Neural Network Fraud Detection via Customer Segmentation
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Solution Overview
Problem
Current fraud detection systems are inefficient in identifying fraudulent transactions, as they primarily rely on blacklisting known fraudulent locations and patterns, failing to effectively differentiate between non-fraudulent and fraudulent transactions based on individual customer behavior.
Innovation Solution
The implementation of neural networks that learn to recognize non-fraudulent transactions by training on specific customer purchase histories, allowing for real-time error analysis to determine transaction legitimacy based on deviation thresholds, thereby identifying potentially fraudulent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fraud detection systems use blacklisting known fraudulent locations and patterns, then they can identify some fraudulent transactions, but they fail to effectively differentiate between non-fraudulent and fraudulent transactions based on individual customer behavior
Solution Approach 1:
The patent segments the fraud detection approach by creating separate fraud detection models for different customer segments. Each model is trained on specific customer behavior patterns, allowing tailored detection for each segment rather than using a single universal blacklist approach. This segmentation enables the system to adapt to individual customer behaviors while maintaining overall fraud detection accuracy.
Solution Approach 2:
The patent applies local quality by customizing fraud detection parameters and thresholds for each customer segment based on their unique behavior patterns. Instead of applying a uniform detection approach, the system adjusts detection criteria locally for each segment, improving the ability to differentiate between legitimate and fraudulent transactions specific to each customer group.
2Productivity
If fraud detection systems use system-wide data for modeling, then they can process transactions efficiently, but they cannot account for individual customer purchasing patterns
Solution Approach 1:
The patent segments the customer base into distinct segments and creates separate fraud detection models for each segment. This allows the system to process transactions efficiently using pre-trained segment-specific models while simultaneously achieving high accuracy by accounting for individual customer purchasing patterns through segment-specific behavior analysis.
Solution Approach 2:
The patent applies preliminary action by pre-training fraud detection models on historical data for each customer segment before actual transaction processing occurs. This preliminary training enables the system to quickly assess individual transactions against segment-specific patterns, maintaining processing efficiency while improving accuracy through pre-established customer behavior benchmarks.
3Reliability
If current fraud detection models are trained on all customer data, then they can identify general fraud patterns, but they generate high false positives and false negatives due to lack of individualization
Solution Approach 1:
The patent segments customers into distinct segments and trains separate fraud detection models for each segment. This segmentation improves reliability by ensuring each model is optimized for its specific segment's behavior patterns, while simultaneously improving measurement precision by reducing false positives and false negatives through individualized detection criteria for each segment.
Solution Approach 2:
The patent applies parameter changes by adjusting detection thresholds and parameters for each customer segment based on their unique behavior characteristics. This allows the system to maintain high reliability across different segments while improving measurement precision by customizing parameters to match each segment's legitimate transaction patterns, thereby reducing misclassification.
Data Source
AI summary
Logic may detect fraudulent transactions. Logic may determine, by a neural network based on the data about a transaction, a deviation of the transaction from a range of purchases predicted for the customer, wherein the neural network is pretrained to predict purchases by the customer based on a purchase history of the customer. Logic may compare the deviation of the transaction from purchases predicted by the customer against a deviation threshold to determine whether the transaction is within the range of purchases predicted by the neural network. Logic may generate a notification in response to a determination that the deviation of the transaction from the range of purchases predicted exceeds a deviation threshold, the notification to identify the transaction as a potentially fraudulent transaction. Logic may train the neural network based on the transaction in response to a determination that transaction is not a fraudulent transaction.