Recurring Income Detection via Transaction Parameter Matching
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Solution Overview
Problem
Current financial management systems are inadequate in detecting and categorizing recurring income sources, as they often lack detailed information for deposit transactions, requiring users to manually identify income transactions repeatedly, which is burdensome and prone to errors, especially for individuals with multiple income sources.
Innovation Solution
A method and system that analyze financial transaction data to define income identification parameters such as amount and date ranges, allowing users to identify and categorize deposit transactions as income, and automatically categorize similar transactions as recurring income, reducing the need for repeated manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of income transactions is required for each deposit, then accuracy of income categorization is improved, but user burden and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of deposit transactions by examining amount patterns, frequency, and temporal characteristics before user review. This preliminary categorization reduces the burden on users while maintaining accuracy through subsequent verification steps.
Solution Approach 2:
The system implements feedback mechanisms where user corrections to automated categorizations are used to refine and improve future automated identification accuracy. This creates a learning loop that reduces user burden over time while maintaining or improving categorization precision.
2Loss of information
If detailed information is collected for all deposit transactions, then ability to identify recurring income is improved, but data processing complexity increases
Solution Approach 1:
The system segments deposit transactions into different categories based on identifiable characteristics such as amount ranges, frequency patterns, and source indicators. This segmentation allows the system to apply different analysis methods to different transaction types, reducing overall processing complexity while maintaining comprehensive information capture.
Solution Approach 2:
The system transforms raw deposit transaction data into standardized parameters such as amount bins, frequency categories, and temporal patterns. This parameter transformation simplifies the data structure and makes it more amenable to automated analysis while preserving the essential information needed to identify recurring income.
3Ease of operation
If automated categorization of deposit transactions is implemented, then user burden is reduced, but accuracy of income identification may deteriorate
Solution Approach 1:
The system performs preliminary automated categorization of deposit transactions based on amount patterns, frequency, and other identifiable characteristics. This preliminary action reduces user burden by handling routine categorizations automatically while maintaining the option for user review and correction to ensure accuracy.
Solution Approach 2:
The system enables self-service automated categorization where deposits are automatically identified and categorized based on learned patterns from user feedback and historical data. This self-service capability significantly reduces user burden while maintaining accuracy through continuous learning and adaptation.
Data Source
AI summary
A method and system for detecting and categorizing recurring income whereby financial transaction data associated with a given user is obtained from one or more sources. One or more income identification parameters are defined. The given user identifies one or more deposit transactions included in the financial transaction data as an income transaction. The one or more income identification parameter values associated with one or more unidentified deposit transactions are then compared with the income identification parameter values associated with the user identified income transactions. Any of one or more unidentified deposit transactions having income identification parameter values the same as, or sufficiently similar to, the income identification parameter values associated with the user identified income transactions are then categorized as identified income transactions of the same type, and/or as being from the same given payor/income source, as the user identified income transactions.