Spectral is a primitive that creates a programmable creditworthiness ecosystem, facilitating capital efficiency on-chain through credit risk analysis. The protocol achieves this by assigning users a Multi Asset Credit Risk Oracle (MACRO) score which allows the synchronization of multiple on-chain data sources to compose a single ERC-721 asset. This type of infrastructure is not only exciting but crucial to the future of decentralized credit markets, underwriting, and overall network trust.
One of the major issues inherent to consumer credit reporting is that it is siloed and monopolized – users have very little control or perspective on their credit. This could change in the near term with the technology that Spectral is building. Because applications for creditworthiness are inhibited by their lack of composability, the implementation of smart contracts that aggregate discrete information into an understandable set of data can change how this reporting works. Spectral’s solution to this problem is what they refer to as a Non-Fungible Credit Score (NFC) - a rigorous multifactorial statistical analysis of on-chain activity. The protocol surveys data points including transaction history, liquidation history, repayment history, credit diversity, along with length of credit history to arrive at this value. Beyond this, Spectral also runs a ML algorithm that filters their data sets for only the highest quality instances of highly predictive credit indicators – ensuring that none of their inputs represent the same information value and thereby impact the accuracy of the final risk model.
The framework that I use to define my thesis on Spectral is the idea of how essential social consensus is for veritable reputation.
Group Processes & Intergroup Relations (GPIR), a peer-reviewed journal dedicated to research on social psychological processes, addresses this notion in their report entitled: “Content, structure, and dynamics of personal reputation: The role of trust and status potential within social networks:”
“With respect to the structure of the reputation of the individual distributed across a social network, given our theoretical definition of reputation as an individual’s “agreed-upon” character, we hypothesized that there will be social consensus and accuracy in distributive reputation, which we hypothesized would increase over time, given the nature of the construct.”
In the same way that we want to be able to read product reviews online and know that their authors have actually used the product, we want to know the same characteristics about the individuals and groups that we trust on-chain. Effectively, we want and deserve clear identifiers as to whom we’re interacting with on-chain to determine the credibility and legitimacy of their actions and the trustworthiness of their statements. The applications for this technology also span vastly beyond simply consumer use-cases. For example, with Spectral, a DeFi credit fund can seamlessly borrow capital from debt providers by providing their NFC data. Yield metrics can correspond to the underlying risk based on their NFC. Underwriters will be able to see the component data of the NFC and make effective credit decisions based on real on-chain events. This is something that can take TradFi credit teams years to compile enough real-world data to valuably score credit sponsors. Spectral could turn years into weeks by updating the infrastructure behind this entire system.
Disclaimer: I am not an investor or active participant in Spectral Finance or its network.