AI-Driven Transaction Classification System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for categorizing business transactions for tax analysis rely heavily on rules-based processing, which can become inaccurate over time due to lack of adaptability to changing tax laws and company structures, and are limited by pre-defined categorizations.
Innovation Solution
The use of an AI model trained on transactional data, which can adapt to changes in tax laws and company structures, and can automatically recognize updates in tax laws by performing internet searches, to categorize transactions accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rules-based processing is used for transaction categorization, then the system is simple to implement and operate, but the accuracy deteriorates over time due to lack of adaptability to changing tax laws and company structures
Solution Approach 1:
The patent implements a hybrid classification system that dynamically combines rules-based processing with machine learning models. The system adapts its classification approach based on the specific transaction type and context, allowing it to maintain simplicity for straightforward cases while achieving high accuracy for complex or changing scenarios through adaptive AI intervention.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the rules-based system and the final classification output. This intermediary layer processes transactions that are ambiguous or don't fit predefined rules, thereby maintaining the simplicity of rules-based operation while improving overall classification accuracy through intelligent intervention.
2Device complexity
If pre-defined categorizations are used, then the device complexity is reduced, but the adaptability to changing tax laws and company structures deteriorates
Solution Approach 1:
The system maintains a static rules-based framework for simplicity but introduces dynamic machine learning models that continuously adapt to changing tax laws and company structures. This allows the system to keep low complexity in its core architecture while gaining adaptability through the adaptive AI component.
Solution Approach 2:
The patent changes the parameters of the classification system by introducing machine learning models that can automatically adjust classification parameters based on learned patterns from historical data and changing regulations, thereby maintaining simple pre-defined categorizations while achieving adaptability through parameter optimization by AI.
3Measurement precision
If manual classification is used, then the accuracy is high, but the productivity and analysis time for large datasets deteriorates
Solution Approach 1:
The patent segments the classification process into two parts: automated machine learning classification for the majority of transactions to maintain high productivity, and manual review only for edge cases or low-confidence classifications. This segmentation preserves high accuracy through selective manual intervention while maintaining fast processing for bulk data.
Solution Approach 2:
The machine learning model performs self-service classification for the majority of transactions without human intervention, achieving high productivity. The system only escalates to manual classification when the AI model's confidence is below a threshold, thereby maintaining accuracy through targeted human review while preserving automated processing speed.
4Ease of manufacture
If rules-based processing is used, then the system is easy to implement, but the reliability deteriorates due to inability to adapt to changing conditions
Solution Approach 1:
The patent introduces a machine learning intermediary that sits between the simple rules-based system and the classification output. This intermediary maintains the ease of implementation of rules-based processing while improving reliability by handling edge cases and adapting to changing conditions through learned patterns from historical data.
Solution Approach 2:
The system maintains fixed rules for ease of implementation but allows the machine learning component to dynamically change classification parameters based on learned patterns and changing tax laws, thereby preserving implementation simplicity while achieving reliability through adaptive parameter optimization.
Data Source
AI summary
Systems and methods disclosed herein provide for classification of business transactions. A client device runs an application that receives identification of a dataset for use in a classification analysis, where the dataset including a plurality of transactions. The application then receives user inputs to create a new analysis and runs a classification analysis of the transactions using one or more of manual classification, rules-based classification, and a machine learning model.