LSTM Neural Network Fraud Detection System
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Solution Overview
Problem
Traditional fraud detection systems during authentication and transactions, particularly those using neural networks based on decision tree forests, struggle to accurately identify anomalies due to complex decision trees that can lead to false positives and undetected fraudulent activities.
Innovation Solution
A machine learning system incorporating a Long Short-Term Memory (LSTM) neural network and a Logistic Regression Classifier, combined with a Jaccard Index matrix for validation, is used to detect fraud by analyzing sequential data and accounting for time differences between transactions, along with feature aggregation and pre-processing systems to enrich and validate transaction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If decision tree forests (Random Forest Classifier) are used for statistical learning, then the system can process large amounts of data, but the decision trees become very complex and generalize the training set badly, leading to undetected fraudulent activities
Solution Approach 1:
The patent segments the fraud detection task into multiple specialized neural networks: LSTM networks for sequential pattern recognition, autoencoders for anomaly detection, and decision tree networks for specific decision rules. Each network type processes the data in a specialized manner, avoiding the complexity issues of single large decision trees while maintaining high detection accuracy through ensemble voting mechanisms.
2Difficulty of detecting and measuring
If complex decision trees are used to analyze data, then the system can capture detailed patterns, but the system accepts fraudulent identification that will not be detected due to poor generalization
Solution Approach 1:
The patent introduces intermediary components including autoencoder networks that learn compressed representations of normal behavior patterns, and a voting mechanism that acts as an intermediary decision layer. The autoencoders encode normal transaction patterns and detect deviations, while the voting mechanism aggregates predictions from multiple specialized networks to make final decisions, improving reliability by preventing any single complex decision tree from dominating the detection process.
3Speed
If traditional neural networks are used for fraud detection, then the system can operate in real-time, but the system fails to identify anomalies due to inadequate sequential data analysis
Solution Approach 1:
The patent employs dynamic LSTM networks that process sequential transaction data in real-time, adapting to changing fraud patterns. The LSTM networks maintain hidden states that capture temporal dependencies across multiple transactions, allowing the system to dynamically adjust to new fraud techniques while maintaining real-time processing speeds. The system dynamically updates its understanding of normal vs. fraudulent behavior through continuous sequential analysis.
Data Source
AI summary
A machine learning system for various computer applications enabling text mining to detect defects or anomalies in an authentication, operation or transaction carried out by the application comprising:A hardware and software arrangement forming a pre-processing system;A hardware and software arrangement forming a neural network leading to an aggregated enriched data processing model,A hardware and software arrangement for the injection of aggregated enriched data into the neural network,A hardware and software arrangement to validate the operation or transaction based on the results obtained at the output of the neural network.