Overview The Decentralized Credit Scoring Agent is an AI Agent developed using the Giza SDK. It manages a smart strategy built on top of a decentralized protocol and interacts with smart contracts based on Verifiable ML Model predictions. The agent follows a predefined logic to execute on-chain transactions based on the output of a verifiable ML Model.

Problem Statement Traditional credit scoring systems often suffer from biases and lack transparency. Decentralized credit scoring aims to address these issues by leveraging blockchain technology and machine learning models to provide fair, transparent, and verifiable credit scores.

Use Cases Financial Agents: Algorithmic structured products, Arbitrage Agents, Insurance Agents, LP Automation Agents Protocol Agents: Treasury management Agents, Reputation scoring Agents, Slashing Agents Social Agents: Recommender Agents, Content mod Agents Features Interaction with smart contracts: The agent interacts with smart contracts to execute on-chain transactions based on credit scoring predictions. Machine Learning integration: The agent utilizes machine learning models to make credit scoring predictions. Verifiable ML Model predictions: The agent ensures the verifiability of ML model predictions through the use of blockchain technology. Deployment The Decentralized Credit Scoring Agent can be deployed on any testnet supported by the Giza SDK. Deployment involves deploying the agent's smart contracts and integrating them with the Giza SDK for interaction with the blockchain.

Documentation Business Case The business case for the Decentralized Credit Scoring Agent lies in providing fair, transparent, and verifiable credit scores to individuals and entities. By leveraging blockchain technology and machine learning models, the agent aims to revolutionize the credit scoring industry and address issues of bias and lack of transparency in traditional credit scoring systems.

Functionality The agent's functionality includes:

Fetching data: The agent fetches credit data from external sources. Model training: The agent trains machine learning models on the fetched data. Making predictions: The agent uses the trained models to make credit scoring predictions. Interaction with smart contracts: The agent interacts with smart contracts to execute on-chain transactions based on the credit scoring predictions. Attack Vectors Data tampering: Attackers may attempt to tamper with the data used for training the machine learning models, leading to biased predictions. Smart contract vulnerabilities: Smart contracts used by the agent may contain vulnerabilities that could be exploited by attackers to manipulate the agent's behavior. Possible Improvements Enhanced data validation: Implementing robust data validation mechanisms to detect and prevent data tampering. Security auditing: Conducting security audits of smart contracts to identify and mitigate vulnerabilities. Conclusion The Decentralized Credit Scoring Agent is a powerful tool for providing fair, transparent, and verifiable credit scores. By leveraging blockchain technology and machine learning models, the agent addresses the shortcomings of traditional credit scoring systems and paves the way for a more inclusive financial ecosystem.