Multi-Dimensional Account Coding for Overlapping Change Detection
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Solution Overview
Problem
Existing data structures struggle to efficiently analyze and compare changes to user and entity attributes over time, requiring substantial computational resources and failing to identify patterns indicative of fraudulent activities or market opportunities.
Innovation Solution
A multi-dimensional coded representation system that uses time, characteristic, and category dimensions to efficiently present and analyze changes in user and entity attributes, enabling rapid risk assessment and pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data structures are used to store and analyze user attributes, then data can be stored in a structured format, but analyzing changes over time requires substantial computational resources and time
Solution Approach 1:
The patent segments the analysis process by creating separate change detection mechanisms for different attribute dimensions (geolocation, device information, transaction behavior). Each dimension is monitored independently through specific data structures that track only changes rather than storing complete historical states, reducing computational overhead while maintaining detection precision.
Solution Approach 2:
The patent introduces a temporal dimension to traditional data structures by implementing versioned records and change logs that track attribute evolution over time. This allows the system to analyze changes efficiently by comparing temporal snapshots rather than processing entire datasets, transforming the problem from analyzing all data to analyzing only dimensional differences.
2Loss of information
If complete user data is stored for analysis, then comprehensive analysis is possible, but large data volumes require substantial computational resources to process
Solution Approach 1:
The patent extracts only the essential change information from complete user data by implementing delta storage mechanisms. Instead of storing entire user profiles and their complete histories, the system extracts and stores only the differences (changes) in attributes, maintaining information completeness for fraud detection while dramatically reducing data volume requiring computational processing.
Solution Approach 2:
The patent performs preliminary change detection and filtering before full analysis by implementing real-time monitoring of attribute changes. This preliminary action identifies only those changes that require detailed analysis, pre-processing the data to separate significant changes from routine updates, thereby reducing the volume of data that needs comprehensive computational processing.
3Reliability
If detailed attribute tracking is implemented, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies local quality by implementing different levels of tracking detail for different attribute types based on their fraud detection importance. Critical attributes like geolocation changes and device information receive detailed tracking with multiple data points, while less critical attributes use simpler tracking mechanisms. This differentiated approach maintains fraud detection accuracy for high-risk indicators while reducing overall system complexity.
Data Source
AI summary
Methods and systems are presented for providing a framework that enables a computer system to analyze and compare changes to different account characteristics of different accounts that occurred over a time period. A code is generated for an account to represent changes to different account characteristics of the account within the time period. Changes to different account characteristics may be highlighted in the code using different colors or patterns. By analyzing the code, overlapping changes from different account characteristics that occurred within the same time frame may be detected. The different change patterns associated with the user account may then be used to assess a risk for the user account and/or a transaction involving the user account.