The problem EtherScore solves

Problem:
Traditional credit scoring systems rely on centralized data sources, which can be intrusive, biased, and vulnerable to data breaches. Many individuals, especially in the decentralized finance (DeFi) space, lack traditional credit histories, making it difficult to access financial services.

Solution:
Our dApp provides a decentralized, privacy-preserving alternative to credit scoring by analyzing blockchain-based assets like USDT balances, NFT holdings, and ENS tokens. Users can leverage their digital assets for credit assessments without exposing personal financial information. This approach democratizes access to credit, eliminates bias, and enhances security by utilizing Nada AI’s blind computation for secure data processing.

Use Cases:

• For Individuals: Secure access to loans or financial services based on your blockchain holdings, even if you lack a traditional credit history.
• For Financial Institutions: A reliable and transparent way to assess creditworthiness in the DeFi space without relying on intrusive data collection methods.

Challenges we ran into

Challenge:
One of the major challenges we faced was ensuring the privacy of user data while performing accurate credit scoring. Since the data we collect (USDT balances, NFT holdings, ENS tokens) is sensitive, we needed a way to analyze it without exposing any personal details.

Solution:
We overcame this hurdle by integrating Nada AI’s blind computation. This allowed us to process the data securely without revealing any sensitive information, ensuring that user privacy was maintained throughout the entire process. Implementing The Graph Protocol for data querying also posed a challenge, as we needed to ensure that the data was fetched efficiently and accurately. We resolved this by fine-tuning our GraphQL queries and optimizing the subgraphs for better performance.

Outcome:
By tackling these challenges, we were able to build a robust and privacy-preserving credit scoring dApp that maintains the integrity and security of user data while delivering reliable credit assessments.