Subscriber-Specific ML Ensemble for Digital Threat Detection
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Solution Overview
Problem
Current technologies fail to accurately detect digital fraud and abuse in real-time and lack the ability to evolve and adapt to new threats, leading to insufficient protection for service providers and users.
Innovation Solution
A machine learning-based system that constructs a subscriber-specific ensemble of distinct models to identify digital threats, trained with subscriber-specific data, and calibrated to generate precise threat scores, replacing global models for enhanced detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global machine learning model is used for all subscribers, then device complexity is reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent segments the single global machine learning model into multiple subscriber-specific models, where each model is trained on data from a specific subscriber. This segmentation allows each model to specialize in detecting threats relevant to its subscriber, improving detection accuracy without requiring excessive complexity in any single model.
Solution Approach 2:
The patent applies local quality by creating models with specialized characteristics tailored to each subscriber's specific data patterns and threat profiles. Each subscriber-specific model develops local expertise in detecting threats relevant to its subscriber, rather than using a generic global model that must accommodate all subscribers equally.
2Ease of operation
If existing technology implementations are used, then ease of operation is maintained, but reliability and real-time detection capability deteriorate
Solution Approach 1:
The system implements self-service by automatically training and deploying subscriber-specific models without requiring manual intervention for each subscriber. The automated pipeline handles data collection, model training, validation, and deployment, maintaining ease of operation while significantly improving reliability through specialized models.
Solution Approach 2:
The patent applies preliminary action by pre-training models on historical subscriber data before deployment. This preliminary training phase allows models to learn from past patterns and prepare for real-time threat detection, improving reliability while the automated process maintains operational simplicity.
3Device complexity
If existing technology implementations are used, then device complexity is kept low, but adaptability to new and never-before-encountered threats deteriorates
Solution Approach 1:
The patent implements dynamics by creating a continuously evolving system where models are retrained on new data as it becomes available. This dynamic approach allows the system to adapt to new and emerging threats automatically, with models evolving their detection capabilities based on latest subscriber data patterns.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and new threat data feed back into the model training process. This feedback loop enables continuous improvement and adaptation to new threats, with models learning from both successful detections and missed threats to enhance future performance.
Data Source
AI summary
A machine learning-based system and method for identifying digital threats that includes implementing a machine learning-based digital threat mitigation service over a distributed network of computers; constructing, by the machine learning-based digital threat mitigation service, a subscriber-specific machine learning ensemble that includes a plurality of distinct machine learning models, wherein each of the plurality of distinct machine learning models is configured to perform a distinct machine learning task for identifying a digital threat or digital fraud; constructing a corpus of subscriber-specific digital activity data for training the plurality of distinct machine learning models of the subscriber-specific ensemble; training the subscriber-specific ensemble using at least the corpus of subscriber-specific digital activity data; and deploying the subscriber-specific ensemble.