Financial Transaction Categorization via Merchant Web Scraping
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Solution Overview
Problem
Manual tracking of cash and check transactions is inconvenient and lacks the detailed financial data integration available in automated systems, leading to inefficiencies in financial management.
Innovation Solution
A method and system that uses a financial management application to obtain and categorize financial transactions by accessing merchant websites, matching transaction data with account data, and reallocating amounts to appropriate budget categories, providing detailed transaction information and improved budgeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking of cash and check transactions is used, then simplicity of operation is maintained, but measurement precision and reliability of financial data are insufficient
Solution Approach 1:
The system automatically performs transaction categorization, budget allocation, and financial analysis without requiring manual user intervention. The financial management application self-processes transaction data from multiple sources and generates budget recommendations autonomously, resolving the contradiction by eliminating manual tracking while maintaining high data precision through automated verification and matching processes
Solution Approach 2:
The patent replaces manual mechanical tracking methods with an automated electronic system that uses data matching algorithms, web scraping technologies, and computational processing. This substitution eliminates the need for manual recording while providing superior measurement precision through systematic data validation and cross-referencing across multiple financial sources
2Productivity
If automated financial management systems are used, then productivity and data integration are improved, but device complexity increases
Solution Approach 1:
The financial management application integrates multiple functions including transaction data collection from various financial institutions, web scraping of merchant websites, transaction categorization, budget allocation, and financial analysis into a single unified system. This multi-functionality approach improves productivity by consolidating operations while managing complexity through integrated architecture rather than separate systems
Solution Approach 2:
The system employs an intermediary data processing layer that standardizes and normalizes transaction data from diverse sources before analysis. This intermediary processing layer acts as a buffer between complex data collection operations and simplified user interface, maintaining high productivity through automated data harmonization while presenting a user-friendly interface that masks underlying system complexity
3Reliability
If detailed transaction data is obtained from multiple sources, then measurement precision and reliability are improved, but loss of time increases
Solution Approach 1:
The system performs preliminary data collection and pre-processing of transaction information from financial institutions and merchant websites during off-peak times or in advance. By gathering and preliminarily processing data before it is needed for analysis, the system reduces real-time processing requirements and maintains high data reliability through comprehensive data collection while minimizing time loss through batch processing and background operations
Data Source
AI summary
A method for tracking financial transactions, including: obtaining a group of financial transactions; identifying a first financial transaction of the group of financial transactions involving a payment from a financial account held by a financial institution; allocating an amount of the first financial transaction corresponding to the payment to a budget category, wherein the first financial transaction includes a name of a merchant; accessing a website of the merchant; matching, on the website of the merchant, a list of purchased items to the first financial transaction based on the amount; extracting, after matching, the list of purchased items from the website of the merchant; identifying, after extracting the list of purchased items, a new budget category based on a purchased item of the list of purchased items; and reallocating at least a portion of the amount corresponding to the purchased item to the new budget category.