Transaction Category Assignment Using ML Encoder and User Feedback
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Solution Overview
Problem
Traditional methods for categorizing transaction data are time-consuming, prone to errors, and lead to inaccurate financial records due to limited context and human intervention, often resulting in compliance issues and delayed detection of errors.
Innovation Solution
A computing server employs a machine-learned encoder model to determine candidate categories for transaction data, receives user feedback, and generates category assignments, reducing processing time and resource consumption while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual categorization methods are used, then human expertise can be applied to complex transactions, but processing time increases and productivity decreases
Solution Approach 1:
The system enables self-service categorization by automatically processing transactions through machine learning models. The automated system extracts data, determines categories, and assigns them without requiring manual intervention for routine transactions, thereby maintaining accuracy while significantly improving processing speed.
Solution Approach 2:
The patent replaces manual mechanical categorization processes with automated machine learning models and natural language processing systems. The machine learning model analyzes transaction data and automatically assigns categories, substituting human expertise with computational algorithms that operate continuously without fatigue.
2Productivity
If automated machine learning models are applied, then processing speed increases, but the system becomes more complex
Solution Approach 1:
The system segments the categorization process into distinct modules: data extraction module, machine learning classification module, and validation module. This segmentation allows each component to be developed, tested, and maintained independently, reducing overall system complexity while maintaining high processing speed through automation.
3Reliability
If manual review of all transactions is performed, then accuracy can be verified, but time consumption increases and errors in detection increase
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from corrected categorizations. When users provide feedback on incorrect automated assignments, the system updates its models to improve future predictions. This feedback loop maintains high reliability while reducing the time needed for manual review by focusing only on edge cases and potential errors.
4Measurement precision
If comprehensive data parsing is performed to ensure accuracy, then categorization precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring data before it enters the machine learning classification pipeline. Data is normalized, validated, and organized in advance, which ensures high extraction accuracy while reducing the processing time required during actual categorization operations.
Data Source
AI summary
A computing server receives, through a message communication channel from a user, a documentation of a transaction. The computing server parses data in the documentation to create a data instance representing the transaction in a database. The computing server determines that the data instance needs an assignment of a category from a list of custom-defined categories. The computing server applies a machine-learned encoder model to features of the data instance to determine candidate categories from the list of custom-defined categories. The computing server transmits a response message to the user through the message communication channel. The response message includes at least one of the candidate categories determined by the machine learning model. Responsive to transmitting the response message to the user, the computing server receives feedback from the user. The computing server generates a category assignment for the data instance based on the user feedback.