Unified Anti-Money Laundering System for Payment Card Channels
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Solution Overview
Problem
Current systems for electronic payment cards lack an aggregated view of end-to-end money flow, making it difficult to detect money laundering activities, particularly through multiple re-loads, purchases, and withdrawals across various channels, which facilitates anonymous loading and rapid movement of funds, evading regulatory oversight.
Innovation Solution
A dynamic anti-money laundering system that receives real-time transaction data from multiple channels, generates transactional profiles, uses predictive algorithms to calculate a probabilistic money laundering score, and generates suspicious activity reports to recommend approval or reporting of transactions, while transmitting these reports to the payment card system and regulatory bodies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple independent channels are used for payment card transactions, then ease of operation and accessibility are improved, but the ability to detect money laundering deteriorates due to lack of aggregated view
Solution Approach 1:
The patent merges data from multiple independent channels (web, mobile, agents, point of sale) into a unified monitoring system that aggregates transaction data across all channels. This allows the system to detect money laundering patterns by analyzing combined data while maintaining the operational independence and accessibility of each individual channel.
2Difficulty of detecting and measuring
If real-time monitoring of all transaction channels is implemented, then money laundering detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a central monitoring system that acts as an intermediary between multiple transaction channels and regulatory bodies. This intermediary aggregates data from all channels, applies analytics and machine learning models to detect money laundering patterns, and generates unified reports, thereby managing complexity centrally while maintaining simple interfaces at each channel level.
Solution Approach 2:
The monitoring system is segmented into modular components including data collection modules for each channel, data aggregation layer, analytics engine with machine learning models, and reporting module. This segmentation allows independent development and maintenance of each component while achieving comprehensive money laundering detection through their coordinated operation.
3Difficulty of detecting and measuring
If aggregated view of end-to-end money flow is provided, then money laundering detection is improved, but data processing requirements and system resources increase
Solution Approach 1:
The system performs preliminary data processing and filtering at each channel level before aggregation, pre-aggregating transaction data in near-real-time. This preliminary action reduces the volume of raw data that needs to be processed centrally while maintaining the ability to detect money laundering patterns across the entire transaction flow.
Solution Approach 2:
The patent transforms raw transaction data into standardized parameters and features suitable for analytical processing. By changing the representation of data from detailed transaction records to aggregated statistical parameters and risk indicators, the system reduces data processing requirements while enhancing money laundering detection capability through pattern recognition on transformed data.
Data Source
AI summary
Electronic payment card money laundering detection includes receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first API; generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compares the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body.