Detecting money laundering activities using drift in a trained Siamese neural network

US11880890B2 · kind B2 · utility

Assignee

• MASTERCARD INTERNATIONAL INCORPORATED · US

Inventors

• Debasmita Das · Sherghati, IN
• Sonali Syngal · Bhangrola, IN
• Ankur Saraswat · Bhangrola, IN
• Garima Arora · Delhi, IN
• Nishant Pant · Dehradun, IN
• Yatin Katyal · Rohtak, IN

Key dates

• Filing date: Feb 8, 2021
• Grant date: Jan 23, 2024
• Expiry date: Jun 11, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/09
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Siamese neural networks (SNN) are configured to detect differences between financial transactions for multiple financial institutions and transactions for a target party. A first neural network of the SNN tracks transactions (target transactions) for a particular customer or financial institution over time and provides a target output vector. Similarly, a second neural network of the SNN tracks transactions (baseline transactions) for all or a plurality of financial institutions (e.g., within a region) over the same period of time and provides a baseline output vector. The transactions for all or a plurality of financial institutions act as a baseline of transactions against which potentially fraudulent or money laundering activity may be compared. Because Siamese neural networks account for temporal changes based on the baseline of transactions, sudden changes in target transactions will only trigger an alarm if such changes (e.g., deviations or drifts) are relative to a baseline of transactions.