Financial Transaction Categorization Using Peer Data Segmentation
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Solution Overview
Problem
Current financial management systems often inaccurately categorize financial transactions due to lack of user-specific data consideration, leading to increased user time for corrections and potential errors.
Innovation Solution
A system and method for automatic categorization of financial transactions that aggregates and categorizes user data based on shared attributes with contributing consumers, using their financial data and demographics to accurately categorize similar transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic categorization is used based on general user data, then categorization speed is improved, but categorization accuracy deteriorates
Solution Approach 1:
The patent applies local quality by transitioning from general user data to user-specific segmented data. The system identifies and segments users into specific groups (e.g., professionals, students, retirees) and uses categorization data from users within the same segment. This ensures that the categorization method is locally optimized for each user group's characteristics, improving accuracy while maintaining automation.
Solution Approach 2:
The patent segments the general user base into distinct user groups based on shared attributes. Instead of using a single general model for all users, the system divides users into segments (such as professionals, students, retirees) and applies categorization based on peer data within each segment. This segmentation resolves the contradiction by enabling accurate, targeted categorization for each group while maintaining overall system automation.
2Measurement precision
If more user-specific data is collected and processed, then categorization accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional system that handles multiple user segments and categorization scenarios through a single unified framework. The system can process different user types (professionals, students, retirees, etc.) and adapt to various categorization needs using the same core architecture, avoiding the need for separate complex systems for each user group.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting categorization parameters based on user segment characteristics. The system modifies categorization thresholds, data sources, and matching criteria according to the specific user group being processed. This allows accurate categorization for each segment without requiring fundamentally different system structures, thereby managing complexity.
3Measurement precision
If manual user input is required for categorization, then categorization accuracy is improved, but user time and effort increase
Solution Approach 1:
The patent applies self-service by enabling the system to automatically perform categorization using data from peer users within the same segment. The system serves itself by leveraging collective user data to make accurate categorization decisions without requiring manual user input. Users benefit from accurate categorization while the system handles the processing automatically, eliminating the need for user time and effort.
Solution Approach 2:
The patent uses feedback mechanisms where user corrections or confirmations of automated categorizations are fed back into the system to refine future categorization. This feedback loop allows the system to learn from user interactions and improve accuracy over time, reducing the need for manual corrections while maintaining high precision.
Data Source
AI summary
Financial data associated with one or more “contributing consumers” is obtained from one or more sources and categorized and associated with a specific expense/income category. One or more attributes associated with the contributing consumers are then identified and used to analyze, aggregate, and categorize the financial data according to the attributes. Data representing a user financial transaction is then obtained for categorization and one or more specific user attributes associated with the user are identified. The user financial transaction is then categorized based, at least in part, on the categorization of similar contributing consumer financial transactions by contributing consumers having at least one of the specific user attributes.