Dynamic Fraud Detection in Streaming Platforms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for mitigating fraud in streaming content consumption are inadequate, as they rely on static rules that fail to adapt to changing user behavior and cannot effectively distinguish between genuine and fraudulent activities.
Innovation Solution
A computer-implemented method and system that dynamically analyze user interaction data to identify outlier events and anomalies, generating user breach profiles and determining fraud events through rule application, thereby triggering appropriate actions and monitoring user risk scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules are used to detect fraudulent activity, then the system is simple to implement, but it cannot adapt to changing user behavior and generates false positives
Solution Approach 1:
The patent implements dynamic fraud detection by continuously analyzing user interaction data and adapting detection parameters based on observed behavior patterns. The system transitions from static rule-based detection to dynamic analysis that adjusts to changing user behaviors, thereby resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and user behavior patterns are fed back into the detection algorithm to continuously improve and adapt. This feedback loop enables the system to learn from past interactions and adjust its detection criteria dynamically, achieving adaptability while managing complexity through iterative refinement.
2Reliability
If monitoring mechanisms deny access based on static rules, then fraudulent activity is limited, but genuine subscribers are also affected
Solution Approach 1:
The patent applies local quality by implementing context-aware detection that considers specific user behaviors, interaction patterns, and situational factors. Instead of applying uniform rules to all users, the system analyzes local characteristics of each user's interaction pattern to make accurate fraud determination, thereby improving reliability without compromising user experience for genuine subscribers.
Solution Approach 2:
The system dynamically changes detection parameters based on analyzed user behavior patterns. By adjusting detection thresholds and criteria according to observed behaviors rather than using fixed parameters, the system achieves more accurate fraud detection that distinguishes genuine users from fraudulent ones, thereby improving reliability while maintaining ease of operation for legitimate users.
3Object-affected harmful factors
If predefined rules limit access after a certain number of logins, then the scope of fraudulent action is limited, but genuine users with software glitches are incorrectly blocked
Solution Approach 1:
The patent implements preliminary analysis of user interaction patterns before applying access restrictions. By pre-analyzing behavior patterns, device characteristics, and interaction quality before triggering blocking rules, the system can identify fraudulent activities more precisely while avoiding false blocking of genuine users experiencing technical issues, thereby improving measurement precision while maintaining protection against fraud.
Data Source
AI summary
A method and system for mitigating risk of frauds related to streaming content consumption is disclosed. Users' interaction data corresponding to a plurality of users of a digital platform related to streaming content is received. A set of users from the plurality of users is determined based on the users' interaction data. The set of users is determined based on the presence of at least one of an outlier event and an anomaly in their respective user interaction data. For each user of the set of users, a user breach profile for a user is generated; a fraud event is determined by applying at least one rule on the user breach profile; at least one action is triggered in response to a determination of the fraud event; and a risk score is determined for the user based on the user breach profile to monitor user interaction data of the user.