At Spectral, we’re creating and incentivizing a network of modelers, creators, users and validators using proof-of-stake mechanics. The idea — similar to Chainlink (LINK) and The Graph’s (GRT) models — is to build a decentralized marketplace with a built-in feedback mechanism that ferrets out and discourages bad actors.
This article is part of CoinDesk’s “Staking Week.” James McGirk is a senior writer at Spectral Finance and the co-founder of Lonely ROCKS.
Our multi-asset credit risk oracle (MACRO) score is a machine learning model that weighs approximately 100 on-chain signals to produce a three-digit score predicting a wallet’s likelihood of liquidation on an on-chain loan.The score is similar to the FICO score, and ranges from 300 (representing a very high risk of liquidation) to 850, representing a very low risk. It’s very similar to what you’d get from a traditional credit report, only instead of relying on Experian, Transunion and Equifax to keep tabs on your spending, you opt-in with your wallet.
The promise of an on-chain credit score is that it’s opt-in, completely transparent, and eventually, production of the algorithm generating the scores can be decentralized by incentivizing a competitive marketplace. Netflix pioneered the technique in the 2000s when they offered and eventually paid a million-dollar bounty to a team of data scientists who improved their recommendation algorithm by 10%.
To create a similar dynamic using smart contracts, we’re building a validator network. The traditional model is to pay rewards to a validator node for producing blocks and validating rewards, and punishing nodes — which is called slashing — by taking away their stake when they misbehave, which entail failing to maintain the node, behaving maliciously or other blockchain malfeasance.
Our model is a little more complicated. We’re incentivizing a contest. So we divide our network into modelers (who are machine learning engineers earning bounties by creating accurate models) and creators, who create data science challenges for the modelers to tackle, in this case an accurate credit score generated from on-chain information.
We also have validators, who vet the models for quality, and, after the contest ends, we have users who pay to use scores (i.e. machine learning…
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