CryptoCurrency : Credit Rating System-UChain

CryptoCurrency : Credit Rating System-UChain

**Credit Rating System**

**What is a traditional credit rating model?**

In the case of FICO , credit scores are dependent on five dimensions: Payment history, number of accounts, length of credit card usage, type of credits, and number of new accounts. Enterprise Credit Report Systems are alike, they all contain information such as bank credit reports, tax reports etc. The data based on credit rating models share one common flaw, reliability of the rating depends on the reliability of its model. Credit information is not direct but indirect data.

How do we perform credit rating through blockchain; User consensus + Coin Days Destroyed

All we need is raw transaction data because blockchain transactions deal with the direction of time, so the marginal cost of repeated consumption is no longer zero, it’s proportional to coin days destroyed. Coin Days Destroyed is a very important concept in blockchain. For any given transaction it is calculated by taking the number of coins in a transaction and multiplying it by the number of days since those coins were spent. If someone has 10 coins they received 100 days ago and they spend it today, then 1000 coin days have been destroyed.

Using coin days destroyed as the weighting factor for credit evaluation can prevent cheaters to repeatedly transfer tokens between two accounts to increase credit. This can also prevent intentional negative reviews since higher coin days destroyed means higher weight of a transaction in the credit evaluation.

When a cheater with two trading accounts tries to give himself a high credit score by transferring coins between accounts repeatedly within a day, only the first transaction will count, as the total weight of coin days destroyed for all transactions the cheater performed almost equals to the amount of the first transaction in the final credit evaluation. This will also be the same for the users that have malicious intent and try to use small value transactions to purposely create bad ratings. It will have little to no effect on the user’s credit.

The weighted model refers to the credit evaluation score obtained by the user multiplied by the coin days destroyed of the transaction to get the user’s final credit score. The model is as follows:

𝑅𝑛 = βˆ‘ 𝑅𝑖 βˆ— π‘Ši

π‘Šπ‘– = 𝐢𝑖 βˆ— 𝐷𝑖

𝑅𝑖 ∈ {βˆ’1,0,1}

𝑖, π‘Šπ‘– ,𝐢𝑖 ,𝐷𝑖 ∈ (0, +∞)

𝑅𝑛 = a user’s final credit score.

𝑅𝑖 = the credit score a user obtains when the 𝑖 π‘‘β„Ž transaction is done.

π‘Šπ‘– = coin days destroyed of 𝑖 π‘‘β„Ž transaction.

𝐢𝑖 = the value of 𝑖 ′𝑠 transaction

𝐷𝑖 = the period of time between 𝑖 π‘‘β„Ž transaction and the last transaction before it.

In addition, UChain also introduces credit data from third-party credit rating agencies as part of the “user credit pass” ecosystem. It is responsible for providing a reliable AI algorithm to get the user information from UChain’s DApps to obtain a reliable data analysis result thus achieving a reliable credit output and UCN as a reward.

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