Threat Score Verification Workflow for Digital Fraud Detection
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Solution Overview
Problem
Current technologies for detecting digital fraud and abuse over the Internet lack accuracy and real-time response capabilities, failing to effectively detect new threats and evolve to neutralize them.
Innovation Solution
A machine learning-informed automated verification system that uses feature extraction and threat scoring models to predict threat scores, which are evaluated against decisioning workflows to determine action on online activities, enabling real-time detection and mitigation of digital threats through automated verification and disposal decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing technology implementations are used to detect digital fraud and abuse, then detection coverage is provided, but detection accuracy and real-time response capability are insufficient
Solution Approach 1:
The system dynamically adapts its detection parameters and thresholds based on real-time threat patterns and historical data. The machine learning models continuously learn from new threats and adjust their detection criteria, enabling the system to maintain high accuracy while responding in real-time to evolving digital fraud and abuse patterns.
Solution Approach 2:
The system implements feedback loops where detection results, threat responses, and outcome data are continuously fed back into the machine learning models. This feedback mechanism enables the system to learn from past detections and improve future detection accuracy while maintaining real-time response capabilities through optimized decision-making based on historical performance data.
2Adaptability or versatility
If existing technology implementations are used, then some detection capability is provided, but the ability to detect new and never-before-encountered threats is lacking
Solution Approach 1:
The system performs preliminary analysis and pattern recognition on incoming data streams using pre-trained machine learning models. By preparing detection templates and threat signatures in advance while maintaining the ability to adapt to new patterns, the system can quickly respond to novel threats without sacrificing detection accuracy through its hybrid approach of predefined rules and adaptive learning.
Solution Approach 2:
The machine learning models automatically learn and adapt to new threat patterns without requiring manual reconfiguration. The system self-updates its detection capabilities by continuously processing new data and identifying emerging threat patterns, enabling it to detect novel threats while maintaining high accuracy through autonomous adaptation.
3Productivity
If manual verification workflows are used, then verification accuracy can be achieved, but response time and automation level are reduced
Solution Approach 1:
The system introduces an intelligent intermediary layer between automated detection and manual verification. Machine learning models act as intermediaries that pre-evaluate threats and prioritize cases requiring human review, enabling the system to maintain high automation levels for routine cases while directing only complex or high-risk cases to manual verification, thus improving overall response time without sacrificing accuracy.
4Measurement precision
If comprehensive feature extraction is performed, then detection thoroughness is improved, but processing time and system complexity increase
Solution Approach 1:
The feature extraction process is segmented into multiple stages: initial rapid extraction of critical features for immediate assessment, followed by deeper analysis of secondary features for complex cases. This segmentation enables the system to maintain low processing time for straightforward threats while achieving comprehensive detection thoroughness when needed, by selectively applying different levels of feature analysis.
Solution Approach 2:
The system applies different levels of feature extraction depth to different data elements based on their relevance and risk indicators. Critical features that have higher predictive value for threat detection are extracted with greater detail and analyzed more thoroughly, while less relevant features receive minimal processing. This local quality approach maintains detection thoroughness for important aspects while reducing overall processing time.
Data Source
AI summary
A system and method for a machine learning-based score driven automated verification of a target event includes: receiving a threat verification request; extracting a corpus of threat features; predicting the machine learning-based threat score; evaluating the machine learning-based threat score against distinct stages of an automated disposal decisioning workflow; computing the activity disposal decision, wherein the activity disposal decision informs an action to allow or to disallow the target online activity; receiving the machine learning-based threat score as input into an automated verification workflow; computing whether an automated verification of the target online activity is required or not based on an evaluation of the machine learning-based threat score against distinct verification decisioning criteria of the automated verification workflow; automatically executing the automated verification of the target online activity and exposing results of the automated verification to the subscriber for allowing or for disallowing the target online activity.