Credit Application Approval Outcomes

This dataset provides detailed records of credit application outcomes, including applicant demographics, financial profiles, application details, and decision rationales. It enables comprehensive analysis of approval and rejection trends, supports risk model optimization, and helps financial institutions refine their credit decision processes.

  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Monitor and analyze credit approval and rejection rates by applicant profile

Sample rows

preview · 8 of 95 rows · all 15 columns
application_idstringdecisionstringapplicant_credit_scoreintegerdecision_reasonstringapplicant_idstringapplication_datedatedecision_datedatecredit_amount_requestedfloatcredit_amount_approvedfloatapplicant_incomefloatapplicant_employment_statusstringapplicant_ageintegerapplicant_postal_codestringapplication_channelstringreviewed_bystring
APP10001approved735meets criteriaAPPL20012024-04-182024-04-19150001400065000employed3490210onlinesys_auto
APP10002rejected422low credit scoreAPPL20022024-04-102024-04-139000022000unemployed4710016branchbr_jmartin
APP10003approved802meets criteriaAPPL20032024-03-222024-03-232800025000120000self-employed4130303phonesys_auto
APP10004pending695blankAPPL20042024-04-012024-04-035000018500student2211211onlineblank
APP10005rejected568insufficient incomeAPPL20052024-03-302024-04-0216000030000retired6960657branchbr_spatel
APP10006approved686meets criteriaAPPL20022024-04-222024-04-2311000900054000employed2994105onlinesys_auto
APP10007approved701meets criteriaAPPL20062024-03-182024-03-183000300034000employed2335203phonesys_auto
APP10008rejected560low credit scoreAPPL20032024-03-242024-03-2517000050000self-employed3894110branchbr_kjones

What the 95 rows show

from the 95-row sample

Rejected decision stands out: mean applicant_credit_score is 431.6, against 740.6 for the rest.

  • 705median applicant_credit_score
  • 4application channels
  • 5applicant employment statuses
  • 8,900median credit_amount_requested
  • 3,000median credit_amount_approved
  • 39,000median applicant_income
Mean applicant_credit_score by decision95 rows
0400800749.3approved50 rows431.6rejected31 rows709.5pending14 rows
applicant_credit_score95 rows, in bands of 100
02550148616438300600900applicant_credit_score →

Median 705, from 310 to 862.

decision_reason81 rows with a value · 14 left blank
  1. meets criteria41
  2. low credit score21
  3. insufficient income8
  4. excellent credit3
  5. strong income2
  6. no employment2
  7. stable employment1
  8. high credit score1
  9. good credit history1
  10. stable income1
15 columns by typefrom the column list below
  • string 8
  • integer 2
  • float 3
  • date 2

Columns

15 columns in three groups
blueprint · 15 columns
columntypedescriptionexample
Text 8 columns
application_idstringUnique identifier for each credit applicationuniqueAPP10001
applicant_idstringUnique identifier for the applicantAPPL2001
decisionstringOutcome of the credit applicationapproved · rejected · pendingapproved
applicant_employment_statusstringEmployment status of the applicantemployed · self-employed · unemployed · student · retiredemployed
applicant_postal_codestringPostal code of the applicant's residenceoptional90210
application_channelstringChannel through which the application was submittedonline · branch · phone · agentonline
decision_reasonstringPrimary reason for approval or rejection (e.g., 'low credit score', 'insufficient income', 'meets criteria')10 reasons · optionalmeets criteria
reviewed_bystringIdentifier for the staff or system that made the decisionoptionalsys_auto
Numbers 5 columns
credit_amount_requestedfloatAmount of credit requested by the applicant0 or more15000
credit_amount_approvedfloatAmount of credit approved (0 if rejected)0 or more14000
applicant_incomefloatAnnual income of the applicant0 or more65000
applicant_ageintegerAge of the applicant at the time of application18 to 10034
applicant_credit_scoreintegerCredit score of the applicant at the time of application300 to 900735
Dates and times 2 columns
application_datedateDate when the credit application was submitted2024-04-18
decision_datedateDate when the application decision was made2024-04-19

Use it for

  • median applica…70595 rowsmean applicant credit…749.3approv…431.6reject…709.5pending

    A finance dashboard

    Applicant_credit_score by decision and a breakdown of decision_reason. Excel, Power BI or Tableau.

  • Why do the 31 rejected rows have a mean applicant_credit_score of 431.6?

    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 applicant_id, application_date and decision_date to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 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 dataset95 rows15 columns
Yours1,000 rows15 columnsapplicant_postal_code: UK only

blueprint · credit-application-approval-outcomes

Behind this dataset

Same schema. As many rows as you need.

These 95 rows came out of a blueprint — 15 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 credit score, income, and loan amount
  • Log approval or rejection status
  • Record application channel (online, branch)
  • Flag repeat applicants
  • Capture time to decision
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
credit-application-approval-outcomes

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