ATM Transaction Usage Patterns

This dataset provides daily aggregated cash withdrawal and deposit activity for each ATM, including transaction counts, total and average amounts, unique card usage, and a flag for suspicious activity. The data is ideal for analyzing ATM usage patterns, forecasting cash demand, and monitoring for potential fraud across different locations. Detailed location fields enable geographic and branch-level insights.

  • opened 13 times
  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

ATM cash demand forecasting and optimization

Sample rows

preview · 8 of 201 rows · all 17 columns
atm_idstringtotal_withdrawal_amountfloatsuspicious_activity_flagbooleanlocation_countrystringlocation_idstringlocation_street_addressstringlocation_citystringlocation_statestringlocation_postal_codestringtransaction_datedatetotal_withdrawal_countintegertotal_deposit_countintegertotal_deposit_amountfloatunique_card_countintegeraverage_withdrawal_amountfloataverage_deposit_amountfloatnotesstring
ATM00127389.5falseUSALOC1001100 Main StChicagoIL606012024-06-0396123422.1587285.31285.18Busy weekday in downtown business district
ATM0029650falseCanadaLOC100245 Queen StTorontoONM5H 2N22024-06-01325122528301.56245Steady weekend flow, tourist area
ATM00314560falseUSALOC1003200 Market LaneBostonMA021102024-06-075711297054255.44270University area, start of month
ATM0047740falseUKLOC100417 High RoadLondonENGN12 8TP2024-06-0824252020322.5260Busy Saturday, city center
ATM005121800trueUSALOC100588 Sunset BlvdLos AngelesCA900282024-06-0121021840200580920Concert event nearby, extremely high withdrawals
ATM006640falseUSALOC100612 Riverbank AveSt. LouisMO631012024-06-02411104160110Low activity at night
ATM0072860falseAustraliaLOC100755 Harbor StreetSydneyNSW20002024-06-0413227010220135Bilingual display tested, steady weekday
ATM00832940falseUSALOC10089 Elm PlaceHoustonTX770022024-06-05122144732110270338Payday, high activity in business area

What the 201 rows show

from the 201-row sample
  • 5%suspicious_activity_flag = true
  • 5,520median total_withdrawal_amount
  • 44location states
  • 22median total_withdrawal_count
  • 4median total_deposit_count
  • 1,125median total_deposit_amount
total_withdrawal_amount201 rows, in bands of 100k
095190190520000013suspicious_activity_flag from 100k100k500k1Mtotal_withdrawal_amount →

Every row with total_withdrawal_amount of 100k or more has suspicious_activity_flag = true. None below it.

location_country201 rows · top 10 of 15 values
  1. USA104
  2. Canada19
  3. UK18
  4. Australia12
  5. Ireland9
  6. N/A9
  7. China8
  8. Austria7
  9. France4
  10. Switzerland3
17 columns by typefrom the column list below
  • string 8
  • integer 3
  • float 4
  • date 1
  • boolean 1

Columns

17 columns in four groups
blueprint · 17 columns
columntypedescriptionexample
Text 8 columns
atm_idstringUnique identifier for the ATM machine.ATM001
location_idstringUnique identifier for the ATM's location.LOC1001
location_street_addressstringStreet address of the ATM location.optional100 Main St
location_citystringCity where the ATM is located.optionalChicago
location_statestringState or province where the ATM is located.optionalIL
location_postal_codestringPostal code of the ATM location.optional60601
location_countrystringCountry where the ATM is located.optionalUSA
notesstringFree-text notes or comments related to the ATM's activity for the given date.optionalLow activity at night
Numbers 7 columns
total_withdrawal_countintegerTotal number of withdrawal transactions at the ATM on the given date.0 or more96
total_withdrawal_amountfloatTotal amount withdrawn from the ATM on the given date.0 or more27389.5
total_deposit_countintegerTotal number of deposit transactions at the ATM on the given date.0 or more12
total_deposit_amountfloatTotal amount deposited into the ATM on the given date.0 or more3422.15
unique_card_countintegerNumber of unique cards used at the ATM on the given date.0 or more · optional87
average_withdrawal_amountfloatAverage amount per withdrawal transaction at the ATM on the given date.0 or more · optional285.31
average_deposit_amountfloatAverage amount per deposit transaction at the ATM on the given date.0 or more · optional285.18
Dates and times 1 column
transaction_datedateDate for which the transaction data is aggregated.2024-06-03
True or false 1 column
suspicious_activity_flagbooleanIndicates if suspicious or potentially fraudulent activity was detected for this ATM on the given date.optionalfalse

15 SQL practice questions for the ATM Transaction Usage Patterns

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

  1. 01BeginnerFreeSELECT · WHERE · ORDER BY

    Suspicious ATM-days

    List every record where suspicious_activity_flag is 1. Return the columns atm_id, location_city, transaction_date and total_withdrawal_amount, ordered by transaction_date ascending and then atm_id ascending.

    Tables: atm_transaction_usage_patterns · your result: 4 columns, 11 rows
  2. 02BeginnerFreeDISTINCT · ORDER BY

    Countries with ATMs

    Return the distinct location_country values in the data, sorted alphabetically. The result must have a single column named location_country.

    Tables: atm_transaction_usage_patterns · your result: 1 columns, 15 rows
  3. 03BeginnerFreeGROUP BY · COUNT

    Records per country

    Count the records for each location_country. Return location_country and record_count, ordered by record_count descending and then location_country ascending.

    Tables: atm_transaction_usage_patterns · your result: 2 columns, 15 rows
  1. 04Total withdrawals by countryBeginner
  2. 05Average deposit by countryPower BI
  3. 06Cities with multiple flagsIntermediate
  4. 07Withdrawal volume bucketsIntermediate
  5. 08Countries above overall averageIntermediate
  6. 09Weekly withdrawalsIntermediate
  7. 10Suspicious rate by countryPower BI
  8. 11Top ATM-days within countryAdvanced
  9. 12Running total by dateAdvanced
  10. 13Day-over-day changeAdvanced
  11. 14Country share of depositsAdvanced
  12. 15Anomalous withdrawals vs country averagePower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

  • suspicious act…5%11 of 201 rows

    A finance dashboard

    The suspicious_activity_flag rate, total_withdrawal_amount and a breakdown of location_country. Excel, Power BI or Tableau.

  • Why do 11 of 201 rows have suspicious_activity_flag = true?

    A class exercise

    Hand out the rows and one question. Everyone works from the same 201 rows.

  • A software demo

    Believable atms with location_id, location_street_address and location_city to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 17 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset201 rows17 columns
Yours1,000 rows17 columnslocation_id: UK only

blueprint · atm-transaction-usage-patterns

Behind this dataset

Same schema. As many rows as you need.

These 201 rows came out of a blueprint — 17 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
  • ATM ID, timestamp, and transaction type required
  • Include transaction amount and location type
  • No customer-level identifiers
  • Simulate various cash denominations
Rows
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
atm-transaction-usage-patterns

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