AI Enumeration Detection for Issuer Fraud Blocking
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Solution Overview
Problem
Existing systems fail to effectively detect and prevent issuer enumeration fraud, which can lead to significant financial losses.
Innovation Solution
Implement an enumeration detection system using machine-learning models to identify patterns in interaction features and discontinue interaction processing when a predetermined threshold is exceeded, thereby preventing fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then system simplicity is maintained, but detection precision and reliability are insufficient to prevent issuer enumeration fraud
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between raw interaction data and fraud detection decisions. This model processes interaction features through multiple layers (embedding layer, convolutional layers, pooling layers, fully connected layers) to extract meaningful patterns, thereby improving detection precision while managing system complexity through modular architecture
Solution Approach 2:
The patent replaces traditional rule-based mechanical fraud detection systems with an artificial neural network-based machine learning system. The neural network automatically learns complex patterns from interaction data without requiring explicit programming of fraud rules, significantly improving detection precision for sophisticated fraud types like issuer enumeration
2Productivity
If manual analysis of interaction data is performed, then system complexity is low, but productivity and speed of fraud detection are insufficient
Solution Approach 1:
The machine learning model performs self-service by automatically learning from interaction data and making fraud detection decisions without human intervention. The model continuously processes interaction features, updates its internal representations, and generates fraud probability scores autonomously, dramatically improving detection speed and productivity
Solution Approach 2:
The system implements continuous processing of interaction data through the machine learning model, which constantly analyzes incoming interaction features and generates real-time fraud assessments. This continuous automated analysis maintains high productivity while the modular architecture manages computational complexity
3Reliability
If interaction processing continues without interruption, then service availability is high, but financial loss from fraud increases
Solution Approach 1:
The system applies preliminary anti-action by proactively identifying and blocking fraudulent interactions before they can cause financial harm. The machine learning model continuously assesses fraud probability and pre-emptively interrupts processing of suspicious interactions, preventing potential losses while maintaining service availability for legitimate users
Solution Approach 2:
The system implements feedback mechanisms where fraud detection results and blocked interactions are fed back into the machine learning model for continuous improvement. This feedback loop enhances reliability by refining the model's ability to distinguish fraudulent from legitimate interactions, reducing false positives that would unnecessarily interrupt service
Data Source
AI summary
A method for discontinuing interaction processing using an enumeration detection system may include receiving data associated with a plurality of interaction instances. The plurality of interaction instances may be associated with an entity. The method may further include extracting one or more interaction features from the data. The method may further include providing the one or more interaction features to a determinative machine-learning model. The determinative machine-learning model may be trained to identify enumeration patterns and output an enumeration score based on the identified enumeration patterns. The method may further include determining that the enumeration score exceeds a predetermined threshold. The method may further include discontinuing interaction processing for the entity based on the enumeration score exceeding the predetermined threshold.