# Play Wallet ## Overview \ The Play Wallet is a universal Web3 gaming wallet that integrates seamlessly into all major gaming platforms. It leverages LLMs for user assistance and enforcement of user-defined transaction policies, and deploys graph-based ML models for robust anomaly and fraud detection. ## Model Type & Architecture * LLM-Based Policy Interpreter: The Play Wallet uses an LLM to interpret human-readable policies (e.g., “Never spend more than 0.1 ETH without confirmation”) and transform them into smart transaction filters. * Graph-Based ML for Fraud Detection: Graph neural networks (GNNs) and graph-based ML models analyze the transaction graph of cryptocurrency and NFT transfers. Suspicious patterns, unusual activity spikes, and known malicious addresses trigger warnings or require additional verification. * On-Device Key Management & HSM Integration: The wallet employs secure enclaves or Hardware Security Modules (HSMs) for private key management. The LLM never has direct access to keys; it only guides user interactions and performs risk assessments based on external data. ## Underlying Tech * Language Modeling + Policy Enforcement: LLMs parse user instructions and convert them into executable filters that run before any on-chain transaction. * Graph-Based Analytics: GNNs trained on large crypto transaction datasets identify suspicious activity. These models incorporate node embeddings, edge relationships, and historical transaction patterns to flag potential fraud. * API Integrations: Seamless integration with major blockchains, NFT standards, and asset marketplaces for frictionless asset management. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.altura.com/altura-documentation/altura-intelligence/play-wallet.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.