AI Settlement Engine for Poker Gaming Activity
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Solution Overview
Problem
Current settlement processes in poker gaming, especially with large groups, are cumbersome due to complex manual calculations, difficulty in following up on pending settlements, and the lack of efficient reuse of settlement rules, leading to increased time and effort.
Innovation Solution
An artificial intelligence system that enables easy onboarding of users and groups, performs settlements based on activity templates and user groups, offers customization of settlement steps, integrates with multiple payment gateways, generates notifications, and retains previous configurations for efficient settlement processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual settlement processes are used with large groups, then flexibility in handling individual cases is maintained, but the time and effort required increases significantly
Solution Approach 1:
The settlement process is segmented into distinct phases: automated calculation phase using AI/ML algorithms to compute settlements based on activity data, and manual review phase for exception handling. This segmentation allows bulk processing of routine settlements while preserving manual intervention capability for complex cases, thereby reducing overall settlement time without sacrificing operational flexibility.
Solution Approach 2:
The system performs preliminary actions by pre-calculating settlement amounts, identifying potential issues, and preparing settlement proposals before formal settlement execution. Activity data is continuously collected and pre-processed, so when settlement is needed, the AI system can quickly generate accurate results without starting from scratch, significantly reducing settlement time.
2Reliability
If complex settlement rules are implemented to handle all scenarios, then settlement accuracy improves, but system complexity increases
Solution Approach 1:
The system applies local quality by using different levels of rule complexity for different settlement scenarios. Simple activity patterns use basic settlement rules, while complex patterns trigger more sophisticated AI analysis. The rule engine dynamically adapts the complexity of rules applied to each specific case, ensuring accurate settlements without requiring all possible complex rules to be active simultaneously, thus managing system complexity.
Solution Approach 2:
The system changes parameters dynamically based on activity characteristics. When activity data shows simple patterns, the system uses simplified settlement parameters and rules. When complex patterns are detected, the system automatically adjusts to use more sophisticated parameters and rules. This parameter adaptation allows the system to maintain high accuracy for diverse scenarios while keeping the effective system complexity manageable for each individual case.
3Measurement precision
If settlement rules are customized for each new activity type, then settlement precision improves, but the effort to configure rules increases
Solution Approach 1:
The system uses copying by creating settlement rule templates from previously successful settlements. When a new activity type needs to be settled, the AI system can copy and adapt rules from similar historical activities rather than creating rules from scratch. This template copying mechanism preserves settlement precision for new activity types while dramatically reducing configuration time and effort.
Solution Approach 2:
The system implements self-service through automated rule generation using machine learning. The AI analysis system learns from historical settlement data and automatically generates appropriate settlement rules for new activity types without requiring manual configuration. This self-service capability maintains high settlement precision by learning from proven patterns while eliminating the time-consuming manual rule creation process.
4Reliability
If follow-up processes are implemented to track pending settlements, then settlement completeness improves, but operational complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the AI analysis system continuously monitors settlement status, compares actual settlements against expected settlements based on activity data, and automatically generates follow-up actions for incomplete settlements. This automated feedback loop ensures settlement completeness without requiring complex manual tracking processes, as the system self-corrects and follows up on pending settlements automatically.
Data Source
AI summary
Exemplary embodiments of the present disclosure directed towards an artificial intelligence system and method for performing settlements based on activity, user groups, and set of virtual/physical entities. The artificial intelligence system comprises artificial intelligence settlement engine configured to enable users for activity and provide activity templates to the artificial intelligence settlement engine from the computing device by the users. Customize the activity templates fully/partially based on dynamic needs and then provide the customized activity templates to the artificial intelligence settlement engine. The artificial settlement engine configured to integrate settlement process steps and/or rules into the settlement process sequence on the artificial intelligence settlement engine and then map settlement process steps and/or rules with the activity templates by the artificial intelligence settlement engine. The artificial settlement engine configured to identify shortest paths on the computing device to settle amount between users by using dynamic parameters and/or settlement process steps and/or rules.