Fraudulent User Identifier Detection via ML Fingerprinting
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Solution Overview
Problem
Software applications face challenges in identifying fraudulent user identifiers due to the computationally expensive process of distinguishing between legitimate and fraudulent users, especially with the rapid generation of fake accounts that can appear valid, leading to inefficient resource allocation and potential malicious activities.
Innovation Solution
The implementation of machine learning models that generate fingerprints for user identifiers, using a classification model to predict legitimacy and a clustering model to identify similar identifiers, allowing for accurate identification and blocking of fraudulent users, thereby optimizing resource allocation and preventing malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of user identifiers is performed to identify fraudulent users, then identification accuracy is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent replaces manual review processes with automated machine learning models that analyze user identifiers. The system uses trained models to generate fraud scores and identify suspicious patterns automatically, eliminating the need for human reviewers while maintaining high identification accuracy and significantly reducing processing time.
Solution Approach 2:
The patent creates digital representations (fingerprints) of user identifiers that can be analyzed and compared without examining the actual identifiers. These fingerprints serve as copies that capture essential characteristics for fraud detection, enabling automated analysis while preserving identification accuracy.
2Productivity
If automated systems are used to process user identifiers at high volume, then processing speed is improved, but identification accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive datasets of fraudulent and legitimate user identifiers before deployment. These pre-trained models are ready to process high volumes of identifiers with high accuracy from the start, eliminating the need for slow manual review while maintaining identification precision.
Solution Approach 2:
The system replaces manual inspection with automated machine learning models that can process identifiers at high speed while maintaining accuracy. The models use learned patterns and features to quickly evaluate each identifier, achieving both high processing speed and sustained identification accuracy.
3Reliability
If resources are allocated to review all user identifiers, then identification thoroughness is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent applies partial action by focusing computational resources only on identifiers that the machine learning model identifies as potentially fraudulent. Instead of reviewing all identifiers equally, the system uses the model to filter and prioritize suspicious cases, maintaining thorough identification while significantly improving resource efficiency by avoiding unnecessary reviews of clearly legitimate identifiers.
Solution Approach 2:
The system replaces comprehensive manual review with targeted automated analysis. Machine learning models efficiently scan all identifiers and automatically focus resources on suspicious cases, achieving thorough identification of fraudulent users while optimizing resource allocation and reducing overall computational energy consumption.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for identifying fraudulent user identifiers in a software application. An example method generally includes generating a vector representation of a user identifier. Using a first machine learning model and the vector representation of the user identifier, a fingerprint representative of the user identifier is generated. Using the first machine learning model and the generated fingerprint, a score is generated. The score generally describes a likelihood that the user identifier corresponds to a fraudulent user identifier. One or more similar user identifiers are identified based on the generated fingerprint and a second machine learning model. One or more actions are taken within a computing system relative to a user associated with the user identifier based on the generated score and the identified one or more similar user identifiers.