Interactive Transaction Categorization With ML Encoder Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional processes for categorizing transaction data are time-consuming, prone to errors, and lead to inaccurate financial records due to limited context and human intervention, resulting in potential compliance issues and backlogs in financial reporting.
Innovation Solution
A computing server uses 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 by delegating annotation tasks to users through interactive categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual categorization processes are used, then human expertise can be applied to categorize transactions, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The system enables users to self-categorize transactions through automated notifications and interactive prompts. When a transaction is detected, the system automatically notifies the user and requests categorization input, allowing the user to complete the task at their convenience without requiring dedicated processing time from the system.
Solution Approach 2:
The system performs preliminary data collection and transaction detection before the actual categorization step. By continuously monitoring and pre-processing transaction data, the system prepares categorized information in advance, reducing the time required when actual categorization decisions are needed.
2Productivity
If automated machine learning models are used to categorize transactions, then processing speed increases, but accuracy may decrease due to lack of contextual understanding
Solution Approach 1:
The system introduces an interactive notification mechanism as an intermediary between automated transaction detection and final categorization. The ML model identifies transactions and generates notifications, but the user serves as the final decision-maker by providing categorization input in response to notifications, combining automated speed with human accuracy.
Solution Approach 2:
The system implements a feedback loop where user categorization inputs are continuously fed back into the system. This feedback allows the ML model to learn from user corrections and improve its categorization accuracy over time, while maintaining high processing speeds through automated initial classification.
3Reliability
If continuous monitoring and real-time user interaction are implemented, then categorization accuracy improves, but network bandwidth and server resources increase
Solution Approach 1:
Instead of continuous real-time communication, the system uses periodic notifications triggered by specific events (new transactions). The server sends notifications only when relevant categorization tasks are available, and users respond at discrete intervals, dramatically reducing network bandwidth consumption compared to continuous monitoring.
Solution Approach 2:
The system extracts and isolates only the essential communication elements needed for categorization - transaction notifications and user responses. By removing unnecessary continuous data transmission and keeping only critical interaction points, the system maintains accuracy while minimizing network resource usage.
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.