Black Money Transactions Dataset

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Black Money Transactions Dataset

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Black Money Transactions Dataset

Use Case

Black Money Transactions Dataset

Description

Explore the Global Black Money Transactions Dataset for insights into illicit financial activities. Discover transaction details, risk scores, shell company involvement, and tax haven transfers.

Description:

Explore the intricate world of black money movements across borders with this extensive dataset. It provides critical insights into illicit financial activities involving multiple countries. Perfect for financial analysts, anti-money laundering experts, or researchers investigating suspicious financial patterns, this dataset offers a rich array of data points essential for comprehensive analysis.

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Key Columns:

Transaction ID: Unique reference for every transaction (e.g., TX0000001).
Country: Country of origin for the transaction (e.g., USA, China).
Amount (USD): Transaction value in US dollars (e.g., 150,000.00).
Transaction Type: Nature of the transaction (e.g., Offshore Transfer, Real Estate Purchase).
Date of Transaction: Timestamp for the transaction (e.g., 2022-03-15 14:32:00).
Person Involved: Identifier for individuals/entities involved (e.g., Entity_5678).
Industry: Sector tied to the transaction (e.g., Real Estate, Banking, Technology).
Destination Country: Country receiving the funds (e.g., Switzerland).
Reported by Authority: Flag indicating whether authorities were notified (e.g., Yes/No).
Source of Money: Legitimacy of the money’s origin (e.g., Legal, Illegal).
Money Laundering Risk Score: Likelihood of laundering on a scale of 1-10 (e.g., 8).
Shell Companies Involved: Number of shell companies facilitating the transaction (e.g., 2).
Financial Institution: Bank or organization handling the transaction (e.g., Bank_123).
Tax Haven Country: Tax haven used in the transaction (e.g., Cayman Islands).
Involvement of Third Parties: Presence of intermediaries or third-party facilitators (e.g., Yes/No).
Multiple Currencies: Whether multiple currencies were involved (e.g., USD, EUR).
Time Elapsed: Duration between suspicious transaction flags and reporting (e.g., 5 days).

Dataset Highlights:

Global Coverage: Spanning multiple countries and industries, this dataset includes a wide variety of countries that are often implicated in financial irregularities. From major global financial hubs to developing nations, the dataset offers a global perspective on black money movements.
Transaction Granularity: Each transaction is uniquely identified and described with a wide array of financial data points. This includes specific countries, industries involved, and other key transactional details such as amounts, dates, and destination countries.
Risk Assessment: One of the key aspects of this dataset is its Money Laundering Risk Score. With scores from 1 to 10, each transaction is rated on its risk of being involved in laundering activities. This attribute alone can be instrumental in training machine learning models to detect high-risk financial behavior.