Hi Maker Community,
As someone having perspective in both the traditional financial system and the blockchain/crypto world, the concept of significant over-collateralization (of min. 150%, typically much higher) has always bothered me as prohibitely expensive and basically - the polar opposite of enabling financial inclusion (say we leave flash loans out of the picture for the time being, as they are exclusive in a whole different purely technical fashion).
Having read up a bit more about the MakerDAO mechanics over the last couple of days, I notice that the average collateral locked up is actually even higher, currently at around ~400% (likely due to the forced liquidations during the mid-March flash crash, where my own CDP also got bitten) - basically, a 4x overcollateralization of the borrowed amount.
I understand that the current protocol risk framework focuses mainly on risk management at collateral asset, liquidity and price level. However, IMO it makes sense to further fine-tune risk at individual CDP-level, by combining historic parameters about CDP-liquidations with information about the ETH wallet behind the CDP and any further voluntarily provided information as an option (i.e. identity-verifications).
In real-world credit risk modelling, verified proof about the owner’s:
- their historical loan performance (number of loans, credit cards taken/repayed/defaulted, number/frequency of late payments, number of concurrent loans - essentially, parameters that in the blockchain world are availble through various MakerDAO liquidations explorers)
- total available assets, their activity within a network, time of first transaction, nr of transaction, balances etc (in the blockchain equivalent, all that info is publicly available on the ETH blockchain)
- their digital presence, identity parameters and other variables voluntarily provided (i.e. phone numbers, e-mails, IP etc)
all feed into a model that can add a risk-modelling dimension on individual level.
Applying that model to every new open CDP, it could allow for an additional dynamic setting of parameters such as:
- lower / higher liquidation threshold/ratio;
- lower / higher liquidation penalty size;
- lower / higher stability fee;
- and many other potential use cases that apportion risk based on further information, obtained at borrower level;
Of course, it’s a given that in the crypto/blockchain world many of these aspects can be obfuscated by opening a CDP with a fresh ETH address each time, but that on its own should already be factored in (i.e. - the absence of information is information in itself).
I’ve see one or two white papers focusing on such models so far, but haven’t seen an actual application / analysis of the portfolio in any of the DeFi Lending protocols that I looked at, so it would really help to get some inputs on:
- What is the easiest way to obtain all the necessary info from the blockchain and convert it into a CSV file?
Perhaps similar to the one in WhiteRabbit’s post here (https://medium.com/@whiterabbit_hq/black-thursday-for-makerdao-8-32-million-was-liquidated-for-0-dai-36b83cac56b6), but looking at ALL CDPs ever opened and all state changes, instead of only a zoom-in on Black Thursday liquidation events;
I realize there would/could be many objections at taking this approach, especially prior to actually having done any analysis on the information, but I would appreciate thoughts or comments on what would be the biggest barrier for adoption of such “product” by DeFi Lending Protocols, such as MakerDAO
Any ideas on predicting the target outcomes or other collaboration on the matter would be greatly appreciated - please feel free to DM;
My intention would be to provide the above to a team of data scientists / analysts, proficient in credit risk modelling, with several possible targets outcomes to predict;
It is fairly possible that the exercise may prove that the available information is not sufficient to build a predictive / discriminating model & distribution, but if it is, it may help to bring a very early version of the “credit scoring” concept on-chain.
Thanks in advance.