Network Data Output

Algorithms

The initial BlockScore® Protocol credit scoring algorithm will prioritize a specific set of risk parameters, a standardized set of global parameters for the network. The algorithm uses mathematical and statistical models using the input data to calculate the users BlockScore®. By storing the blocks of compiled data represented by cryptographic hashes on the data ledger, this data can then be used as the building blocks to train and increase the accuracy of the credit scoring algorithms on the network.

Score Factors

Calculating A Users BlockScore

Methodology – BlockScore put the most significant factors in detecting loan default as the priority by combing traditional scoring models with social, economic, and blockchain data to create a fair and inclusive risk protocol.

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Level 1 – Financial Score (40-45%) – The ability to provide collateral and down-payments through multiple streams is crucial when building a credit history and financial identity in today’s economy. The financial factors influence the size of the loan and loan terms.

Financial Data

Digital Assets – Cryptocurrencies, NFTs, Metaverse, IoT Devices

Assets – Traditional Investments

Liquidity – Cash Reserve

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Level 2 – Reliability Score (45-55%) – A study conducted at Stanford University shows consumers would care more about their credit score if it was not tied so heavily to personal income. Simply put, just because someone makes more income does not mean they are more responsible or trustworthy. Reliability is measured by past behavior, such as historical data that shows patterns of consistent financial decisions.

Payment History

Loan Activity

Total Debt

Verified Public Records

Blockchain Activity

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Level 3 – Social Score (15%) – According to a study conducted by Carnegie Mellon, social metrics detect default with higher accuracy than credit scores. The study shows that consumers naturally put more weight on social situations good credit offers than the actual credit score itself. Further research done by Stanford University indicates that social data performs on the same level as traditional credit scoring methods and even outperforms in detecting loan defaults in many situations.

Social KYC – Public Identity

Facebook Verified

Twitter Verified

Linkedin Verified

Digital Footprint

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Score Range