Risk Modeling

The current state of multiple, centralized data interpretation means that models built on top of this layer are incorrect and incomposable. Combined with the increasing sophistication of consumer-facing applications, the barrier to mainstream adoption still exists. While institutions continue to grow into DeFi, retail use will be limited to those few who possess significant financial acumen.

Uniswap V3(10) is a good example of the added complexity that DeFi tools can introduce. The lender has to distribute their capital over a price range interval to maximize the gain of their capital while trying to minimize risk. Development of such risk assessment models is extremely useful for new users who don’t have any data analysis experience but who are now able to make informed/risk adjusted decisions based on the models. This also helps a user understand the possible impact of a particular transaction on their portfolio.

The DeFi space is still growing into maturity in terms of data availability and integrity, which has left room for a new platform to arise that provides data in a standardized form for data scientists to build off of tied to an entire smart contract-enabled system with game theory incentives at its core. In the proceeding section we will shed some light on the difference between statistical models and machine learning-based models (predictive models) which helps categorize different techniques currently used to analyze financial data.