Quality Score Estimation for Digital Content Fraud Detection
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Solution Overview
Problem
In digital content distribution systems, there is a technological gap between rewardable engagements and desired conversions, leading to fraudulent and low-quality engagements that deceive promoters, degrade user experience, and reduce trust among ecosystem players.
Innovation Solution
A computer-implemented method calculates segment-specific estimated quality scores to automatically detect intent-less user engagements by determining conversion and engagement data for campaigns and segments, allowing for real-time detection and mitigation of engagement fraud through adjusting rewards and improving user interface quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If promoters offer higher rewards for specific engagements to increase conversions, then engagement quantity increases, but engagement quality deteriorates due to fraudulent and intent-less engagements
Solution Approach 1:
The patent replaces manual quality assessment mechanisms with automated machine learning models that analyze engagement patterns, device characteristics, and behavioral data to detect fraudulent engagements. This substitution enables real-time quality filtering without increasing operational complexity, allowing promoters to maintain high engagement quantities while filtering out low-quality interactions through algorithmic evaluation rather than human review.
Solution Approach 2:
The system implements feedback loops where engagement quality scores are continuously calculated and fed back to adjust reward distributions. Promoters receive real-time quality metrics that inform their reward strategies, enabling them to dynamically adjust incentives based on detected fraud patterns. This feedback mechanism ensures that reward offerings remain effective at driving conversions while automatically reducing incentives for fraudulent engagement sources.
2Reliability
If automated detection systems are implemented to identify fraudulent engagements, then engagement quality improves, but system complexity increases
Solution Approach 1:
The detection system is segmented into modular components: device characteristic analysis modules, behavioral pattern recognition modules, quality score calculation modules, and reward adjustment modules. Each segment handles a specific aspect of fraud detection independently, allowing the system to achieve high detection accuracy through specialized sub-systems rather than a monolithic complex structure. This modular architecture enables independent optimization and maintenance of each detection function.
Solution Approach 2:
The patent introduces intermediary quality score metrics that bridge raw engagement data and final fraud detection decisions. These intermediary scores aggregate multiple detection signals into standardized quality indicators, simplifying the decision-making process. The intermediaries act as mediators between complex multi-source data and the final reward allocation system, reducing overall system complexity by creating standardized evaluation layers.
3Measurement precision
If real-time detection of intent-less engagements is implemented, then conversion accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of device characteristics and initial engagement patterns before full conversion detection occurs. By pre-evaluating static device attributes and early behavioral signals, the system establishes baseline quality scores that accelerate subsequent real-time detection. This preliminary action reduces the computational burden during critical conversion moments, maintaining high detection accuracy while minimizing processing delays.
Solution Approach 2:
The patent implements partial real-time detection where not all engagements undergo full analysis at the same level of detail. Low-risk engagements receive expedited processing with reduced analysis depth, while high-risk engagements trigger more comprehensive scrutiny. This differentiated approach maintains high detection accuracy for fraudulent conversions while allowing rapid processing of legitimate engagements, effectively reducing overall processing time without sacrificing precision.
Data Source
AI summary
A method, product and system are provided for determining estimated quality scores in digital content distribution systems. The method comprises, based on monitoring of engagements and conversions, determining a segment-specific estimated quality score of a given traffic; determining an observation-based pair-specific quality score for the given traffic and for a specific campaign; and determining, based on the segment-specific estimated quality score and based on the observation-based pair-specific quality score, a pair-specific estimated quality score of the specific campaign for the given traffic segment indicating an estimated quality score of the specific campaign when presented in the given traffic segment.