-222021-03-23T11:35:072500060auto6600032femalemarried190017approvedgood_credit4.3462.15
APP2022-0000002self-employed786.1trueTX2022-07-142022-07-15T14:22:51120000180home12000044malemarried277005approvedstable_income3.8881.65
APP2023-0000003employed678.7falseNY2023-02-012023-02-02T16:45:10650036personal5400028femalesingle010011approvedmedium_risk7.1200.38
APP2020-0000004student624falseMA2020-05-292020-05-30T09:12:431500048education2100020non-binarysingle002139approvededucation_loan6.9360.27
APP2023-0000005employed690.4falseIL2023-11-132023-11-13T17:05:31580024medical4820036femalemarried160616approvedgood_history5.5256.02
APP2022-0000006self-employed842.6trueNJ2022-09-182022-09-18T13:43:13350000300home22500053malemarried307030approvedgood_credit3.31716.89
APP2024-0000007employed755.2trueWA2024-01-032024-01-04T10:22:084100072auto9500041femaledivorced198101approvedhigh_income3.9646.99
APP2022-0000008employed662.9falseOH2022-05-072022-05-07T15:12:12850036debt_consolidation5700029malesingle043215approvedgood_credit5.2255.04

What the 200 rows show

from the 200-row sample

Unemployed (applicant employment status) stands out: mean applicant_credit_score is 249.0, against 705.0 for the rest.

  • 32%is_existing_customer = true
  • 685.3median applicant_credit_score
  • 2decisions
  • 4applicant genders
  • 5applicant marital statuses
  • 10loan purposes
Mean applicant_credit_score by applicant_employment_status200 rows
05001,000701.1employ…125 rows874.1self-e…30 rows249.0unempl…25 rows462.1student18 rows594.4other2 rows
applicant_credit_score200 rows, in bands of 100
03570297106136255251105001,000applicant_credit_score →

Median 685.3, from 0.0 to 997.4.

property_state180 rows with a value · 20 left blank
  1. CA23
  2. TX17
  3. NY17
  4. WA16
  5. IL13
  6. MA12
  7. NJ12
  8. FL11
  9. UT7
  10. PA7
20 columns by typefrom the column list below
  • string 9
  • integer 3
  • float 5
  • date 1
  • datetime 1
  • boolean 1

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 9 columns
application_idstringUnique identifier for each loan application recorduniqueAPP2021-0000001
loan_purposestringPurpose of the loan as stated by the applicant (e.g., home, auto, education, personal)10 purposesauto
applicant_employment_statusstringEmployment status of the applicant (e.g., employed, self-employed, unemployed, retired, student)6 valuesemployed
applicant_genderstringGender of the applicant (if collected, otherwise 'unknown')male · female · non-binary · unknown · optionalfemale
applicant_marital_statusstringMarital status of the applicant (e.g., single, married, divorced, widowed, unknown)single · married · divorced · widowed · unknown · optionalmarried
property_statestringState or region where the property (if applicable) is locatedoptionalCA
property_postal_codestringPostal code of the property or applicant's addressoptional90017
decisionstringAutomated decision outcome for the loan applicationapproved · rejectedapproved
decision_reason_codestringCode indicating the primary reason for approval or rejection (e.g., 'low_income', 'high_debt', 'good_credit', etc.)optionalgood_credit
Numbers 8 columns
loan_amountfloatTotal amount requested by the applicant for the loan0.01 or more25000
loan_term_monthsintegerDuration of the loan in months1 or more60
applicant_incomefloatAnnual income of the applicant in the local currency0 or more66000
applicant_ageintegerAge of the applicant at the time of application18 to 10032
applicant_dependentsintegerNumber of dependents the applicant supports0 or more · optional1
applicant_credit_scorefloatCredit score of the applicant at the time of application0 to 1,000 · optional702.5
interest_ratefloatInterest rate offered to the applicant (if approved), as a percentage0 to 100 · optional4.3
monthly_paymentfloatMonthly payment amount for the loan (if approved)0 or more · optional462.15
Dates and times 2 columns
application_datedateDate when the loan application was submitted2021-03-22
decision_datetimedatetimeTimestamp when the automated decision was made on the application2021-03-23T11:35:07
True or false 1 column
is_existing_customerbooleanIndicates if the applicant is an existing customer of the institutionfalse

Use it for

  • is existing cu…32%63 of 200 rowsmean applicant credit…701.1empl…874.1self…249.0unem…462.1stud…

    A finance dashboard

    The is_existing_customer rate, applicant_credit_score by applicant_employment_status and a breakdown of property_state. Excel, Power BI or Tableau.

  • Why do the 25 unemployed rows have a mean applicant_credit_score of 249.0?

    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 applications with application_date, decision_datetime and loan_amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 20 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 rows20 columns
Yours1,000 rows20 columnsproperty_state: UK only

blueprint · loan-application-decision-records

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 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
  • Include applicant credit score and income
  • Approval status must align with bank policy
  • Randomize application timestamps
  • Remove identifiable personal data
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
loan-application-decision-records

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