Neural Network Fraud Detection Reducing False Positives
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Solution Overview
Problem
Current systems often incorrectly flag legitimate transactions as fraudulent, leading to false positives and user frustration, as they are overly cautious in preventing fraudulent transactions.
Innovation Solution
An Artificial Neural Network (ANN) system that analyzes transaction data, demographic information, and user profiles to generate a false positive score, distinguishing between legitimate and fraudulent transactions by considering factors like merchant type, transaction timing, and user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based systems are used to prevent fraudulent transactions, then fraud detection capability is improved, but false positive rate increases causing user frustration
Solution Approach 1:
The patent introduces an artificial neural network as an intermediary layer between the transaction system and the rules engine. The neural network analyzes transaction patterns and user behavior to generate a fraud risk score, which then modulates the application of rules. This intermediary learns from historical data to distinguish between legitimate and fraudulent transactions, reducing false positives while maintaining fraud detection effectiveness.
Solution Approach 2:
The system dynamically changes the parameters of rule application based on neural network output. Instead of applying fixed rules, the system adjusts rule sensitivity and thresholds based on the learned fraud risk score. This allows the system to be more lenient with low-risk transactions and more stringent with high-risk transactions, optimizing both fraud detection and user experience.
2Reliability
If overly cautious rules are implemented to detect fraud, then security is improved, but transaction approval rate decreases
Solution Approach 1:
The system applies partial rule enforcement based on risk levels. Instead of applying all security rules uniformly to every transaction, the neural network identifies transactions that require full scrutiny versus those that can proceed with minimal checks. This partial action approach maintains security for suspicious transactions while allowing legitimate transactions to flow through with fewer obstacles.
Solution Approach 2:
The system transitions from static, fixed rules to dynamic, adaptive rule application. The neural network continuously learns from new data and adjusts its fraud risk assessments in real-time. This dynamic approach allows the system to adapt its security posture to current threat landscapes and individual user behaviors, optimizing the balance between security and transaction flow.
3Ease of manufacture
If traditional rules engines are used for transaction authorization, then implementation simplicity is maintained, but false positive identification capability deteriorates
Solution Approach 1:
The patent merges the simplicity of rules-based systems with the intelligence of machine learning. The neural network is integrated with the existing rules engine, combining the interpretability and ease of implementation of rules with the pattern recognition and accuracy of learned models. This hybrid approach maintains implementation simplicity while dramatically improving false positive identification capability.
Solution Approach 2:
The system replaces purely mechanical, deterministic rule evaluation with a learning-based system that can identify complex patterns. The neural network substitutes for simple threshold-based rules in identifying false positives, using learned representations of normal and fraudulent behavior patterns. This substitution maintains the overall system structure while enhancing detection precision.
Data Source
AI summary
Disclosed herein are systems and methods that identify and improve upon false positive scores. Some embodiments may include artificial neural network learning methods that utilize data input from users as well as enterprise machines. Information, such as transaction timing, prior transaction data, and demographics, may be taken as inputs to the neural network processing. The processing may be done on one or more neural network computers or nodes.