Customer Loan Repayment Histories
This dataset provides granular, event-level records of customer loan repayments, including payment amounts, statuses, timing, and outstanding balances. It enables robust risk modeling, customer segmentation, and repayment behavior analysis for lenders and financial institutions. The inclusion of customer and loan attributes supports advanced analytics and credit scoring applications.
Sample rows
preview · 8 of 200 rows · all 20 columns| customer_idstring | payment_methodstring | interest_paidfloat | payment_statusstring | loan_idstring | payment_event_idstring | payment_datedate | payment_due_datedate | payment_amountfloat | days_past_dueinteger | remaining_principalfloat | principal_paidfloat | late_fee_paidfloat | loan_start_datedate | loan_end_datedate | loan_amountfloat | loan_statusstring | customer_birth_datedate | customer_genderstring | customer_postal_codestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUST10001 | auto_debit | 22.5 | paid | LN20001 | PEV30001 | 2023-01-10 | 2023-01-10 | 322.5 | 0 | 8232.5 | 300 | 0 | 2022-12-15 | 2024-12-15 | 10000 | active | 1995-09-15 | female | 10001 |
| CUST10002 | bank_transfer | 50 | late | LN20002 | PEV30002 | 2023-01-15 | 2023-01-10 | 350 | 5 | 9650 | 290 | 10 | 2022-12-01 | 2024-11-30 | 12000 | active | 1968-03-22 | male | 75201 |
| CUST10003 | cash | 0 | missed | LN20003 | PEV30003 | 2023-02-01 | 2023-01-28 | 0 | 4 | 590 | 0 | 0 | 2022-01-01 | 2023-12-31 | 1500 | active | 2000-11-07 | other | 94105 |
| CUST10004 | credit_card | 5 | partial | LN20004 | PEV30004 | 2022-11-12 | 2022-11-12 | 75 | 0 | 425 | 70 | 0 | 2022-10-01 | 2023-09-30 | 1000 | active | 2002-02-19 | female | 60616 |
| CUST10005 | check | 62.5 | late | LN20005 | PEV30005 | 2023-03-05 | 2023-03-01 | 812.5 | 4 | 29650 | 750 | 0 | 2023-01-15 | 2025-01-14 | 30000 | active | 1955-07-23 | male | 06830 |
| CUST10006 | cash | 0 | missed | LN20006 | PEV30006 | 2022-07-15 | 2022-07-15 | 0 | 2 | 2000 | 0 | 0 | 2022-06-01 | 2023-06-01 | 2000 | active | 1998-04-14 | unknown | 19104 |
| CUST10007 | bank_transfer | 25 | paid | LN20007 | PEV30007 | 2023-05-18 | 2023-05-18 | 525 | 0 | 9475 | 500 | 0 | 2023-04-01 | 2024-04-01 | 10000 | active | 1988-12-01 | male | 02139 |
| CUST10008 | check | 200 | paid | LN20008 | PEV30008 | 2023-06-10 | 2023-06-10 | 2000 | 0 | 80000 | 1800 | 0 | 2023-01-01 | 2028-01-01 | 100000 | active | 1970-08-08 | female | 90210 |
| CUST10009 | auto_debit | 20 | late | LN20009 | PEV30009 | 2023-02-14 | 2023-02-10 | 400 | 4 | 7600 | 370 | 10 | 2022-02-01 | 2025-02-01 | 10000 | active | 1997-05-20 | male | 60616 |
| CUST10010 | credit_card | 5 | partial | LN20010 | PEV30010 | 2023-05-01 | 2023-05-01 | 75 | 0 | 625 | 70 | 0 | 2022-09-01 | 2023-09-01 | 1000 | active | 2001-07-14 | other | 10001 |
| CUST10011 | other | 0 | missed | LN20011 | PEV30011 | 2023-04-15 | 2023-04-10 | 0 | 5 | 10000 | 0 | 0 | 2022-04-01 | 2024-04-01 | 10000 | active | 1990-03-08 | unknown | 60616 |
| CUST10012 | check | 50 | paid | LN20012 | PEV30012 | 2023-06-25 | 2023-06-25 | 850 | 0 | 50000 | 800 | 0 | 2023-01-10 | 2028-01-10 | 60000 | active | 1950-02-01 | female | 06830 |
| CUST10013 | cash | 0 | missed | LN20013 | PEV30013 | 2023-03-05 | 2023-03-02 | 0 | 3 | 500 | 0 | 0 | 2022-06-01 | 2023-07-01 | 500 | active | 2005-11-15 | male | 30303 |
| CUST10014 | credit_card | 10 | partial | LN20014 | PEV30014 | 2022-12-08 | 2022-12-08 | 125 | 0 | 875 | 115 | 0 | 2022-08-01 | 2023-08-01 | 1000 | active | 1999-10-20 | female | 10001 |
| CUST10015 | auto_debit | 50 | late | LN20015 | PEV30015 | 2023-04-30 | 2023-04-25 | 600 | 5 | 9400 | 540 | 10 | 2022-05-01 | 2025-05-01 | 10000 | active | 1989-01-28 | other | 19104 |
| CUST10016 | bank_transfer | 50 | paid | LN20016 | PEV30016 | 2023-07-01 | 2023-07-01 | 550 | 0 | 8950 | 500 | 0 | 2022-07-01 | 2024-07-01 | 10000 | active | 1977-09-19 | male | 60616 |
| CUST10017 | credit_card | 10 | paid | LN20017 | PEV30017 | 2023-09-10 | 2023-09-10 | 90 | 0 | 410 | 80 | 0 | 2023-01-01 | 2024-01-01 | 500 | active | 2004-04-16 | female | 02139 |
