Credit Scoring Models Data

This dataset provides a comprehensive view of credit risk scoring for lending decisions, including FICO and alternative scores, scoring model versions, bureau sources, and detailed tracking of score changes and lending outcomes. It enables robust analysis of risk assessment models, applicant profiles, and credit decision processes for financial institutions and fintechs.

  • opened 7 times
  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Credit risk modeling and validation

Sample rows

preview · 8 of 800 rows · all 20 columns
application_idstringemployment_statusstringloan_amount_requestedfloatscore_change_reasonstringcustomer_idstringfico_scoreintegeralternative_scorefloatscoring_model_versionstringbureau_namestringscore_datedatescore_changeintegerdecisionstringdecision_datedateloan_amount_approvedfloatincomefloataddress_streetstringaddress_citystringaddress_statestringaddress_postal_codestringaddress_countrystring
APP00001employed230000on-time paymentsCUST00175582.5v1.2.0Experian2022-02-1418approved2022-02-15227000198000812 Maple AveNew YorkNY10021USA
APP00002student8000missed utility billCUST00248822.1v2.0.1Equifax2022-04-07-43review2022-04-0801300023 College Dr.MadisonWI53706USA
APP00003self-employed48000regular usageCUST00364164.7v1.3.0TransUnion2022-06-117approved2022-06-134750071000191 Market StAustinTX73301USA
APP00004unemployed2200bankruptcyCUST004381blankv2.1.0-betaOther2022-08-18-112declined2022-08-190042 Oak LaneToledoNA43604USA
APP00005employed420000debt paid offCUST00580294.8v1.2.1TransUnion2022-10-2217approved2022-10-234200003500001917 Pine StSan FranciscoCA94109USA
APP00006self-employed57000on-time paymentsCUST00672579.2v1.2.0Experian2022-12-056approved2022-12-075620091000902 Main StDenverCO80203USA
APP00007other6000missed paymentCUST00753338.2v2.0.2Equifax2022-01-12-29declined2022-01-13010000619 Elm StSpringfieldIL62704USA
APP00008retired650000debt paid offCUST00884699.3v1.3.2Experian2022-03-2425approved2022-03-25650000950000333 Beverly DrBeverly HillsCA90210USA

What the 800 rows show

from the 800-row sample

Retired (employment status) stands out: mean loan_amount_requested is 319,025, against 65,830 for the rest.

  • 47,000median loan_amount_requested
  • 3decisions
  • 4bureau names
  • 49address states
  • 239address cities
  • 671median fico_score
Mean loan_amount_requested by employment_status800 rows
0200k400k137.3kemplo…227 ro…81,289self-…157 ro…4,430unemp…147 ro…8,749stude…93 rows319kretir…102 ro…7,354other74 rows
loan_amount_requested800 rows, in bands of 100k
02555105051481061749110400k700kloan_amount_requested →

Median 47,000, from 1,000 to 670,000.

score_change_reason800 rows · top 10 of 34 values
  1. on-time payments208
  2. missed payment149
  3. debt paid off129
  4. regular usage99
  5. foreclosure34
  6. bankruptcy33
  7. long credit history23
  8. improved payment history21
  9. missed utility bill20
  10. bankruptcy filing13
20 columns by typefrom the column list below
  • string 12
  • integer 2
  • float 4
  • date 2

Columns

20 columns in three groups
blueprint · 20 columns
columntypedescriptionexample
Text 12 columns
application_idstringUnique identifier for each credit applicationuniqueAPP00001
customer_idstringUnique identifier for the customer applying for creditCUST001
scoring_model_versionstringVersion identifier for the credit scoring model usedv1.2.0
bureau_namestringName of the credit bureau providing the scoreExperian · Equifax · TransUnion · OtherExperian
score_change_reasonstringReason for the change in credit score, if applicableoptionalon-time payments
decisionstringLending decision based on the score (e.g., approved, declined, review)approved · declined · reviewapproved
employment_statusstringApplicant's employment status6 values · optionalemployed
address_streetstringApplicant's street addressoptional812 Maple Ave
address_citystringApplicant's cityoptionalNew York
address_statestringApplicant's state or provinceoptionalNY
address_postal_codestringApplicant's postal codeoptional10021
address_countrystringApplicant's countryoptionalUSA
Numbers 6 columns
fico_scoreintegerFICO credit score of the applicant at the time of application300 to 850755
alternative_scorefloatAlternative credit risk score (e.g., based on non-traditional data sources)0 to 100 · optional82.5
score_changeintegerChange in credit score compared to previous score (positive or negative)optional18
loan_amount_requestedfloatAmount of loan requested by the applicant0 or more230000
loan_amount_approvedfloatAmount of loan approved (may be less than requested)0 or more · optional227000
incomefloatApplicant's annual income at time of application0 or more · optional198000
Dates and times 2 columns
score_datedateDate when the credit score was obtained2022-02-14
decision_datedateDate when the lending decision was made2022-02-15

Use it for

  • median loan am…47,000800 rowsmean loan amount requ…137.3kempl…81.3kself…4.4kunem…8.7kstud…

    A finance dashboard

    Loan_amount_requested by employment_status and a breakdown of score_change_reason. Excel, Power BI or Tableau.

  • Why do the 102 retired rows have a mean loan_amount_requested of 319,025?

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

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1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset800 rows20 columns
Yours1,000 rows20 columnsaddress_street: UK only

blueprint · credit-scoring-models-data

Behind this dataset

Same schema. As many rows as you need.

These 800 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
  • 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

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