The field of decentralized finance has been growing at a rapid pace. This growth has been driven by the institutional privileges that these platforms provide individual users. Many protocols for lending (Compound(1), Aave(2)), yield farming (Curve(3), Yearn(4) and exchanges (Uniswap(5), Sushiswap(6)) have seen unprecedented growth. Consequently, new protocols are continually being developed.
The constant stream of protocol deployment is compounding data problems. To keep pace with DeFi data’s exponential growth, there is a clear need to assure data integrity and preserve decentralization via user-based governance. While blockchain data is natively provable, normalization and backward compatibility is virtually nonexistent. Credmark employs game theory, data pipelines, machine learning, and governance defined data structures to allow data scientists and developers to easily create models.
Once the data is structured, statistical and predictive models can be used for deeper insights and the results can be deployed into production environments. This allows users to make more actionable, informed, and risk-adjusted decisions on their DeFi positions.
Users in DeFi are faced with a variety of complex financial tools normally reserved for institutional investors and professional traders. The access to these tools democratizes finance, but retail users lack the ability to compete due to the absence of in-house back-office services. Credmark provides these services on the retail users’ behalf by bootstrapping the platform with risk management tools and analysis. These risk models address protocol, pool, asset, and counterparty risk scenarios using a variety of agents, validated periodically using backtesting and outlier detection.
By incentivizing developers to contribute strategic models and to participate in community governance, we quickly incentivize model accuracy, usefulness, accountability, ownership, and completeness.