| CUST10018 | bank_transfer | 100 | late | LN20018 | PEV30018 | 2023-06-14 | 2023-06-10 | 1200 | 4 | 11800 | 1050 | 50 | 2022-06-01 | 2024-06-01 | 13000 | active | 1982-05-12 | male | 60616 |
| CUST10019 | other | 0 | missed | LN20019 | PEV30019 | 2023-07-20 | 2023-07-15 | 0 | 5 | 70000 | 0 | 0 | 2021-01-01 | 2026-01-01 | 70000 | active | 1960-11-19 | unknown | 10001 |
| CUST10020 | auto_debit | 50 | paid | LN20020 | PEV30020 | 2023-08-01 | 2023-08-01 | 1000 | 0 | 9000 | 950 | 0 | 2023-05-01 | 2024-05-01 | 10000 | active | 1992-02-03 | male | 90210 |
What the 200 rows show
from the 200-row sampleBank_
- 17.1median interest_
paid - 2loan statuses
- 4customer genders
- 42customer postal codes
- 316.4median payment_
amount - 1median days_
past_ due
Median 17.1, from 0.0 to 1,000.
- string 8
- integer 1
- float 6
- date 5
Columns
20 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
customer_id | string | Unique identifier for the customer who took out the loan | CUST10001 |
loan_id | string | Unique identifier for the loan associated with the payment event | LN20001 |
payment_event_id | string | Unique identifier for each individual loan payment eventunique | PEV30001 |
payment_status | string | Status of the payment event (e.g., paid, missed, late, partial, pending)paid · missed · late · partial · pending | paid |
payment_method | string | Method used for payment (e.g., bank_transfer, credit_card, cash, check, auto_debit)6 values · optional | auto_debit |
loan_status | string | Current status of the loan (e.g., active, closed, defaulted, charged_off)active · closed · defaulted · charged_off · optional | active |
customer_gender | string | Gender of the customermale · female · other · unknown · optional | female |
customer_postal_code | string | Postal code of the customer's primary addressoptional | 10001 |
| Numbers 7 columns | |||
payment_amount | float | Amount paid in this payment event0 or more | 322.5 |
days_past_due | integer | Number of days the payment was late. Zero if paid on or before due date.0 or more | 0 |
remaining_principal | float | Outstanding principal balance after this payment event0 or more | 8232.5 |
interest_paid | float | Amount of interest paid in this payment event0 or more | 22.5 |
principal_paid | float | Amount of principal paid in this payment event0 or more | 300 |
late_fee_paid | float | Amount of late fee paid in this payment event, if any0 or more · optional | 0 |
loan_amount | float | Original principal amount of the loan0 or more · optional | 10000 |
| Dates and times 5 columns | |||
payment_date | date | Date when the payment was made or scheduled | 2023-01-10 |
payment_due_date | date | Scheduled due date for the payment | 2023-01-10 |
loan_start_date | date | Date when the loan was originally issuedoptional | 2022-12-15 |
loan_end_date | date | Scheduled end/maturity date of the loanoptional | 2024-12-15 |
customer_birth_date | date | Date of birth of the customer (for risk modeling and segmentation)optional | 1995-09-15 |
Use it for
A finance dashboard
Interest_
paid by payment_ method and a breakdown of payment_ status. Excel, Power BI or Tableau. Why do the 44 bank_
transfer rows have a mean interest_ paid of 167.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Customers200CUST1000122.5auto_deb…CUST1000250bank_tra…CUST100030cash
A software demo
Believable customers with loan_
id, payment_ event_ id and payment_ date to fill a screen in front of a buyer.
This dataset200 rows20 columns
Yours1,000 rows20 columnscustomer_postal_code: UK only
blueprint · customer-loan-repayment-histories
Behind this dataset
Same schema. As many rows as you need.
These 200 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
- Each payment includes customer, loan ID, date, amount.
- Missed and partial payments labeled.
- Loan type, term, and rate provided.
- Early payoff and restructuring events tracked.
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
- customer-loan-repayment-histories