Financial Transaction Classification Using Search Engine Affinity
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Solution Overview
Problem
Current methods for classifying financial transactions are inefficient, as they rely on manual categorization, which is time-consuming and prone to errors, and do not effectively utilize search engines to accurately assign categories based on text descriptions.
Innovation Solution
A method and system that processes text descriptions of financial transactions using a computer processor to generate search terms, selects relevant categories through a search engine, calculates affinity scores, and iteratively refines category assignments based on user feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization is used to classify financial transactions, then flexibility in handling diverse transactions is maintained, but time consumption and error rates increase
Solution Approach 1:
The patent replaces manual mechanical categorization with an automated computer-based system that uses search engines and affinity algorithms to classify financial transactions. The system processes text descriptions, generates search terms, and automatically assigns categories without human intervention, thereby eliminating time consumption while maintaining or improving accuracy through systematic computational methods.
Solution Approach 2:
The classification system performs self-service by automatically processing financial transaction data without requiring manual categorization. The system generates its own search terms from transaction text descriptions, executes searches, calculates affinity scores, and assigns categories autonomously, enabling the system to serve itself in the classification task while improving efficiency.
2Measurement precision
If simple categorization methods are used, then processing speed is maintained, but classification accuracy deteriorates
Solution Approach 1:
The patent introduces search engines as intermediary tools between the text description and category assignment. The search engine acts as a mediator that retrieves relevant information and categories based on generated search terms, enabling more accurate classification without requiring the entire system to become excessively complex. The affinity algorithm serves as another intermediary that objectively measures the relationship between transaction descriptions and categories.
Solution Approach 2:
The classification process is segmented into distinct modular steps: text description processing, search term generation, search execution, affinity score calculation, and category assignment. Each module performs a specific function independently, allowing the system to achieve high accuracy through a coordinated sequence of specialized operations rather than a single complex process.
3Productivity
If automated classification systems are implemented, then productivity increases, but adaptability to new transaction types decreases
Solution Approach 1:
The classification system is designed with universal functionality to handle diverse and new transaction types. The search engine can query any category from the plurality of named categories, and the affinity algorithm can evaluate relationships between text descriptions and any category. This multi-functional design allows the system to adapt to new transaction types and categories without requiring fundamental changes to the core classification mechanism.
Solution Approach 2:
The system incorporates dynamic elements that allow it to adapt to new transaction types. The search term generation process dynamically creates queries based on the specific text description of each transaction, and the category selection dynamically adjusts based on affinity scores calculated from search results. This dynamic approach enables the system to handle novel transaction types while maintaining high productivity through automated processing.
Data Source
AI summary
A method involves classifying a financial transaction of an item. The method steps include receiving a text description of the item, processing the text description to generate a search term, selecting a set of named categories, conducting a first search for the search term and each named category, processing first results of the first search, calculating an affinity score for each named category, identifying a first target affinity score, identifying a target category from the set of named categories, presenting the text description and the target category, receiving feedback that the target category is incorrect, generating a revised set of named categories by removing the target category from the set of named categories, conducting a second search for the search term and each of the revised set of named categories, and presenting a revised target category.