Table Game Fraud Detection via Statistical Pattern Analysis
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Solution Overview
Problem
Existing fraud detection systems in game halls, such as casinos, struggle to identify sophisticated fraudulent acts and collusion between dealers and players, especially when large wins are not accompanied by obvious suspicious behavior.
Innovation Solution
A fraudulence monitoring system for table games that continuously tracks bet amounts and game outcomes, compares data using probability statistic calculations and past data, and detects suspicious patterns through a management control device with advanced calculation functions and artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surveillance cameras are used to monitor fraudulent acts by detecting large wins, then fraud detection capability is improved, but sophisticated fraudulence acts and continuous small wins cannot be detected
Solution Approach 1:
The system segments the monitoring task by tracking each game outcome and bet amount individually, accumulating statistical data over time. Instead of relying on single large win detection, the system divides the monitoring process into continuous tracking of individual game results, comparing each against statistical expectations to identify patterns of fraudulence.
Solution Approach 2:
The system performs preliminary statistical analysis by calculating expected win rates and bet amount distributions before fraud occurs. By establishing baseline statistical parameters in advance and continuously comparing actual game results against these expectations, the system can detect deviations that indicate sophisticated fraudulence acts.
2Device complexity
If simple win detection is used, then system complexity is reduced, but continuous winning of small amounts cannot be identified as fraudulent
Solution Approach 1:
The system merges multiple data streams including bet amounts, game outcomes, player identification, and statistical expectations into a unified monitoring framework. By combining these diverse data sources and analyzing them together through integrated statistical comparison, the system achieves high detection accuracy for continuous small wins without requiring overly complex individual components.
Solution Approach 2:
The system continuously compares actual game results against expected statistical parameters and provides feedback when deviations are detected. This feedback mechanism enables the system to identify fraudulent patterns such as continuous small wins by comparing actual performance against statistical expectations, maintaining accuracy while managing complexity through iterative comparison.
3Measurement precision
If statistical comparison is performed continuously, then fraud detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial statistical comparison by focusing computational resources on analyzing only the deviations that exceed predetermined thresholds. Instead of continuously processing every possible parameter combination, the system performs statistical comparisons on key variables (win rates, bet amounts) and only conducts detailed analysis when anomalies are detected, reducing processing time while maintaining accuracy.
Data Source
AI summary
A detection system of the present disclosure stores positions and the amount of game tokens that a game participant places on a game table based on a measurement result by a bet chip measuring device in the same persons for each game participant or player positions of the game table. A management control device compares an actual winning rate and a total return amount with figures obtained by a probability statistic calculation at the time of an end of the number of games to determine whether there is a significant difference therebetween and specifies any one of the game participant or the player position, the game table, or a room having the game table where the significant difference is occurring.