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.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Customer segmentation and behavioral modeling

Sample rows

preview · 8 of 200 rows · all 18 columns
customer_idstringtransaction_statusstringtransaction_amountfloatis_internationalbooleanlocation_statestringtransaction_idstringtransaction_datetimedatetimecurrencystringtransaction_typestringmerchant_namestringmerchant_categorystringaccount_idstringaccount_typestringtransaction_descriptionstringbalance_after_transactionfloatcounterparty_accountstringlocation_citystringlocation_countrystring
CUST1023completed-75.32falseILTXN100012023-12-11T16:25:42ZUSDdebitTargetgroceriesACC104872checkingPOS debit at Target for groceries1356.44blankChicagoUS
CUST1045completed2850falseTXTXN100022023-12-13T08:51:33ZUSDdepositAbco Corp PayrollsalaryACC100322checkingSalary deposit from Abco Corp4422.13PRL67839DallasUS
CUST1081completed-150falseCATXN100032023-12-19T19:18:10ZUSDwithdrawalATM Wells FargoatmACC107651checkingATM withdrawal at Wells Fargo963.27blankSan JoseUS
CUST1094completed38.45falseWATXN100042023-12-28T11:04:02ZUSDcreditAmazon RefundecommerceACC106781credit_cardRefund from Amazon-312.88blankSeattleUS
CUST1072pending-110.25falseCATXN100052024-01-02T15:33:53ZUSDpaymentNetflixentertainmentACC101283credit_cardMonthly Netflix subscription payment-421.67blankLos AngelesUS
CUST1057completed-12.5falseTXTXN100062023-12-24T21:40:16ZUSDfeeblankbank_feeACC101149checkingMonthly maintenance fee1873.92blankHoustonUS
CUST1031completed1.23falseAZTXN100072024-01-06T10:11:34ZUSDcreditInterest PaymentinterestACC102209savingsMonthly interest payment2843.88blankPhoenixUS
CUST1039completed-250falseNYTXN100082023-12-12T13:57:08ZUSDtransferVenmo P2Pperson_to_personACC103672checkingTransfer to Venmo account1912.66VENM9234New YorkUS

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
transaction_status200 rows by value
090180179completed90%12pending6%9reversed5%
transaction_amount200 rows, in bands of 200k
0601201138210022-200k600k1.2Mtransaction_amount →

Median -9.0, from -75,000 to 1,200,000.

location_state200 rows · top 10 of 38 values
  1. TX28
  2. CA22
  3. MA16
  4. WA13
  5. FL13
  6. IL12
  7. ON11
  8. AZ8
  9. BE6
  10. NY5
18 columns by typefrom the column list below
  • string 14
  • float 2
  • datetime 1
  • boolean 1

Columns

18 columns in four groups
blueprint · 18 columns
columntypedescriptionexample
Text 14 columns
customer_idstringUnique identifier for each retail banking customerCUST1023
transaction_idstringUnique identifier for each transactionuniqueTXN10001
currencystringISO 4217 currency code for the transaction (e.g., USD, EUR)10 currenciesUSD
transaction_typestringType of transaction (e.g., debit, credit, payment, withdrawal, deposit, transfer, fee, refund)9 valuesdebit
merchant_namestringName of the merchant or payee involved in the transaction, if applicableoptionalTarget
merchant_categorystringCategory of the merchant or transaction (e.g., groceries, utilities, entertainment, travel, healthcare)optionalgroceries
account_idstringUnique identifier for the customer account used in the transactionACC104872
account_typestringType of account used (e.g., checking, savings, credit_card, loan)checking · savings · credit_card · loan · otherchecking
transaction_descriptionstringFree-text description or memo for the transactionoptionalRefund from Amazon
transaction_statusstringStatus of the transaction (e.g., completed, pending, failed, reversed)completed · pending · failed · reversedcompleted
counterparty_accountstringAccount number or identifier for the counterparty, if applicable (e.g., for transfers)optionalPRL67839
location_citystringCity where the transaction took place, if availableoptionalChicago
location_statestringState or province where the transaction took place, if availableoptionalIL
location_countrystringCountry where the transaction took place, if available11 countries · optionalUS
Numbers 2 columns
transaction_amountfloatMonetary value of the transaction (positive for credit, negative for debit)-75.32
balance_after_transactionfloatAccount balance immediately after the transactionoptional1356.44
Dates and times 1 column
transaction_datetimedatetimeDate and time when the transaction occurred2023-12-11T16:25:42Z
True or false 1 column
is_internationalbooleanIndicates if the transaction was international (true) or domestic (false)optionalfalse

Use it for

  • is internation…13%26 of 200 rowsrows by transaction s…179comple…12pending9revers…

    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.

  • A software demo

    Believable customers with transaction_id, transaction_datetime and transaction_amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 18 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows18 columns
Yours1,000 rows18 columnslocation_city: UK only

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.

Rules it was built with
  • 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.
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
retail-banking-transaction-sequences

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