Retail Banking Transaction Sequences
This dataset provides detailed, chronological transaction records for retail banking customers, including transaction types, amounts, merchant categories, account details, and geolocation data. Designed for advanced analytics, it enables modeling of spending patterns, customer segmentation, and risk assessment for financial institutions. The comprehensive structure supports both behavioral analysis and operational monitoring.
Sample rows
preview · 8 of 200 rows · all 18 columns| customer_idstring | transaction_statusstring | transaction_amountfloat | is_internationalboolean | location_statestring | transaction_idstring | transaction_datetimedatetime | currencystring | transaction_typestring | merchant_namestring | merchant_categorystring | account_idstring | account_typestring | transaction_descriptionstring | balance_after_transactionfloat | counterparty_accountstring | location_citystring | location_countrystring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CUST1023 | completed | -75.32 | false | IL | TXN10001 | 2023-12-11T16:25:42Z | USD | debit | Target | groceries | ACC104872 | checking | POS debit at Target for groceries | 1356.44 | blank | Chicago | US |
| CUST1045 | completed | 2850 | false | TX | TXN10002 | 2023-12-13T08:51:33Z | USD | deposit | Abco Corp Payroll | salary | ACC100322 | checking | Salary deposit from Abco Corp | 4422.13 | PRL67839 | Dallas | US |
| CUST1081 | completed | -150 | false | CA | TXN10003 | 2023-12-19T19:18:10Z | USD | withdrawal | ATM Wells Fargo | atm | ACC107651 | checking | ATM withdrawal at Wells Fargo | 963.27 | blank | San Jose | US |
| CUST1094 | completed | 38.45 | false | WA | TXN10004 | 2023-12-28T11:04:02Z | USD | credit | Amazon Refund | ecommerce | ACC106781 | credit_card | Refund from Amazon | -312.88 | blank | Seattle | US |
| CUST1072 | pending | -110.25 | false | CA | TXN10005 | 2024-01-02T15:33:53Z | USD | payment | Netflix | entertainment | ACC101283 | credit_card | Monthly Netflix subscription payment | -421.67 | blank | Los Angeles | US |
| CUST1057 | completed | -12.5 | false | TX | TXN10006 | 2023-12-24T21:40:16Z | USD | fee | blank | bank_fee | ACC101149 | checking | Monthly maintenance fee | 1873.92 | blank | Houston | US |
| CUST1031 | completed | 1.23 | false | AZ | TXN10007 | 2024-01-06T10:11:34Z | USD | credit | Interest Payment | interest | ACC102209 | savings | Monthly interest payment | 2843.88 | blank | Phoenix | US |
| CUST1039 | completed | -250 | false | NY | TXN10008 | 2023-12-12T13:57:08Z | USD | transfer | Venmo P2P | person_to_person | ACC103672 | checking | Transfer to Venmo account | 1912.66 | VENM9234 | New York | US |
| CUST1100 | completed | 3800 | false | MA | TXN10009 | 2024-01-03T09:44:17Z | USD | deposit | MediCare Payroll | salary | ACC109821 | checking | Salary deposit from MediCare | 4912.12 | PRL85017 | Boston | US |
| CUST1079 | completed | -54.9 | false | FL | TXN10010 | 2023-12-18T17:22:12Z | USD | debit | Walgreens | pharmacy | ACC107231 | checking | Debit at Walgreens for medication | 852.11 | blank | Miami | US |
| CUST1089 | completed | 2000 | false | TX | TXN10011 | 2023-12-29T12:23:53Z | USD | deposit | FreelancePay | services | ACC106521 | savings | Deposit from FreelancePay | 10032.24 | FRP9901 | Austin | US |
| CUST1092 | pending | -200 | false | CA | TXN10012 | 2024-01-07T06:44:19Z | USD | payment | Apple.com | electronics | ACC108219 | credit_card | Apple online store purchase | -812.27 | blank | Cupertino | US |
| CUST1103 | completed | -650 | false | CO | TXN10013 | 2023-12-15T14:29:12Z | USD | withdrawal | ATM Bank of America | atm | ACC110987 | checking | ATM withdrawal at Bank of America | 1342.09 | blank | Denver | US |
| CUST1050 | completed | 0.89 | false | CA | TXN10014 | 2023-12-17T20:14:16Z | USD | credit | Bank Interest | interest | ACC105872 | savings | Interest credit | 5921.33 | blank | San Diego | US |
| CUST1042 | completed | -34.95 | false | MA | TXN10015 | 2024-01-05T18:32:51Z | USD | debit | CVS Pharmacy | pharmacy | ACC104321 | checking | Debit at CVS for over-the-counter meds | 2104.8 | blank | Boston | US |
| CUST1067 | completed | -112.16 | false | CA | TXN10016 | 2023-12-21T11:15:09Z | USD | payment | Spotify | entertainment | ACC107312 | credit_card | Spotify annual payment | -144.55 | blank | San Francisco | US |
