FEATURES

A new standard for on-chain data

Fact Finance provides secure and compliant data feeds, enabling precise and efficient asset tokenization on blockchain rails

Proof of Authenticity

Proof of authenticity

On-chain wallet validation that the data comes directly from the official data provider, eliminating risks of tampering.

Verified partnerships

Commercial relationships with trusted data providers through licensing agreements with research institutes, government agencies, specialized data companies.

Cryptographic security

Each provider receives a unique cryptographic key registered on-chain, acting as a tamper-proof signature for all submitted data.

On-Chain validation

Each data point is signed with a unique cryptographic key. After that, Fact Finance verifies authenticity before publishing it on-chain.

Confidence Index

Confidence Index

Our system monitors data for anomalies using statistical and density-based detection techniques. Any outlier data is flagged so the consumer contract can determine how to handle it.

Reliable

Values with normal volatility. Trusted for critical applications.

Acceptable

Values with normal volatility. Suitable for most applications.

Outlier

Values with high volatility. Not suitable for critical applications.

Benefits

Automation

Allows protocols to automate responses based on data classification.

Precision

Uses statistical analysis and LOF for accurate anomaly detection.

Risk management

Mitigates exposure to unreliable or manipulated data.

Scalability

Easily integrates with various protocols, optimizing risk management.

External Auditors

External auditors

A pool of independent auditors validates the integrity and accuracy of the data provided.

Consensus checks

Multiple participants, such as clients or nodes, collaboratively verify data accuracy, ensuring reliability through decentralized validation.

Audit firms

Independent audit firms assess data integrity, providing external oversight to enhance credibility and compliance.

AI analysis

Machine learning and predictive models analyze data contextually, identifying anomalies and ensuring accuracy.