Collusion Detection in Wagering Systems via Player Profile Deviation
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Solution Overview
Problem
Existing gaming systems face challenges in detecting and preventing collusion among players, which undermines the integrity of competitive games by making it difficult to monitor and address collusive behavior effectively.
Innovation Solution
A method and apparatus that monitor player actions in multiple games, generate profile data based on historical play, detect deviations from this data to identify collusive outcomes, and take preventive actions such as alerting collusion detectors or altering gameplay results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If player behavior is monitored and profile data is generated to detect collusion, then the ability to detect collusive behavior is improved, but the system complexity increases
Solution Approach 1:
The monitoring system is divided into separate functional modules: a profiling module that generates player profiles from historical data, a detection module that identifies collusive behavior by comparing actions against profiles, and a response module that handles detected collusion. This segmentation allows each module to specialize in one aspect of collusion detection, improving overall accuracy while managing system complexity through modular design.
Solution Approach 2:
Player profiles are generated in advance by analyzing historical play patterns before actual collusion detection occurs. These pre-computed profiles capture normal player behavior characteristics, enabling the system to quickly compare current actions against established baselines during gameplay, thereby improving detection accuracy without requiring complex real-time analysis of all historical data.
2Reliability
If real-time monitoring of player actions is implemented, then collusion detection capability is improved, but the processing time and computational resources increase
Solution Approach 1:
Player behavior profiles are pre-computed from historical data before real-time detection is needed. These profiles contain aggregated statistics about normal player behavior patterns, allowing the system to perform quick comparisons during actual gameplay without requiring complex real-time analysis, thus maintaining high reliability while minimizing processing time.
Solution Approach 2:
The system focuses monitoring efforts on specific actions and behaviors that are most indicative of collusion, rather than analyzing every player action in detail. By concentrating computational resources on high-risk behaviors identified through the profiling phase, the system achieves reliable detection while reducing overall processing time and resource consumption.
3Measurement precision
If strict deviation thresholds are used to identify collusive behavior, then detection precision is improved, but false positives increase
Solution Approach 1:
The system applies different deviation thresholds and detection criteria tailored to specific player profiles, game types, and behavioral contexts rather than using a single uniform threshold. By customizing detection parameters to match local characteristics of each player's normal behavior patterns, the system improves detection precision while reducing false positives that would occur with rigid universal thresholds.
Solution Approach 2:
The detection system dynamically adjusts deviation thresholds based on the player's historical variability, game context, and confidence levels from profile matching. Rather than using fixed thresholds, the system modifies detection parameters adaptively, allowing stricter thresholds for highly consistent players and more lenient thresholds for naturally variable players, thereby improving precision while minimizing false positives.
Data Source
AI summary
Various embodiments that may generally relate to collusion are described. Collusion detection may be used to prevent players in a wagering environment from violating the integrity of a game. Player actions may be tracked to develop a wagering profile that is specific to various game situations. A player acting in a manner that would be against their interest and against their defined profile may be considered a colluding action. Information about collusion actions may be presented for evaluation and/or anti-collusion actions may be automatically taken in response to such collusion actions being determined.