| CUST1057 | completed | -14.25 | false | TX | TXN10017 | 2024-01-04T17:57:21Z | USD | fee | blank | bank_fee | ACC101149 | checking | Overdraft fee | 1859.67 | blank | Houston | US |
| CUST1095 | completed | -350 | false | PA | TXN10018 | 2023-12-27T13:30:11Z | USD | transfer | Zelle Transfer | person_to_person | ACC109582 | checking | Zelle transfer to friend | 3992.61 | ZELLE39 | Philadelphia | US |
| CUST1023 | completed | 24.75 | false | IL | TXN10019 | 2024-01-01T08:11:46Z | USD | refund | Best Buy | electronics | ACC104872 | checking | Refund from Best Buy | 1381.19 | blank | Chicago | US |
| CUST1047 | completed | 15000 | false | NY | TXN10020 | 2024-01-07T19:44:22Z | USD | deposit | Morgan Stanley | investment | ACC102893 | savings | Investment deposit from Morgan Stanley | 25032.14 | MSINV75 | New York | US |
What the 200 rows show
from the 200-row sample- 13%is_
international = true - -9.0median transaction_
amount - 4account types
- 8transaction types
- 10currencies
- 11location countries
Median -9.0, from -75,000 to 1,200,000.
- string 14
- float 2
- datetime 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 14 columns | |||
customer_id | string | Unique identifier for each retail banking customer | CUST1023 |
transaction_id | string | Unique identifier for each transactionunique | TXN10001 |
currency | string | ISO 4217 currency code for the transaction (e.g., USD, EUR)10 currencies | USD |
transaction_type | string | Type of transaction (e.g., debit, credit, payment, withdrawal, deposit, transfer, fee, refund)9 values | debit |
merchant_name | string | Name of the merchant or payee involved in the transaction, if applicableoptional | Target |
merchant_category | string | Category of the merchant or transaction (e.g., groceries, utilities, entertainment, travel, healthcare)optional | groceries |
account_id | string | Unique identifier for the customer account used in the transaction | ACC104872 |
account_type | string | Type of account used (e.g., checking, savings, credit_card, loan)checking · savings · credit_card · loan · other | checking |
transaction_description | string | Free-text description or memo for the transactionoptional | Refund from Amazon |
transaction_status | string | Status of the transaction (e.g., completed, pending, failed, reversed)completed · pending · failed · reversed | completed |
counterparty_account | string | Account number or identifier for the counterparty, if applicable (e.g., for transfers)optional | PRL67839 |
location_city | string | City where the transaction took place, if availableoptional | Chicago |
location_state | string | State or province where the transaction took place, if availableoptional | IL |
location_country | string | Country where the transaction took place, if available11 countries · optional | US |
| Numbers 2 columns | |||
transaction_amount | float | Monetary value of the transaction (positive for credit, negative for debit) | -75.32 |
balance_after_transaction | float | Account balance immediately after the transactionoptional | 1356.44 |
| Dates and times 1 column | |||
transaction_datetime | datetime | Date and time when the transaction occurred | 2023-12-11T16:25:42Z |
| True or false 1 column | |||
is_international | boolean | Indicates if the transaction was international (true) or domestic (false)optional | false |
Use it for
A finance dashboard
The is_
international rate, transaction_ amount by transaction_ status and a breakdown of location_ state. Excel, Power BI or Tableau. Why do 26 of 200 rows have is_
international = true? A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
- Customers200CUST1023-75.32completedCUST1052-3500completedCUST1097-50000completed
A software demo
Believable customers with transaction_
id, transaction_ datetime and transaction_ amount to fill a screen in front of a buyer.
blueprint · retail-banking-transaction-sequences
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 18 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.
- Each transaction linked to unique customer.
- Fields include amount, merchant, type, and channel.
- Temporal gaps between transactions are preserved.
- All data is anonymized.
- Flags for suspicious activity are included.
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
- retail-banking-transaction-sequences