SME Lending Decision Dataset

This dataset provides a comprehensive view of SME loan applications, featuring detailed business profiles, financial metrics, application specifics, and lending decisions. It enables robust credit risk modeling, operational analytics, and benchmarking for financial institutions serving small and medium enterprises.

  • opened 13 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Credit risk modeling and loan approval prediction

Sample rows

preview · 8 of 200 rows · all 32 columns
application_idstringcollateral_typestringannual_revenuefloatbusiness_address_statestringbusiness_idstringbusiness_namestringindustry_sectorstringbusiness_typestringbusiness_established_datedatebusiness_address_streetstringbusiness_address_citystringbusiness_address_postal_codestringbusiness_address_countrystringnumber_of_employeesintegernet_profitfloatyears_in_businessfloatcredit_scoreintegerloan_amount_requestedfloatloan_purposestringcollateral_valuefloatapplication_datedatedecision_datedatedecision_statusstringapproved_loan_amountfloatinterest_ratefloatloan_term_monthsintegerrepayment_frequencystringprevious_loans_countintegerprevious_loans_defaultedintegercontact_person_namestringcontact_person_emailstringcontact_person_phonestring
APP00001Property17300000.5OHBIZ1001Greenfield Manufacturing CoManufacturingCorporation1998-04-121232 Oakview BlvdCleveland44101USA213162500026885850000Expansion12000002024-02-062024-02-20Approved8500002.996Quarterly91Jason Shepherdjason.shepherd@greenfieldco.com+1-216-555-7821
APP00002Inventory980000TXBIZ1002Sunrise Retail GroupRetailLLC2010-07-19832 Market StreetDallas75201USA238900013.673565000Inventory680002024-03-132024-03-21Approved650005.118Monthly20Nina Alvareznina.alvarez@sunriserg.com+1-214-555-3245
APP00003None14500000CABIZ1003TechNova SolutionsIT ServicesCorporation2007-11-28455 Innovation ParkSan Jose95131USA115137500016.59021200000Expansion02024-01-152024-01-28Approved12000003.1120Quarterly70Stephanie Choistephanie.choi@technova.com+1-408-555-9881
APP00004Equipment42000IABIZ1004FreshRoots FarmAgricultureLLC2016-04-0385 County Road 19Des Moines50301USA738008.165512000Equipment Purchase150002024-05-012024-05-15Approved120007.912Monthly00Derek Fultonderek.fulton@freshrootsfarm.com+1-515-555-6234
APP00005None37000ILBIZ1005Urban Eats DinerFood ServiceSole Proprietorship2022-09-15111 Maple AvenueChicago60611USA412001.64689000Working Capital02024-04-13blankPendingblankblankblankblank00Maya Sandersmaya.sanders@urbaneats.com+1-312-555-7705
APP00006Property10500000GABIZ1006Express Transit LogisticsTransportationCorporation2002-02-09299 Industrial AveAtlanta30318USA13592500022.2807650000Equipment Purchase7000002024-02-222024-03-07Approved6500004.484Quarterly81Curtis Danielscurtis.daniels@expresstransit.com+1-404-555-4462
APP00007None32000CABIZ1007Creative Spark StudioCreative ArtsLLC2021-05-259029 Pinewood LaneLos Angeles90026USA39002.94728000Working Capital02024-05-02blankPendingblankblankblankblank00Carlos Benitezcarlos.benitez@creativespark.com+1-323-555-3298
APP00008None98000WABIZ1008Blue Horizon ConsultingConsultingPartnership2019-02-05455 Market PlaceSeattle98104USA661005.269019000Expansion02024-04-012024-04-12Approved190008.224Monthly10Kendra Leekendra.lee@bluehorizon.com+1-206-555-7611

What the 200 rows show

from the 200-row sample

Property (collateral type) stands out: mean annual_revenue is 14,091,000, against 873,058 for the rest.

  • 275,000median annual_revenue
  • 2business address countries
  • 2repayment frequencies
  • 3decision statuses
  • 5business types
  • 5loan purposes
Mean annual_revenue by collateral_type200 rows
010M20M358.7kNone99 rows14.1MProper…30 rows2.2MEquipm…43 rows719kInvent…27 rows680kReceiv…1 rows
annual_revenue200 rows, in bands of 2.5M
085170164135675261012.5M25Mannual_revenue →

Median 275,000, from 5,200 to 23,800,000.

business_address_state200 rows · top 10 of 40 values
  1. CA18
  2. TX16
  3. ON14
  4. OH10
  5. FL10
  6. CO9
  7. NC9
  8. AB9
  9. WA8
  10. OR8
32 columns by typefrom the column list below
  • string 17
  • integer 5
  • float 7
  • date 3

