Recurrent Neural Network Subcluster Classification for Financial Behavior Analysis
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Solution Overview
Problem
Existing systems lack the ability to accurately and automatically identify users making poor financial decisions through electronic transactions, such as impulsive purchases or excessive spending, and provide personalized feedback to improve their financial behavior.
Innovation Solution
A neural network, specifically a recurrent neural network (RNN), is trained to classify users into subclusters based on transaction patterns, allowing for the identification of users demonstrating poor financial decisions and generating user-specific feedback by associating them with model users having similar historical behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rule-based systems are used to monitor transaction data, then system complexity is reduced, but the ability to accurately recognize patterns in consumer spending behaviors and provide intelligent feedback is insufficient
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with an artificial neural network (RNN) that automatically learns and recognizes patterns in consumer spending behaviors. The RNN processes sequential transaction data and identifies complex spending patterns without requiring explicit programming of rules, thereby improving measurement precision while managing system complexity through automated learning.
2Adaptability or versatility
If generic feedback is provided to all users, then system complexity is reduced, but the ability to provide personalized and effective financial guidance is lost
Solution Approach 1:
The patent segments users into different clusters based on their spending behavior patterns identified by the RNN. By grouping users with similar behaviors together, the system can provide tailored feedback specific to each cluster's characteristics. This segmentation approach enables personalized guidance while managing complexity through systematic categorization of user groups.
Solution Approach 2:
The patent applies local quality by providing different types of feedback to different user clusters based on their specific spending patterns. Each cluster receives customized feedback designed to address their particular financial behaviors and needs, rather than applying a uniform feedback approach to all users. This enhances adaptability while maintaining manageable system complexity.
3Productivity
If manual analysis of transaction data is performed, then data privacy can be better controlled, but productivity and the ability to process large volumes of sequential data are significantly reduced
Solution Approach 1:
The patent implements self-service by enabling the RNN to automatically process, analyze, and learn from sequential transaction data without requiring manual intervention. The system autonomously identifies spending patterns, clusters users, and generates feedback, thereby dramatically improving productivity and data processing efficiency while operating at a high level of automation.
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for training and using a neural network for subcluster classification. For example, a method may include receiving or generating a plurality of user data sets of users, grouping the plurality of user data sets into one or more clusters of user data sets, grouping each of the one or more clusters into a plurality of subclusters, training the neural network for each of the plurality of subclusters to associate the subcluster with sequential patterns found within the subcluster in order to generate a trained neural network, receiving a first series of transactions of a first user, inputting the first series of transactions into the trained neural network, and classifying the first user into a subcluster of the plurality of subclusters based on the first series of transactions of the first user input into the trained RNN.