3D Transaction Matrix for Fraud Detection Accuracy
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Solution Overview
Problem
Current financial risk management approaches, such as data mining and transaction sample analysis, fail to effectively predict fraudulent transactions due to their reliance on independent data points and lagging analysis methods, which do not account for the dependent nature of customer transactions and the complexity of relationships between them.
Innovation Solution
A 3D matrix is constructed from historical user transactions, which is then used to train a convolutional neural network to predict the risk level of new transactions, allowing for proactive risk assessment and alert transmission based on predicted risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data mining and transaction sample analysis are used to detect financial fraud, then individual transactions can be labeled, but the dependent nature of customer transactions and relationships between them cannot be captured
Solution Approach 1:
The patent transforms traditional flat transaction data into a 3D matrix structure with dimensions representing different transaction attributes and time sequences. This dimensional transformation enables the convolutional neural network to capture spatial relationships and temporal patterns in transaction data, thereby preserving the dependent nature of transactions while improving fraud detection accuracy.
Solution Approach 2:
The patent replaces traditional data mining mechanical methods with a convolutional neural network-based intelligent system. The CNN automatically learns complex patterns and relationships in transaction data through training, substituting manual feature engineering and rule-based analysis with adaptive machine learning that can capture transaction dependencies.
2Reliability
If traditional data mining methods are used for fraud detection, then analysis can be performed on individual transactions, but proactive risk assessment cannot be achieved
Solution Approach 1:
The patent implements proactive risk assessment by training the convolutional neural network on historical transaction data to learn fraud patterns before they occur. The trained model can then predict potential fraud in real-time or near-real-time, enabling preliminary risk identification and prevention actions before fraudulent transactions are completed, thus reducing loss of time in risk detection.
3Measurement precision
If complex transaction relationships are analyzed, then fraud detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent extracts and isolates the core complexity of transaction relationship analysis into a specialized convolutional neural network model. By separating the complex pattern recognition function into a dedicated CNN component trained on 3D transaction matrices, the system manages complexity through modular design while maintaining high fraud detection accuracy.
Data Source
AI summary
An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.