Finance22 columns
Wire Transfer Compliance Data
This dataset provides comprehensive wire transfer compliance records, including originator and beneficiary details, screening outcomes, regulatory holds, and cross-border reporting flags. It enables financial institutions and regulators to monitor, audit, and analyze wire transfers for compliance with anti-money laundering (AML) and cross-border transaction regulations.
opened 1 timeslast updated 29 Oct 2025by GoMask
The brief that made it
Automated compliance screening and reporting for wire transfers
Blueprint
blueprint · 22 columns
| column | type | description |
|---|---|---|
| transfer_id | string | Unique identifier for each wire transfer transaction |
| transfer_date | datetime | Date and time when the wire transfer was initiated |
| amount | float | Total amount transferred in the wire transaction |
| currency | string | ISO 4217 currency code for the transfer amount (e.g., USD, EUR) |
| originator_name | string | Full legal name of the wire transfer originator |
| originator_account_number | string | Account number of the wire transfer originator |
| originator_bank_name | string | Name of the originator's bank |
| originator_bank_country | string | Country where the originator's bank is located |
| beneficiary_name | string | Full legal name of the wire transfer beneficiary |
| beneficiary_account_number | string | Account number of the wire transfer beneficiary |
| beneficiary_bank_name | string | Name of the beneficiary's bank |
| beneficiary_bank_country | string | Country where the beneficiary's bank is located |
| purpose_code | string | Standardized code indicating the purpose of the wire transfer (e.g., salary, invoice payment) |
| purpose_description | string | Detailed description of the wire transfer's purpose |
| regulatory_hold_flag | boolean | Indicates if the transfer is currently on regulatory hold |
| regulatory_hold_reason | string | Reason for regulatory hold, if applicable |
| screening_status_originator | string | Screening result for the originator (e.g., cleared, flagged, pending) |
| screening_status_beneficiary | string | Screening result for the beneficiary (e.g., cleared, flagged, pending) |
| cross_border_flag | boolean | Indicates if the wire transfer is cross-border (international) |
| reporting_required_flag | boolean | Indicates if regulatory reporting is required for this transfer |
| reporting_reference_number | string | Reference number for regulatory reporting, if applicable |
| compliance_notes | string | Additional notes or comments from compliance officers |
Sample rows
preview · 4 of 600 rows
| transfer_id | transfer_date | amount | currency | originator_name |
|---|---|---|---|---|
| T20240001 | 2024-05-28T09:25:41 | 54.78 | USD | Robert Hall |
| T20240002 | 2024-06-01T17:59:59 | 9999999.99 | USD | Clara Williams |
| T20240003 | 2024-05-31T23:59:59 | 100 | EUR | Jan Novak |
| T20240004 | 2024-06-04T08:14:32 | 4589.32 | GBP | Sophie Taylor |
Behind this dataset
blueprint · wire-transfer-compliance-data
Same schema. As many rows as you need.
These 600 rows came out of a blueprint — 22 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.
1,000 rows10,000 rows100,000 rows1,000,000 rows
Open the blueprint in Data FactoryFree tier covers your first 1,000 rows.
More in Finance
Browse all →Bank Transaction Categorization SampleThis 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.Finance Expense Categorization DatasetThis 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.Personal Finance Budgeting RecordsThis 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.