Siamese Neural Network Drift Detection for Money Laundering
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Solution Overview
Problem
Current anti-money laundering (AML) solutions struggle to effectively detect money laundering activities from a large volume of financial transactions due to active concealment by parties and the difficulty in tracking transactions across multiple financial institutions.
Innovation Solution
A method utilizing a Siamese neural network, specifically bidirectional long short-term memory (LSTM) recurrent neural networks, to generate output vectors for target and baseline financial transactions, calculating a drift score to identify potential money laundering by comparing transactions of a target party against a baseline of transactions from similar parties, triggering an alarm if the score indicates suspicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AML monitoring methods are used to review financial transactions, then detection capability is maintained at basic levels, but the system cannot effectively handle large volumes of transactions and active concealment by parties
Solution Approach 1:
The patent replaces traditional mechanical AML monitoring methods with a Siamese neural network system that uses bidirectional LSTM recurrent neural networks. This automated machine learning system processes transactions without manual intervention, achieving both high detection precision through learned discriminative spaces and high productivity by handling large transaction volumes efficiently.
Solution Approach 2:
The system transforms transaction data into vector representations and measures drift in this transformed parameter space. By changing from traditional transaction analysis parameters to neural network output vectors, the system can detect subtle patterns of money laundering while maintaining high processing speeds through efficient vector distance calculations.
2Measurement precision
If manual review of financial transactions is performed to ensure accurate detection, then detection precision is maintained, but the system cannot scale to handle large volumes of transactions across multiple financial institutions
Solution Approach 1:
The patent replaces manual review processes with an automated Siamese neural network system. The bidirectional LSTM networks automatically learn to distinguish legitimate from suspicious transactions, maintaining high detection accuracy while eliminating the scalability limitations of manual review. The system can process transactions across multiple financial institutions simultaneously.
Solution Approach 2:
The Siamese neural network architecture is designed to be universal across different financial institutions and transaction types. The same network structure and training methodology can be applied to various datasets, making the system scalable and adaptable without requiring institution-specific manual configuration for each new partner.
3Reliability
If traditional transaction monitoring systems are used to track money laundering across multiple financial institutions, then basic monitoring is achieved, but the system cannot effectively track transactions across institutional boundaries
Solution Approach 1:
The Siamese neural network system is designed with universal applicability across multiple financial institutions. Both institutions use identical network architectures and training methodologies, enabling consistent detection standards and reliable tracking of transactions across institutional boundaries. The system can ingest and process data from different sources using the same processing pipeline.
Solution Approach 2:
The patent introduces a centralized drift detection server that acts as an intermediary between multiple financial institutions. This server receives transaction data from various institutions, processes it through the Siamese neural network, and generates drift scores that indicate potential money laundering. The intermediary enables cross-institutional tracking while maintaining system reliability through standardized processing.
Data Source
AI summary
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.