Transaction Sorting via Merchant Account Frequency Table
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Solution Overview
Problem
Current electronic transaction management systems lack automation for the initial sorting of transactions to accounts, relying heavily on manual efforts by accounting staff, which is burdensome, costly, and prone to human error, especially during the implementation period.
Innovation Solution
A system and method that infers account names for transaction sorting by analyzing data from multiple users' account sorting decisions, generating a merchant account frequency table to determine the likelihood of account associations for new transactions, providing automatic assistance in sorting transactions to the most probable account.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting of transactions to accounts is performed, then sorting accuracy can be maintained, but the workload and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing sorting decisions from multiple users to build a frequency table before new transactions need sorting. This pre-computed knowledge base enables automated sorting recommendations, reducing both time consumption and manual workload while maintaining accuracy through crowd-sourced validation.
Solution Approach 2:
The patent introduces an intermediary mechanism - the merchant account frequency table - that mediates between raw transaction data and sorting decisions. This table serves as a knowledge base that translates multiple users' sorting decisions into automated recommendations, reducing direct manual intervention while preserving sorting accuracy.
2Productivity
If automation is implemented for transaction sorting, then productivity increases, but the system complexity increases
Solution Approach 1:
The system creates a simplified copy of the sorting knowledge in the form of a frequency table that records account associations with merchants. This copy enables automated sorting without requiring complex real-time analysis, thereby increasing productivity while keeping the system relatively simple by storing pre-computed associations rather than implementing complex sorting algorithms.
Solution Approach 2:
The automation is prepared in advance by building the frequency table from historical sorting decisions before it is needed for actual transaction sorting. This preliminary computation simplifies the runtime system, as the complex analysis work is done beforehand, enabling fast automated sorting without high system complexity during operation.
3Manufacturing precision
If historical transactions are sorted manually during implementation, then accurate categorization is achieved, but the implementation burden increases
Solution Approach 1:
The system incorporates feedback from multiple users' sorting decisions into the frequency table. This collective feedback mechanism ensures that the automated recommendations are based on validated, accurate categorizations from experienced users, maintaining high categorization accuracy while reducing the implementation burden through automation.
Solution Approach 2:
The frequency table acts as an intermediary that captures the expertise and accuracy of manual sorting without requiring direct manual intervention during implementation. It translates the collective knowledge of multiple users into automated recommendations, achieving accurate categorization while improving ease of operation.
4Measurement precision
If account sorting decisions from multiple users are analyzed, then sorting accuracy improves, but data processing requirements increase
Solution Approach 1:
The system performs data processing in advance by analyzing multiple users' sorting decisions and building the frequency table before actual transaction sorting is needed. This preliminary processing consolidates data analysis work into a one-time setup phase, reducing ongoing data processing requirements while maintaining high account association accuracy through comprehensive user feedback analysis.
Data Source
AI summary
A system and method for use with a data management service provides automatic assistance of transaction data sorting to an account based on account name inferences. A table is generated from transactions that have been previously sorted to accounts, in which the table contains occurrence frequencies of associations between merchants and accounts. The occurrence frequencies of the table are utilized to analyze an unsorted transaction. The merchant associated with the unsorted transaction is matched to a merchant of the table. The occurrence frequencies of the accounts of the table associated with the matched merchant are determined. The accounts of the table are matched to accounts of the user's chart of accounts. A determined likelihood account is calculated for the unsorted transaction for account sorting.