The FreshCredit® Network – Fair and Inclusive
BlockScore Protocol – Data Scoring Protocol DSP
The FreshCredit® Network is a transparent, decentralized blockchain supporting the BlockScore® Protocol is a dynamic indicator of an individual’s creditworthiness, adapting to the maturity of the user’s credit history whether they are new to credit or have been building for years. The credit algorithms can interact with the users’ centralized financial institution, alternative data providers via the side-chain operators, and on-chain assets and historical behavior to assess the risk of default before generating a credit score.
Network Data Input
The BlockScore® Protocol is an inclusive scoring model because it is standardized yet composable, providing open-source developers with an initial modular framework to build new credit scoring models. They can then propose new attributes for consideration, such as assets, organizations, and alternative data sources unique to each culture’s needs and regulatory demands through a process of network consensus. If the network approves the consensus, these additional attributes will be implemented to bootstrap credit scoring models that traditional scoring models may overlook.
This standardized credit risk protocol can be viewed as a prioritized set of global risk parameters. Using the technological advantages of the Polkadot/Substrate blockchain framework, new iterations of the credit scoring models do not need to fork, resulting in a new blockchain. Instead, they can continue to be part of the entire network, intrinsically providing more data for all parachain networks to utilize and benefit.
Regulator Data – Implementing credit drift for scenario-conditioned credit scores also provides a mechanism for lenders to communicate changes in risk profile more effectively to auditors, regulators, and other stakeholders as well as individual borrowers.
Economic Data – Controlling for economic factors in the credit score process can provide key insights and allow lenders to disentangle credit risk due to borrower idiosyncratic factors from broader external trends in the economy. – Moody’s Analytics
Social Network Data – Research shows that borrowed credit score, the number of successes, prestige, the number of failures, repayment period, forum currency are the essential attributes for predicting the default. In these factors, prestige and forum currency are social network information, the repayment period is loan information, and others are credit information. The credit score is less important than the six other attributes. – Science Direct
Digital Footprint – Users give explicit permission to social data, digital footprints, and blockchain data can supplement when traditional methods like government docs, financial institution KYC, Etc., are unavailable. A study conducted by the FDIC using 250,000 observations using digital footprints suggests that a lender that uses data from both sources (credit bureau score + digital footprint) can make superior lending decisions compared to lenders that only access one of the two sources of information.
Blockchain Data – Cryptocurrencies, NFTs, Metaverse, IoT Devices
Financial Data – Plaid
Credit Data – CRA
Network Data Output
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.
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.
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.
Digital Assets – Cryptocurrencies, NFTs, Metaverse, IoT Devices
Assets – Traditional Investments
Liquidity – Cash Reserve
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.
Verified Public Records
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