Early Money Laundering Network Growth Prediction and Prioritization
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Solution Overview
Problem
Existing anti-money-laundering systems struggle to effectively predict and prevent the growth of money laundering networks, leading to increased resource allocation and financial losses as these networks expand.
Innovation Solution
A system and method using artificial intelligence, including machine-learning models and predictive algorithms, to monitor and predict the growth of money laundering networks, allowing for preemptive action by generating scores to prioritize networks at high risk of future growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-money-laundering systems flag suspicious accounts and networks, then suspicious activity is identified, but the networks continue to grow and remain active
Solution Approach 1:
The system performs preliminary actions by predicting future network growth before it occurs. The growth prediction module analyzes current network characteristics and transaction patterns to forecast which flagged networks are likely to expand, enabling preemptive intervention before the networks actually grow and cause greater harm.
Solution Approach 2:
The system prepares beforehand by establishing priority rankings of flagged networks based on predicted growth risk. This cushioning mechanism ensures that when resources are allocated for investigation and takedown operations, the highest-risk networks are addressed first, preventing the worst-case scenarios from materializing.
2Reliability
If financial institutions investigate all flagged money laundering networks, then comprehensive monitoring is achieved, but time and resource allocations increase significantly
Solution Approach 1:
The system applies local quality by differentiating between high-risk and low-risk flagged networks. Instead of uniform monitoring of all networks, the growth prediction module identifies specific networks with characteristics indicating high likelihood of expansion, concentrating investigative resources on these localized high-risk targets while reducing scrutiny on lower-risk networks.
Solution Approach 2:
The system changes the parameter of resource allocation from uniform distribution across all flagged networks to differentiated allocation based on predicted growth risk. The prioritization engine transforms the resource allocation strategy by using growth prediction scores to dynamically adjust investigation intensity and resource deployment across different networks.
3Productivity
If money laundering networks are allowed to grow, then more transactions and accounts are processed, but costs and money losses increase
Solution Approach 1:
The system applies preliminary anti-action by identifying and prioritizing networks that are likely to grow into high-risk money laundering operations. By predicting growth patterns and flagging networks before they expand significantly, the system enables preemptive takedown actions that prevent these networks from processing large volumes of illicit transactions, thereby avoiding future financial losses.
Data Source
AI summary
A growth predictor includes a monitor, a prediction engine, and a prioritization engine. The monitor receives or generates first information of a network already identified as a candidate money laundering (ML) network by an anti-money-laundering system. The prediction engine predicts second information indicative of a growth size of the ML network at a future time based on the first information. The prediction engine executes one or more predictive models to generate the second information indicative of growth size based on the first information, which indicates one or more changes that have occurred in the candidate ML network over a past period of time. The prioritization engine determines a priority of the candidate ML network based on the second information.