Consumer Lending Application Workflow

This dataset provides detailed, step-by-step workflow traces for consumer lending applications, including applicant demographics, application status changes, review and underwriting timelines, and funding outcomes. It enables comprehensive analysis of lending process efficiency, bottlenecks, and applicant characteristics for process optimization and risk assessment.

  • last updated 21 Jul 2025
  • by GoMask
The brief that made it

Process optimization and bottleneck analysis in lending workflows

Sample rows

preview · 8 of 200 rows · all 27 columns
application_idstringapplicant_address_countrystringloan_amount_requestedfloatreviewer_idstringcustomer_idstringapplication_created_datetimedatetimecurrent_statusstringstatus_updated_datetimedatetimeloan_amount_approvedfloatloan_purposestringreview_start_datetimedatetimereview_end_datetimedatetimeunderwriter_idstringunderwriting_start_datetimedatetimeunderwriting_end_datetimedatetimedecision_datetimedatetimedecision_reasonstringfunded_datetimedatetimefunded_amountfloatapplication_channelstringapplicant_incomefloatapplicant_employment_statusstringapplicant_credit_scoreintegerapplicant_address_streetstringapplicant_address_citystringapplicant_address_statestringapplicant_address_postal_codestring
APP000001USA18500blankCUST0012023-03-05T10:23:15Zsubmitted2023-03-05T10:23:15Zblankauto_purchaseblankblankblankblankblankblankblankblankblankonline67000employed743832 Main StreetBostonMA02114
APP000002USA1200blankCUST0022023-03-12T15:12:47Zsubmitted2023-03-12T15:12:47Zblankemergency_expenseblankblankblankblankblankblankblankblankblankmobile_app7800student52454 Elm LaneMadisonWI53706
APP000003GBR45000blankCUST0032023-03-20T09:44:11Zsubmitted2023-03-20T09:44:11Zblankhome_improvementblankblankblankblankblankblankblankblankblankbranch120000self_employed69329 Oxford RdManchesterENGM13 9PL
APP000004IRL1500blankCUST0042023-03-22T13:08:32Zsubmitted2023-03-22T13:08:32Zblankutility_paymentblankblankblankblankblankblankblankblankblankphone11200unemployed41716 Willow StreetDublinLDD02 X285
APP000005CAN350000blankCUST0052023-03-25T14:25:19Zsubmitted2023-03-25T14:25:19Zblankreal_estate_purchaseblankblankblankblankblankblankblankblankblankagent310000self_employed8011028 Bayview AveTorontoONM4G 3A9
APP000006CAN11000REVIEWER01CUST0062023-03-10T11:35:51Zin_review2023-03-11T09:20:00Zblankmedical_expense2023-03-10T13:00:00Zblankblankblankblankblankblankblankblankbranch42000retired75590 King StHamiltonONL8P 1A1
APP000007USA21000REVIEWER02CUST0072023-03-14T17:02:18Zin_review2023-03-15T08:31:22Zblankdebt_consolidation2023-03-14T18:00:00Zblankblankblankblankblankblankblankblankonline55000employed7242130 Oak DriveDenverCO80209
APP000008GBR8500REVIEWER03CUST0082023-03-30T12:53:41Zin_review2023-04-01T09:43:19Zblankauto_repair2023-03-30T15:00:00Zblankblankblankblankblankblankblankblankmobile_app31500employed66112 Park LaneBirminghamENGB15 2TT

What the 200 rows show

from the 200-row sample

AUS (applicant address country) stands out: mean loan_amount_requested is 800,608, against 66,533 for the rest.

