Transaction Categorization Using Location Codes and Business Databases
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Solution Overview
Problem
Conventional financial analysis software lacks accurate automated categorization of transaction data, leading to cumbersome data entry and low automation efficiency, as users often struggle to assign correct categories due to incomplete or unclear transaction information.
Innovation Solution
A system and method that identifies state and city codes in transaction data to narrow the search space, uses business listings databases to match transaction data with business names, and assigns category confidence scores based on matching criteria to automate transaction categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional algorithms are used to categorize transactions, then automation is provided, but categorization accuracy is low
Solution Approach 1:
The patent segments the categorization process into multiple independent modules: transaction data parsing, business name extraction, database matching, and category assignment. Each module handles a specific aspect of the categorization task, improving both automation extent and accuracy by breaking down the complex problem into manageable components that can be optimized independently.
Solution Approach 2:
The patent introduces an intermediary database containing business names, addresses, and categories that acts as a mediator between transaction data and category assignments. This intermediary layer enables more accurate matching by providing additional context (business details, locations) that bridge the gap between raw transaction data and meaningful categorization, thereby improving accuracy while maintaining automation.
2Measurement precision
If manual categorization is performed by users, then categorization accuracy can be improved, but time consumption increases
Solution Approach 1:
The system enables self-service categorization by automatically processing transaction data through multiple algorithms and databases to generate category recommendations without requiring user intervention. The system serves itself by extracting business names, matching against databases, and assigning categories autonomously, thereby maintaining high accuracy while eliminating the time users would otherwise spend on manual categorization.
Solution Approach 2:
The patent performs preliminary actions by pre-processing transaction data to extract relevant information (business names, amounts, dates) before the actual categorization step. It also pre-loads and structures the business database in advance, so that when categorization is needed, the system can quickly match and assign categories without requiring users to manually input or search for information, thus reducing time loss while maintaining accuracy.
3Reliability
If the search space for categorization is not narrowed, then all possible categories are considered, but processing efficiency decreases
Solution Approach 1:
The patent applies local quality by narrowing the search space to locally relevant categories based on the specific transaction context. Instead of considering all possible categories uniformly, the system identifies the most relevant category subset for each transaction based on extracted business information, location data, and transaction characteristics. This allows the system to maintain reliability by considering appropriate categories while improving productivity by excluding irrelevant ones from the search process.
Solution Approach 2:
The system performs partial action by not exhaustively searching through all possible categories for every transaction. Instead, it uses heuristics and contextual information to perform a focused search on the most likely category subset. This partial search approach maintains sufficient reliability for practical purposes while dramatically improving processing speed by avoiding unnecessary comparisons with irrelevant categories.
Data Source
AI summary
A system and method employs a categorized list of business names to identify a category corresponding to transaction information, such as credit card transaction data.