Finance17 cols · 200 rows
This dataset contains labeled bank transaction records, including detailed transaction metadata, merchant information, and manually or automatically assigned expense categories. It is ideal for developing, training, and benchmarking automated expense categorization models for personal finance, budgeting, and regulatory compliance applications.
| merchant_name | amount | category |
|---|
| Whole Foods Market | -95.32 | groceries |
| ACME Corp | 2500 | income |
| Netflix | -56.8 | entertainment |
posting_dateis_recurringmerchant_category_code+11 more
Finance18 cols · 200 rows
This dataset contains detailed synthetic bank transaction records, each labeled with spending categories such as groceries, travel, and utilities. It includes transaction metadata, merchant details, recurrence information, and account associations, making it ideal for developing and benchmarking personal finance management tools, automated expense categorization, and financial analytics solutions.
| merchant_name | merchant_category | category |
|---|
| FreshMart | Supermarket | groceries |
| NetVision | Streaming Service | entertainment |
| MetroCorp Payroll | Payroll | income |
transaction_idaccount_idtransaction_date+12 more
Finance20 cols · 200 rows
This dataset provides a comprehensive, labeled collection of real-world bank transactions, including raw descriptions, merchant information, categorized labels, and enrichment fields for AI training. It enables robust development of transaction categorization, statement enrichment, and personal finance analytics models, supporting both supervised and semi-supervised learning scenarios.
| merchant_name | category | subcategory |
|---|
| Amazon | Shopping | Online Retail |
| Acme Corp | Income | Salary |
| Spotify | Entertainment | Music Streaming |
transaction_idaccount_idtransaction_date+14 more
Finance17 cols · 206 rows
This dataset provides granular, masked credit card transaction records including transaction amounts, merchant details, timestamps, authorization codes, and fraud flags. It supports robust analysis for fraud detection, merchant risk assessment, and payment trends across regions and card types. The dataset is ideal for financial institutions, payment processors, and analytics teams seeking actionable insights into card-based payments.
| merchant_name | card_type | transaction_status |
|---|
| FreshMart Grocery | Visa | approved |
| Burger Express | MasterCard | approved |
| TechZone Electronics | Visa | approved |
transaction_idmasked_card_numbertransaction_amount+11 more
Finance18 cols · 200 rows
This dataset provides granular, transaction-level data on digital wallet usage, including top-ups, peer-to-peer transfers, and merchant payments. It features rich contextual information such as user, wallet, merchant, device, and location details, making it ideal for payment analytics, fraud detection, and fintech product development.
| transaction_type | status | merchant_name |
|---|
| top-up | completed | – |
| transfer | pending | – |
| merchant-payment | completed | Tesco Express |
transaction_iduser_idtransaction_datetime+12 more
Finance17 cols · 201 rows
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.
| location_country | total_withdrawal_count | suspicious_activity_flag |
|---|
| USA | 96 | false |
| Canada | 32 | false |
| USA | 57 | false |
atm_idlocation_idlocation_street_address+11 more
Finance25 cols · 500 rows
This dataset provides detailed, end-of-day and periodic bank statements in compliance with ISO20022 camt.053 standards, including complete transaction histories, charges, interest calculations, and final balances for each account. It is ideal for accounting, reconciliation, financial analysis, and regulatory reporting, offering granular insights into account activity and transaction-level details.
| transaction_type | counterparty_name | charge_type |
|---|
| credit | Acme Corp | – |
| debit | SuperMart | – |
| debit | BerlinElectric | – |
statement_idaccount_idaccount_iban+19 more
Finance18 cols · 200 rows
This dataset provides detailed, household-level records of income and expenses, including transaction categories, payment methods, recurrence patterns, and basic household demographics. It enables comprehensive budgeting analysis, supports financial literacy initiatives, and can power personalized financial recommendations and research into household spending habits.
| record_type | category | subcategory |
|---|
| income | salary | – |
| expense | rent | – |
| income | investment | dividends |
household_idrecord_idamount+12 more
Finance20 cols · 200 rows
This dataset contains detailed, structured expense records including transaction dates, amounts, categories, payment methods, merchant details, and location information. It is ideal for personal finance management, business accounting, machine learning classification, and budgeting applications, providing granular insights into spending patterns and expense tracking.
| merchant_name | amount | category |
|---|
| The Grove Bistro | 53.25 | Food |
| Pacific Utilities | 0 | Other |
| Netflix Australia | 19.99 | Entertainment |
currencyis_business_expensereceipt_available+14 more
Finance28 cols · 200 rows
This dataset contains rich, structured information about bank customers, including demographics, account details, product holdings, financial metrics, and segmentation labels. It is ideal for financial institutions seeking to personalize marketing, manage risk, and identify cross-selling opportunities through data-driven customer segmentation and profiling.
| first_name | last_name | account_type |
|---|
| Alex | Jones | checking |
| Meera | Singh | student |
| Riley | Brown | savings |
customer_idgenderdate_of_birth+22 more
Finance19 cols · 200 rows
This dataset provides comprehensive performance metrics for individual bank branches, including account activity, transaction volumes, and customer feedback scores over specific reporting periods. It enables detailed analysis of operational efficiency, customer satisfaction, and issue resolution, supporting data-driven management decisions and branch benchmarking.
| branch_name | address_country | accounts_opened |
|---|
| Downtown Financial | USA | 400 |
| Bayview Branch | USA | 380 |
| Lakeshore Center | USA | 305 |
branch_idbranch_manageraddress_street+13 more