Scalable UX Test Fraud Detection via ML and Panel Adapters
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Solution Overview
Problem
User experience (UX) testing systems face challenges in scalability and fraud detection due to tight coupling with panel providers, requiring cumbersome code updates and limited ability to filter out fraudulent respondents.
Innovation Solution
A scalable system architecture that parallelizes requests across multiple panel providers using a redirect service for normalized respondent records, combined with machine learning models for fraud detection and asynchronous processing to enhance system resilience and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If survey code is configured to include panel-specific APIs and schemas, then the survey works with a specific panel provider, but the system has limited ability to scale and requires cumbersome code updates when changing panel providers
Solution Approach 1:
The patent introduces an intermediary layer (adapter pattern) between the survey code and panel provider APIs. This adapter translates panel-specific APIs and schemas into a standardized internal format, allowing the survey code to remain unchanged when switching panel providers. The adapter acts as a mediator that handles the complexity of different panel provider interfaces while presenting a unified interface to the survey system.
Solution Approach 2:
The patent creates a universal survey codebase that can work with multiple panel providers through standardized interfaces. By designing the survey system to communicate through a common protocol rather than provider-specific APIs, the same survey code can be deployed across different panel providers without modification, achieving multi-functionality and scalability.
2Reliability
If panel providers maintain high-quality panelist pools, then fraudulent respondents are reduced, but panel providers have limited insight into the quality of survey results
Solution Approach 1:
The patent implements a feedback mechanism where survey result quality metrics are collected, analyzed, and fed back to panel providers. This feedback loop includes information about respondent performance, completion rates, and data quality indicators, enabling panel providers to improve their panelist pools based on actual survey outcomes rather than relying solely on their internal selection processes.
Solution Approach 2:
The patent performs preliminary quality assessment of survey results through automated validation rules and anomaly detection before results are finalized. This preliminary action identifies potential fraud or low-quality responses early in the process, allowing for corrective measures to be taken and providing insights to panel providers about potential issues in their respondent pools.
3Quantity of substance
If multiple panel providers are used to increase respondent availability, then fraud detection becomes more complex and requires additional filtering mechanisms
Solution Approach 1:
The patent merges fraud detection capabilities into a centralized system that handles multiple panel providers uniformly. Instead of implementing separate fraud detection mechanisms for each provider, the system combines all respondent data through standardized interfaces and applies unified fraud detection algorithms, reducing overall system complexity while maintaining comprehensive fraud detection across all providers.
Data Source
AI summary
Techniques and embodiments are described herein for detecting and mitigating fraudulent activity within user experience (UX) test applications. In some embodiments, a system applies a set of rules and/or machine learning (ML) models to each respondent of an online survey or UX test. Different ML models may be trained to learn domain-specific patterns indicative of fraudulent activity. The system may then select the ML models based on attributes of the UX test and/or respondent. The selected rules and/or ML models may generate a probabilistic score representing a likelihood that the respondent is currently engaging in or will engage in fraudulent activity with respect to a UX test. If the score exceeds a threshold, then the system may take action to mitigate the fraudulent activity, such as triggering the removal of the user from an accepted respondent pool, halting further engagement between the respondent and the UX test, and generating alerts.