Automated Financial Transaction Classification System
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Solution Overview
Problem
Current systems lack an efficient method to automatically categorize financial transactions to distinguish between individual and small business accounts, as well as to identify the industry of account holders based on transaction history, with existing methods relying on manual analysis and limited data accuracy.
Innovation Solution
A machine learning-based system that analyzes transaction histories to automatically classify accounts as individual or small business, and identifies the industry by utilizing statistical features such as transaction amounts, frequencies, and vendor categories, employing algorithms like decision trees, support vector machines, and neural networks to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to categorize financial transactions, then system complexity is low, but productivity and accuracy are insufficient
Solution Approach 1:
The patent replaces manual mechanical analysis with automated machine learning algorithms including decision trees, support vector machines, and neural networks. These algorithms automatically process transaction data, vendor information, and spending patterns to categorize transactions and identify account types, eliminating the need for manual review while significantly improving productivity and accuracy.
Solution Approach 2:
The system enables self-service categorization where the machine learning model autonomously analyzes transaction histories, vendor categories, and spending behaviors to automatically determine account types (individual vs. small business) and industry classifications without requiring user intervention. The system continuously learns and improves through feedback loops.
2Measurement precision
If limited data is used for classification, then data processing requirements are low, but measurement precision and classification accuracy deteriorate
Solution Approach 1:
The patent segments transaction data into multiple analytical dimensions including vendor categories, spending amounts, transaction frequencies, temporal patterns, and merchant types. By dividing the classification task into these segments, the system can process and analyze each dimension separately using appropriate machine learning algorithms, improving overall classification accuracy while managing data complexity.
Solution Approach 2:
The system transforms transaction data from simple transaction records into multi-dimensional feature spaces by extracting attributes such as spending patterns, vendor categories, temporal distributions, and transaction frequencies. This dimensional transformation enables more accurate classification by capturing complex relationships that cannot be detected in the original data form.
3Productivity
If automated classification systems are implemented, then productivity improves, but reliability and accuracy may deteriorate without continuous learning
Solution Approach 1:
The patent implements feedback mechanisms where user corrections and confirmations of automated classifications are fed back into the machine learning models. This continuous feedback loop allows the system to learn from actual outcomes, adjust its prediction algorithms, and improve accuracy over time while maintaining high automation levels. The system uses this feedback to refine its understanding of transaction patterns and classification criteria.
Data Source
AI summary
Various embodiments of the present disclosure are directed to a system to automatically categorize. Various embodiments can retrieve financial transaction data for the user. Then, various embodiments can identify user classifications using a neural network based at least in part on the financial transaction data. At least some embodiments can then display the user classifications to the user and prompt the user to verify that a user classification of the user classifications describes the user. Various embodiments can then receive feedback from the user, which can be used to train the neural network.