Phishing Data Clustering for Fraud Investigation Efficiency
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Solution Overview
Problem
Current systems for fraud investigation are inefficient due to the need for manual repetition of data searches across large datasets, leading to time-consuming and resource-intensive processes, and struggle to prioritize investigations effectively due to insufficient information from individual data items.
Innovation Solution
A data analysis system that automatically generates memory-efficient clustered data structures, analyzes them, and provides human-readable summaries, allowing analysts to efficiently evaluate and prioritize clusters based on automated scoring and interactive user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data search and analysis is performed across large datasets, then analysts can examine individual data items in detail, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent segments the large dataset into multiple clusters based on shared characteristics and relationships. Each cluster represents a grouped subset of data items that share common features, allowing analysts to work with smaller, more manageable units rather than examining every individual data item across the entire dataset.
Solution Approach 2:
The system performs preliminary automated analysis to generate clusters before the analyst begins their investigation. This pre-processing step organizes data items into meaningful groups based on their relationships and characteristics, so that when the analyst reviews the data, the most relevant items are already grouped and prioritized, significantly reducing the time needed to identify suspicious patterns.
2Loss of information
If analysts manually review large collections of data items, then they can identify relevant information, but processing efficiency decreases and memory resources are consumed
Solution Approach 1:
The patent merges multiple related data items into unified clusters based on their shared characteristics and relationships. By combining data items that are related through common features, the system presents a consolidated view that maintains information completeness while reducing the total number of individual items the analyst must review, thereby improving processing efficiency.
Solution Approach 2:
The clustering mechanism serves multiple functions simultaneously: it organizes data by relationships, prioritizes suspicious items, reduces data volume for review, and maintains comprehensive information coverage. This multi-functional approach allows the system to improve productivity without sacrificing information completeness.
3Measurement precision
If individual data items are analyzed in isolation, then detailed examination is possible, but the ability to prioritize investigations is insufficient
Solution Approach 1:
The patent implements a nested structure where individual data items are contained within clusters, which themselves can be part of larger groupings. This hierarchical organization allows analysts to examine individual items in detail when needed while simultaneously understanding their context within broader patterns, enabling effective prioritization at multiple levels of abstraction.
Solution Approach 2:
The cluster acts as an intermediary between individual data items and the analyst's prioritization decisions. By grouping related items together and presenting them as a unified unit with aggregated characteristics, the cluster provides the contextual information needed for prioritization while still allowing detailed examination of individual items when the analyst chooses to drill down.
Data Source
AI summary
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.