Transaction Categorization via Co-Location Analysis
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Solution Overview
Problem
Financial software applications face challenges in accurately categorizing transactions with ambiguous information, leading to inaccurate commitment to data stores and increased user effort in reorganization, especially when dealing with smaller merchants or incomplete transaction data.
Innovation Solution
An application agent scans multiple data stores for previously committed transactions of a similar type, determines co-located transactions, ranks them based on co-location scores, and writes the incoming transaction to the data table with the highest ranking, ensuring accurate categorization and reducing the need for user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If transactions with ambiguous information are committed to a generic 'unorganized' account, then the system maintains simplicity and availability, but transaction categorization accuracy deteriorates and user effort for reorganization increases
Solution Approach 1:
The system performs preliminary actions by scanning multiple data stores and analyzing co-located transactions before committing an ambiguous transaction. It proactively determines the most appropriate data table by examining historical transaction patterns and merchant relationships, rather than defaulting to a generic account. This preliminary analysis enables accurate categorization while maintaining system availability.
Solution Approach 2:
The system introduces an intermediary analysis layer that examines co-located transactions and merchant relationships to bridge the gap between ambiguous transaction data and appropriate categorization. This intermediary process analyzes the transaction context, identifies similar merchants, and determines the most likely correct categorization before commitment, improving accuracy without requiring direct user intervention.
2Measurement precision
If the system scans multiple data stores and analyzes co-located transactions to determine accurate categorization, then transaction categorization accuracy improves, but processing time and system complexity increase
Solution Approach 1:
The system applies partial action by focusing its analysis on specific, relevant factors rather than examining all possible transaction attributes. It prioritizes analyzing co-located transactions in the same data tables and merchant relationship patterns, which provide the most discriminative power for categorization. This selective approach achieves high accuracy without requiring exhaustive analysis of all transaction dimensions.
Solution Approach 2:
The system uses copying by examining patterns from previously committed transactions in the same data tables. It copies the categorization logic established by historical transaction co-location patterns, using these patterns as templates to determine the appropriate destination for new ambiguous transactions. This approach leverages existing data structures and patterns to accelerate decision-making.
3Measurement precision
If the system uses co-location analysis of similar transactions to determine categorization, then categorization accuracy for ambiguous transactions improves, but device complexity and computational resources increase
Solution Approach 1:
The system applies universality by using the same co-location analysis mechanism for multiple purposes: determining data table categorization, identifying merchant relationships, and establishing transaction patterns. This single analytical approach serves multiple categorization needs across different data stores and transaction types, reducing the need for separate specialized systems for each categorization task.
Solution Approach 2:
The system performs self-service by autonomously analyzing its own historical transaction data and co-location patterns to determine categorization rules. It uses its existing data store infrastructure and transaction records to generate categorization decisions without requiring external classification systems or manual rule configuration. The system serves its own categorization needs using its existing resources and data structures.
Data Source
AI summary
Embodiments disclosed herein relate to a method of writing a transaction to a data store having one or more data tables. An application agent receives transaction data for a first transaction of a first type. Application agent attempts to write the first transaction of the first type to a data table in the data store, provided that a transaction of the first type has been committed to the data store. Application agent scans a plurality of additional data stores for previously committed transactions of the first type Application agent determines additional transactions of another type that are co-located in a data table. Application agent ranks the additional transactions of the other type. Application agent identifies a data table in the data store to which a top ranking additional transaction of the other type has been committed. Application agent writes the first transaction to identified data table in the data store.