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7.6 ReSource Credit Risk Analysis Algorithm
In partnership with Teller Finance, we've developed the first adaptation of an Underwriting Credit Risk Analysis (CRA) algorithm.
The main goal of the CRA is to give each underwriter a top down view of each member's health in the network and their stickiness. Traditional credit reports factor in exclusively credit and payment history. Since ReSource is a mutual credit network, we need to understand not only their traditional credit and payment history, but also their perceived value in the network. A member can have a sub par credit score, but if they are well perceived in their social circle/network, and the community values them, it should contribute a considerable amount to their protocol-native credit score as that score is only applicable inside of the network.
The CRA takes into consideration four key pillars. The score is an aggregation of both on and off chain data from each of the pillars below:
  • Financials
  • Social
  • Macro Industry
  • AML/KYC/Fraud
<Insert 3-axis graph from Teller>
The financial data is collected from both on and off chain data sources such as Dune Analytics, Plaid, Debank, Zapper, Twitter, Google, Equifax, prior ReSource Network history, and other similar sources.
Over time, as a user matures within the network their activity is monitored and added to their history. This allows underwriters to reassess and subsequently assign more credit. This, like a traditional credit score, can incentivize members to provide continuous value in exchange for more trust inside of the network.
The ReSource/Teller CRA is just one example of how a network can gather and analyze the data of each member in their respective network and provide guidance as to how much credit the underwriter can offer.
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