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Web3 Predictive Features

What does this mean?​

Predictive features in Web3 leverage blockchain data to forecast outcomes and behaviors. These capabilities are pivotal in optimizing user experiences, automating operations, infrastructure performance optimization, and facilitating informed decision-making in decentralized environments. Platforms like Seshat are designed to address the unique challenges associated with these features, particularly around data preparation and deployment.

Examples of Predictive Features in Web3​

1. Token and NFT Recommendation Systems​

Use Case: Personalized Asset Recommendations​

Token and NFT recommendation systems utilize on-chain transfer transaction history, user activity, and on-chain market trends to suggest relevant assets to users.

Challenges​

  • Data Fragmentation: Data scattered across various blockchains, making aggregation and standardization a significant challenge.
  • Messy Data: Different types of addresses, various transactions, and numerous outliers, anomalies, missing values, or even transactions that do not make sense.
  • Dynamic Data Interpretation: Adapting models to the rapid pace of change in tokenomics and NFT properties.

Seshat's Solution: Seshat’s Training Dataset Engine (TDE) facilitates the aggregation and preprocessing of diverse on-chain data, while the Real-Time Predictive Engine (RPE) supports adaptive learning and real-time recommendations.

2. Fraud Detection in DeFi​

Use Case: Anomaly Detection for Transaction Monitoring​

Predictive features can identify potentially fraudulent transactions by analyzing patterns that deviate from the norm, such as unusual withdrawal sizes or unfamiliar transaction paths.

Challenges​

  • Complex Data Patterns: Complex and often non-linear relationships in DeFi transactions are difficult to model.
  • Real-Time Processing Needs: Requires real-time transaction processing for effective fraud detection.
  • Scalability: Solutions must efficiently scale with the growing volume of transactions.

Seshat's Solution: The RPE’s real-time analysis capabilities and the TDE’s efficient data preparation methods enable quicker and more accurate fraud detection.

3. Sybil Attack Detection​

Use Case: Identifying Malicious Actors in Networks​

Sybil attack detection involves identifying entities that create multiple accounts to influence network operations, such as in voting systems or consensus mechanisms.

Challenges​

  • Identification of Duplicate Patterns: Detecting similarities in transaction patterns that could indicate Sybil accounts.
  • Data Complexity: Handling complex, multi-dimensional data from decentralized networks.
  • Deployment at Scale: Implementing detection mechanisms that operate efficiently as the network grows.

Seshat's Solution: Seshat’s TDE can streamline the handling of complex datasets, while the RPE provides the computational power needed for real-time detection and response.

4. Liquidity Mapping for Cross-Chain Platforms​

Use Case: Optimizing Asset Liquidity Across Different Blockchains​

Liquidity mapping helps platforms understand and predict liquidity variations across chains, facilitating better asset allocation and transfer strategies.

Challenges​

  • Cross-Chain Data Integration: Integrating data from multiple blockchains poses significant technical difficulties.
  • Predictive Accuracy: Developing models that can accurately predict liquidity needs based on historical and real-time data.
  • Adaptive Learning: Models must continuously adapt to changes in blockchain dynamics and liquidity conditions.

Seshat's Solution: Seshat’s comprehensive data integration tools in the TDE and the adaptive, scalable modeling capabilities of the RPE address these challenges effectively.

5. Lending Protocol Recommendation​

Use Case: Tailored Lending Options for Users​

Predictive analytics can suggest optimal lending protocols based on a user’s credit history, transactional behavior, and prevailing market conditions.

Challenges​

  • Data Sensitivity and Security: Safeguarding sensitive financial data during analysis.
  • Complex Financial Models: Developing models that understand and predict risks accurately.
  • Real-Time Recommendation Needs: Providing recommendations in real time to capitalize on prevailing market conditions.

Seshat's Solution: The secure, robust data processing and real-time operational capabilities of Seshat’s dual engines ensure that lending recommendations are both timely and reliable.

All in all, Developing predictive features in the Web3 space involves overcoming complex challenges associated with data handling, model training, and deployment. Seshat’s dual-engine approach provides the necessary tools and infrastructure to address these issues, enabling developers to create more secure, efficient, and user-friendly predictive features for a variety of applications.