  • 14,750median loan_amount_requested
  • 5application channels
  • 5applicant employment statuses
  • 7current statuses
  • 14loan purposes
  • 21underwriters
Mean loan_amount_requested by applicant_address_country200 rows
0500k1M32,111USA69 rows123.2kCAN44 rows83,508GBR42 rows35,800IRL27 rows800.6kAUS18 rows
loan_amount_requested200 rows, in bands of 200k
09018017583113322201M2Mloan_amount_requested →

Median 14,750, from 440.0 to 2,000,000.

reviewer_id156 rows with a value · 44 left blank
  1. REVIEWER0214
  2. REVIEWER0514
  3. REVIEWER0112
  4. REVIEWER0312
  5. REVIEWER0412
  6. REVIEWER138
  7. REVIEWER148
  8. REVIEWER117
  9. REVIEWER127
  10. REVIEWER157
27 columns by typefrom the column list below
  • string 14
  • integer 1
  • float 4
  • datetime 8

Columns

27 columns in three groups
blueprint · 27 columns
columntypedescriptionexample
Text 14 columns
application_idstringUnique identifier for each lending applicationuniqueAPP000001
customer_idstringUnique identifier for the customer applying for the loanCUST001
current_statusstringCurrent status of the application in the workflow7 valuessubmitted
loan_purposestringPurpose of the loan as provided by the applicantauto_purchase
reviewer_idstringIdentifier for the staff member who reviewed the applicationoptionalREVIEWER01
underwriter_idstringIdentifier for the underwriter assigned to the applicationoptionalUNDER01
decision_reasonstringReason for the final decision (if rejected or withdrawn)optionalLow credit score
application_channelstringChannel through which the application was submitted (e.g., online, branch, phone)online · branch · phone · mobile_app · agentonline
applicant_employment_statusstringEmployment status of the applicant at the time of applicationemployed · self_employed · unemployed · retired · studentemployed
applicant_address_streetstringApplicant's street address832 Main Street
applicant_address_citystringApplicant's cityBoston
applicant_address_statestringApplicant's state or provinceMA
applicant_address_postal_codestringApplicant's postal or ZIP code02114
applicant_address_countrystringApplicant's country5 countriesUSA
Numbers 5 columns
loan_amount_requestedfloatAmount of money requested by the applicant0 or more18500
loan_amount_approvedfloatFinal approved loan amount (if approved)0 or more · optional35000
funded_amountfloatAmount actually funded to the customer (if applicable)0 or more · optional20000
applicant_incomefloatSelf-reported annual income of the applicant0 or more67000
applicant_credit_scoreintegerCredit score of the applicant at the time of application300 to 900743
Dates and times 8 columns
application_created_datetimedatetimeTimestamp when the application was created2023-03-05T10:23:15Z
status_updated_datetimedatetimeTimestamp when the current status was last updated2023-03-05T10:23:15Z
review_start_datetimedatetimeTimestamp when the review process startedoptional2023-03-10T13:00:00Z
review_end_datetimedatetimeTimestamp when the review process endedoptional2023-03-12T15:00:00Z
underwriting_start_datetimedatetimeTimestamp when underwriting beganoptional2023-03-13T08:00:00Z
underwriting_end_datetimedatetimeTimestamp when underwriting was completedoptional2023-03-25T11:00:00Z
decision_datetimedatetimeTimestamp when the final decision (approved/rejected/withdrawn) was madeoptional2023-03-25T13:00:00Z
funded_datetimedatetimeTimestamp when the loan was funded (if applicable)optional2023-03-25T17:00:00Z

Use it for

  • median loan am…14,750200 rowsmean loan amount requ…32.1kUSA123.2kCAN83.5kGBR35.8kIRL

    A finance dashboard

    Loan_amount_requested by applicant_address_country and a breakdown of reviewer_id. Excel, Power BI or Tableau.

  • Why do the 18 AUS rows have a mean loan_amount_requested of 800,608?

    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, application_created_datetime and current_status to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 27 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows27 columns
Yours1,000 rows27 columnsapplicant_address_street: UK only

blueprint · consumer-lending-application-workflow

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 27 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
  • Each application tracks workflow steps with timestamps
  • Approval, rejection, and manual review steps logged
  • Link to applicant demographics and loan type
  • Escalation and exception events flagged
  • Funding and disbursement steps included
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
consumer-lending-application-workflow

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