Finance Transaction Time-of-Day Trends

This dataset contains detailed, timestamped financial transactions from retail banks and payment processors, including account, merchant, channel, and location information. Designed for in-depth analysis of temporal spending patterns, fraud detection, and channel usage trends, it supports a wide range of financial analytics and business intelligence applications.

  • last updated 29 Jul 2025
  • by GoMask
The brief that made it

Temporal analysis of customer spending patterns

Sample rows

preview · 8 of 200 rows · all 15 columns
transaction_idstringchannelstringtransaction_amountfloatis_fraudulentbooleanmerchant_category_codestringaccount_idstringtransaction_timestampdatetimecurrency_codestringmerchant_idstringmerchant_namestringtransaction_typestringlocation_citystringlocation_statestringlocation_countrystringtransaction_statusstring
a9F8b2C3-1dE4POS-54.23false5814ACC0000012023-05-09T08:13:43ZUSDMRCHT001StarbuckspurchaseSeattleWAUScompleted
3kXn5z7P-2mQ8in-branch1200falseblankACC0000022023-05-09T06:47:21ZUSDblankblankdepositDenverCOUScompleted
b2NtR5Pa-12qaPOS-250false5411ACC0000032023-05-09T13:18:55ZEURMRCHT002CarrefourpurchaseParisIDFFRcompleted
LmP7Qw2v-8sZ9mobile-20.5false5411ACC0000042023-05-09T16:45:37ZGBPMRCHT003Tesco ExpresspurchaseLondonLNDGBcompleted
HqN4v6eL-5yT7POS-80.75false5541ACC0000052023-05-09T21:03:14ZUSDMRCHT004Shell OilpurchaseHoustonTXUScompleted
QyT1u9Gh-3wX6online-200true5732ACC0000062023-05-09T19:22:09ZUSDMRCHT005Apple StorepurchaseSan JoseCAUSpending
d8R3k7Lm-5pN1ATM2000falseblankACC0000012023-05-09T17:38:00ZUSDblankblankdepositBellevueWAUScompleted
x3wVt8Qz-7kC2POS-35.65false5812ACC0000072023-05-08T09:10:13ZUSDMRCHT006SubwaypurchaseChicagoILUScompleted

What the 200 rows show

from the 200-row sample

In-branch channel stands out: mean transaction_amount is 5,130, against -27.1 for the rest.

  • 6%is_fraudulent = true
  • -38.0median transaction_amount
  • 4transaction statuses
  • 6currency codes
  • 7transaction types
  • 12location countries
transaction_amount200 rows, in bands of 10k
080160157411000001-10k40k80ktransaction_amount →

Median -38.0, from -2,500 to 80,000.

merchant_category_code142 rows with a value · 58 left blank
  1. 541139
  2. 591220
  3. 581414
  4. 581213
  5. 573212
  6. 59429
  7. 56519
  8. 52005
  9. 41214
  10. 55413
15 columns by typefrom the column list below
  • string 12
  • float 1
  • datetime 1
  • boolean 1

Columns

15 columns in four groups
blueprint · 15 columns
columntypedescriptionexample
Text 12 columns
transaction_idstringUnique identifier for each financial transactionuniquea9F8b2C3-1dE4
account_idstringUnique identifier for the customer or account initiating the transactionACC000001
currency_codestringISO 4217 currency code for the transaction (e.g., USD, EUR, GBP)6 codesUSD
merchant_idstringUnique identifier for the merchant or payee involved in the transactionoptionalMRCHT001
merchant_namestringName of the merchant or payeeoptionalStarbucks
merchant_category_codestringMerchant Category Code (MCC) indicating the type of business or serviceoptional5814
transaction_typestringType of transaction (e.g., purchase, withdrawal, deposit, transfer, payment, refund)8 valuespurchase
channelstringChannel through which the transaction was made (e.g., online, in-branch, ATM, mobile, POS)6 values · optionalPOS
location_citystringCity where the transaction took place (if available)optionalSeattle
location_statestringState or province where the transaction took place (if available)optionalWA
location_countrystringCountry where the transaction took place (ISO 3166-1 alpha-2 code)12 countries · optionalUS
transaction_statusstringCurrent status of the transaction (e.g., completed, pending, failed, reversed)completed · pending · failed · reversedcompleted
Numbers 1 column
transaction_amountfloatMonetary value of the transaction (positive for credits, negative for debits)-54.23
Dates and times 1 column
transaction_timestampdatetimeDate and time when the transaction occurred (in UTC)2023-05-09T08:13:43Z
True or false 1 column
is_fraudulentbooleanIndicates whether the transaction was flagged as potentially fraudulentoptionalfalse

Use it for

  • is fraudulent6%12 of 200 rows

    A finance dashboard

    The is_fraudulent rate, transaction_amount by channel and a breakdown of merchant_category_code. Excel, Power BI or Tableau.

  • Why do the 25 in-branch rows have a mean transaction_amount of 5,130?

    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 transactions with account_id, transaction_timestamp and transaction_amount to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows15 columns
Yours1,000 rows15 columnslocation_city: UK only

blueprint · finance-transaction-time-of-day-trends

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 15 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
  • Transactions grouped by account and type
  • Morning and evening peaks simulated
  • Weekend vs weekday effects visible
  • Large transactions flagged for compliance
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
finance-transaction-time-of-day-trends

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