Guidance for Building a Predictive Responsible Gaming Model on Amazon SageMaker
Overview
This Guidance shows how you can build and train an ML model using Amazon SageMaker AI to predict problematic gambling behavior. Using your own player data, an impartial ML model can be trained, then deployed for inference. This model creates a risk score for your players, predicting problematic play in near real time. As a result, you can intervene proactively to facilitate early support and prevention of harm.
Benefits
Protect players through predictive insights
Implement automated early warning systems that identify at-risk gambling patterns before they become problematic. Transform player behavioral data into actionable protective measures that support responsible gaming intiatives.
Strengthen regulatory compliance measures
Help support efforts to meet evolving responsible gaming regulations with data-driven player protection systems. Demonstrate proactive responsibility through comprehensive behavioral analysis and automated monitoring.
Scale player protection efficiently
Process and analyze large volumes of player data while maintaining strict security requirements. Focus on player safety while the infrastructure automatically handles computational demands.
How it works
These technical details feature an architecture diagram to illustrate how to effectively build this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Deploy with confidence
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.
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