Automated Money Laundering Detection via Entity Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Financial analysts face challenges in efficiently identifying and prioritizing potential money laundering activities due to insufficient information from individual data items, leading to time-consuming and resource-intensive manual searches, and difficulties in distinguishing between similar entities involved in money laundering.
Innovation Solution
A data analysis system that automatically detects money laundering by analyzing relationships and patterns among entities such as IP addresses, computing devices, and financial accounts, using money laundering indicators and scoring criteria to determine the likelihood of malfeasance, thereby facilitating efficient identification and prioritization of investigations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analysts manually search and analyze individual data items to identify money laundering activities, then they can examine detailed information, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary automated analysis of data items against money laundering indicators before analyst review, pre-identifying suspicious patterns and relationships. This preliminary processing filters out benign cases and prepares prioritized lists, allowing analysts to focus their detailed examination only on high-risk cases that require human judgment.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw data and analyst decision-making. This intermediary process automatically compares data items against established money laundering indicators, scores suspiciousness levels, and generates prioritized investigation lists, thereby reducing the time analysts spend on initial data screening while maintaining analytical accuracy.
2Ease of operation
If analysts examine individual entity data items in isolation, then they can focus on specific characteristics, but they lack contextual information from related entities
Solution Approach 1:
The system merges information from multiple related data items by automatically identifying and grouping entities that share common characteristics or relationships (such as multiple financial accounts accessed from the same IP address or device). This consolidation creates a comprehensive view that preserves contextual information while presenting it in an organized manner that maintains analytical simplicity.
Solution Approach 2:
The patent segments the analysis process into distinct layers: individual entity evaluation, relationship identification, and contextual pattern recognition. By breaking down the complex analysis into manageable segments that build upon each other, the system maintains ease of operation at each stage while progressively incorporating more contextual information from related entities.
3Reliability
If analysts manually prioritize investigations based on entity characteristics, then they can use professional judgment, but similar entities become difficult to distinguish and prioritize accurately
Solution Approach 1:
The system transforms the prioritization task by introducing quantitative scoring parameters based on money laundering indicators. Instead of relying solely on qualitative professional judgment, the system calculates numerical scores that reflect the degree of suspiciousness for each entity, making it easier to distinguish between similar cases while maintaining reliability through objective measurement criteria.
Solution Approach 2:
The patent replaces the manual mechanical process of comparing and prioritizing entities with an automated computational system. This substitution uses algorithms to systematically evaluate entities against multiple indicators, calculate scores, and generate prioritized lists, thereby reducing analysis complexity while improving prioritization accuracy through consistent application of evaluation criteria.
Data Source
AI summary
In various embodiments, systems, methods, and techniques are disclosed for analyzing various entity data items including users, computing devices, and IP addresses, to detect malfeasance. The data and/or database items may be automatically analyzed to detect malfeasance, such as criminal activity to disguise the origins of illegal activities. Various money laundering indicators or rules may be applied to the entity data items to determine a likelihood that money laundering is occurring. Further, the system may determine one or more scores (and/or metascores) for each entity data item that may be indicative of a likelihood that it is involved in money laundering. Scores/metascores may be determined based on, for example, various money laundering scoring criteria and/or strategies. Account entities may be ranked based on their associated scores/metascores. Various embodiments may enable an analyst to discover various insights related to money laundering.