Columns

32 columns in three groups
blueprint · 32 columns
columntypedescriptionexample
Text 17 columns
application_idstringUnique identifier for each loan applicationuniqueAPP00001
business_idstringUnique identifier for each SME businessBIZ1001
business_namestringRegistered name of the SME businessSunrise Retail Group
industry_sectorstringPrimary industry sector of the businessManufacturing
business_typestringType of business entity (e.g., LLC, Partnership, Sole Proprietorship)5 valuesCorporation
business_address_streetstringStreet address of the business1232 Oakview Blvd
business_address_citystringCity where the business is locatedCleveland
business_address_statestringState or province where the business is locatedOH
business_address_postal_codestringPostal code of the business address44101
business_address_countrystringCountry where the business is located2 countriesUSA
loan_purposestringPurpose for which the loan is requested5 valuesExpansion
collateral_typestringType of collateral offered (if any)6 values · optionalProperty
decision_statusstringFinal decision on the loan applicationApproved · Rejected · PendingApproved
repayment_frequencystringFrequency of loan repaymentsMonthly · Quarterly · Annually · Other · optionalQuarterly
contact_person_namestringName of the primary contact person for the applicationJason Shepherd
contact_person_emailstringEmail address of the contact personliam.fisher@truetaste.ca
contact_person_phonestringPhone number of the contact person+1-216-555-7821
Numbers 12 columns
number_of_employeesintegerTotal number of employees in the business1 or more213
annual_revenuefloatAnnual revenue of the business in local currency0 or more17300000.5
net_profitfloatNet profit of the business in the last fiscal year0 or more · optional1625000
years_in_businessfloatNumber of years the business has been operating0 or more26
credit_scoreintegerCredit score of the business or owner0 to 1,000 · optional885
loan_amount_requestedfloatAmount of loan requested by the business0 or more850000
collateral_valuefloatEstimated value of the collateral offered0 or more · optional1200000
approved_loan_amountfloatAmount of loan approved (if applicable)0 or more · optional850000
interest_ratefloatInterest rate offered for the approved loan0 to 100 · optional2.9
loan_term_monthsintegerLoan term in months1 or more · optional96
previous_loans_countintegerNumber of previous loans taken by the business0 or more · optional9
previous_loans_defaultedintegerNumber of previous loans defaulted by the business0 or more · optional1
Dates and times 3 columns
business_established_datedateDate when the business was established1998-04-12
application_datedateDate when the loan application was submitted2024-02-06
decision_datedateDate when the lending decision was madeoptional2024-02-20

15 SQL practice questions for the SME Lending Decision Dataset

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

  1. 01BeginnerFreeSELECT · ORDER BY · LIMIT

    Ten largest loan requests

    Return application_id, business_name and loan_amount_requested for the 10 applications with the highest loan_amount_requested. Order the rows by loan_amount_requested from highest to lowest, then by application_id ascending.

    Tables: sme_lending_decision_dataset · your result: 3 columns, 10 rows
  2. 02BeginnerFreeDISTINCT · ORDER BY

    Distinct industry sectors

    Return each distinct industry_sector found in the data as a single column named industry_sector, sorted alphabetically.

    Tables: sme_lending_decision_dataset · your result: 1 columns, 15 rows
  3. 03BeginnerFreeWHERE · AND · ORDER BY

    Rejected Canadian applications

    Return application_id, business_name, business_address_city and credit_score for applications where business_address_country is 'CAN' and decision_status is 'Rejected'. Order by credit_score ascending, then by application_id ascending.

    Tables: sme_lending_decision_dataset · your result: 4 columns, 6 rows
  1. 04Applications per decision statusBeginner
  2. 05Average credit score by loan purposePower BI
  3. 06Sectors with many applicationsIntermediate
  4. 07Credit score bandsIntermediate
  5. 08Requests above the overall averageIntermediate
  6. 09Monthly application volumeIntermediate
  7. 10Approval rate by business typePower BI
  8. 11Top two applications per sectorAdvanced
  9. 12Running total of requestsAdvanced
  10. 13Month-over-month change in applicationsAdvanced
  11. 14Sector share of approved amountAdvanced
  12. 15Sector credit score anomaliesPower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

  • median annual …275,000200 rowsmean annual revenue b…358.7kNone14.1MProp…2.2MEqui…719kInve…

    A finance dashboard

    Annual_revenue by collateral_type and a breakdown of business_address_state. Excel, Power BI or Tableau.

  • Why do the 30 Property rows have a mean annual_revenue of 14,091,000?

    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 business_id, business_name and industry_sector to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 32 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 rows32 columns
Yours1,000 rows32 columnsbusiness_address_street: UK only

blueprint · sme-lending-decision-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 32 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
  • Business sector identified
  • Financial ratios provided per SME
  • Loan approval status included
  • SME age in years tracked
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
sme-lending-decision-dataset

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