Credit Scoring Models Data

This dataset provides a comprehensive view of credit risk scoring for lending decisions, combining FICO and alternative scores, bureau data, model versions, and detailed score change history. It enables robust analysis of applicant risk profiles, model performance, and regulatory compliance in credit decisioning.

  • opened 10 times
  • last updated 1 Nov 2025
  • by GoMask
The brief that made it

Credit risk modeling and applicant profiling

Sample rows

preview · 8 of 800 rows · all 21 columns
application_idstringemployment_statusstringincomefloatscore_change_typestringcustomer_idstringfico_scoreintegeralternative_scorefloatscoring_model_versionstringapplication_datedatedecision_datedatelending_decisionstringbureau_namestringbureau_scoreintegerscore_change_datedatescore_before_changeintegerscore_after_changeintegeraddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringaddress_countrystring
APP0001retired220000blankCUST1001785850.5v4.1.02024-03-022024-03-03approvedEquifax788blankblankblank501 Beverly DrBeverly HillsCA90210US
APP0002employed88000updateCUST1002672743.1v3.9.22024-03-042024-03-07approvedTransUnion6692024-03-05658672221 Michigan AveChicagoIL60601US
APP0003student0disputeCUST100339542.9v1.3.12024-03-052024-03-08declinedExperian3892024-03-074003951412 College AveAnn ArborMI48104US
APP0004employed127000blankCUST1004730795.3v4.2.02024-03-062024-03-07approvedExperian732blankblankblank88 Queen StNew YorkNY10016US
APP0005self-employed45000blankCUST1002649blankv3.7.02024-03-072024-03-08pendingTransUnion650blankblankblank909 Market StSan FranciscoCA94103US
APP0006retired350000blankCUST1005810980.4v4.1.02024-03-082024-03-09approvedEquifax812blankblankblank1628 Maplewood RdDallasTX75201US
APP0007unemployed26000correctionCUST1006580325.6v2.2.32024-03-082024-03-09declinedTransUnion5762024-03-0960058048 Trenton PlTrentonNJ08608US
APP0008employed64000blankCUST1007695715.7v3.6.02024-03-102024-03-16approvedExperian701blankblankblank1432 BroadwayDenverCO80203US

What the 800 rows show

from the 800-row sample

Retired (employment status) stands out: mean income is 494,908, against 73,736 for the rest.

  • 96,785median income
  • 3lending decisions
  • 3bureau names
  • 17address states
  • 28address cities
  • 159address postal codes
Mean income by employment_status733 rows
0400k800k110.8kemploy…353 rows84,654self-e…65 rows14,290unempl…126 rows494.9kretired103 rows476.7student86 rows
income733 rows, in bands of 200k
0320640632482491730600k1.2Mincome →

Median 96,785, from 0.0 to 1,200,000.

score_change_type284 rows with a value · 516 left blank
  1. update102
  2. dispute96
  3. correction74
  4. initial12
21 columns by typefrom the column list below
  • string 12
  • integer 4
  • float 2
  • date 3

Columns

21 columns in three groups
blueprint · 21 columns
columntypedescriptionexample
Text 12 columns
application_idstringUnique identifier for each credit applicationuniqueAPP0001
customer_idstringUnique identifier for each customerCUST1001
scoring_model_versionstringVersion identifier of the scoring model usedv4.1.0
lending_decisionstringFinal lending decision for the applicationapproved · declined · pendingapproved
bureau_namestringName of the credit bureau providing the score3 namesEquifax
score_change_typestringType of score change event (e.g., update, correction, dispute)update · correction · dispute · initial · optionalupdate
employment_statusstringApplicant's employment status at time of applicationemployed · self-employed · unemployed · retired · student · optionalretired
address_streetstringApplicant's street addressoptional501 Beverly Dr
address_citystringApplicant's city of residenceoptionalBeverly Hills
address_statestringApplicant's state of residenceoptionalCA
address_postal_codestringApplicant's postal codeoptional90210
address_countrystringApplicant's country of residenceoptionalUS
Numbers 6 columns
fico_scoreintegerFICO credit score at the time of application300 to 850785
alternative_scorefloatAlternative credit score based on non-traditional data sources0 to 1,000 · optional850.5
bureau_scoreintegerCredit score reported by the bureau300 to 850 · optional788
score_before_changeintegerCredit score before the change event300 to 850 · optional658
score_after_changeintegerCredit score after the change event300 to 850 · optional672
incomefloatApplicant's reported annual income0 or more · optional220000
Dates and times 3 columns
application_datedateDate when the credit application was submitted2024-03-02
decision_datedateDate when the lending decision was madeoptional2024-03-03
score_change_datedateDate when the score change occurredoptional2024-03-05

Use it for

  • median income96,785733 rowsmean income by employ…110.8kempl…84.7kself…14.3kunem…494.9kreti…

    A finance dashboard

    Income by employment_status and a breakdown of score_change_type. Excel, Power BI or Tableau.

  • Why do the 103 retired rows have a mean income of 494,908?

    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 customer_id, fico_score and alternative_score to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 21 columns, your size and your rules. See 20 rows before you pay.

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This dataset800 rows21 columns
Yours1,000 rows21 columnsaddress_street: UK only

blueprint · credit-scoring-models-data-2

Behind this dataset

Same schema. As many rows as you need.

These 800 rows came out of a blueprint — 21 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
  • FICO score range: 300-850
  • Score from 3 bureaus: Equifax, Experian, TransUnion
  • Middle score used for decisions
  • Score factors: payment history, utilization, length, new credit, mix
  • Hard inquiry recorded with permission
  • Score refresh frequency based on product
  • Alternative data: rent, utilities, if thin file
  • Model version tracked for consistency
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-scoring-models-data-2

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