Litentry x DeCredit: Identity in DeFi Credit

What is DID(decentralized identity) x DeFi credit and how do we see this combination evolve? In this article, we’ve invited Catherine from DeCredit, an off-chain credit oracle project, which is also a member of our DID Startup Program, to shed some light on this question.

  1. What is DID x DeFi credit? What problem does this combination solve?

Catherine (DeCredit): DID (decentralized identifier) is a new type of identifier that allows for decentralized, verified digital identification. A DID is any subject determined by the DID’s controller (e.g., a person, organization, thing, data model, abstract entity, etc.). Decentralized financial protocols combined with blockchain-based identity systems offer a way for previously disadvantaged people to participate in a genuinely global economic system.

DeFi solutions can enable consumers who don’t have additional assets to lower their collateralization needs and analyze their creditworthiness using reputation and financial activity traits rather than traditional data points like property ownership and income. The DeFi community values data privacy and open access when it comes to personal identifying information. Decentralized digital identity systems can be extremely useful in this situation. No one party will have complete control over an individual’s financial data if decentralized credit scoring and a decentralized digital identity system are combined.

Any decentralized credit scoring system (e.g., Discredit) applied to DeFi lending and borrowing needs to put user privacy and security first. We cannot ask individuals to give up their data sovereignty in exchange for a well-working DeFi lending system.

Mel (Litentry): DID and DeFi bestow reciprocally: On one hand, decentralized identity data such as transaction behavior, assets balance, loan repayment records, etc. can be extracted and processed by DeFi applications to generate a user credit score, which is used to evaluate user’s credibility and subsequently provide corresponding financial services such as lending. This approach aims to reduce the demand for over-collateralization in DeFi lending services, thereby introducing a lending model with reduced collaterals through credit. DeFi, on the other hand, constantly generates data that feedback the decentralized identities industry, including data providers and identity aggregation protocols like Litentry. Using machine learning to decode and label DeFi user data, these data can be converted into valuable applications such as market analytics and visualized kanban, so as to use the data to solve blockchain information delay problems in the market.

2. What are the main challenges that DeFi credit is facing in practice and tech-wise? How do we solve the problems?

Catherine (DeCredit): With the rapid development of DEFI, credit products and credit models in the blockchain world are becoming more and more popular.

At the beginning of blockchain time, people wanted to leverage the peer-to-peer, decentralized nature of blockchain and use peer-to-peer matching to facilitate the disintermediation of borrowers and lenders. In this model, borrowers and lenders put their lending needs on the chain and smart contract algorithms automatically match up borrowers and lenders. However, as blockchain lending is in its early stages, it is difficult to achieve peer-to-peer matching and thus causing an unsatisfactory user experience.

Aave, Makerdao and Compound have changed the situation by shifting the peer-to-peer aggregation model into one where lending is traded through liquid pools. The process is automatically executed and the threshold is low. This lending market attracts large amounts of capital by offering higher interest rates than traditional financial markets, and their gains can be further expanded by locked mining. However, the over-collateralization model also has many problems, including but not limited to: risks such as liquidation due to fluctuations in collateral value; limited capital utilization due to reliance on over-collateralization in lieu of credit guarantees; and in the loan market, the collateral lending only accounts for 30% and the majority of lending needs cannot be met, such as consumer loans and credit facilities.

As the world first protocol, in addition to building “on-chain and off-chain “ omnichannel lending scenario, DeCredit creates a strict scoring system, which is a system for quantitatively assessing individual credit risk, based on the Credit-Scoring algorithm and the Credit-Matching algorithm to quantify and calculate the credit score that reflects an individual’s creditworthiness, thus providing DeCredit with a basis for loan allocation, analysis, evaluation, and optimization. Once you have higher scoring, which means you have a higher level, the system will increase your mortgage rate so that to better improve asset utilization. And with such a system, we can better realize the application of decentralized credit in actual business.

Mel (Litentry): In my opinion, the hardest problems in the promotion and application of decentralized credit mainly lie in two aspects: 1) how well does it fit in lending platforms, and 2) the scarcity of data on blockchain backing decentralized credit.

The former focuses on the solution of using decentralized credit to reduce the need for collaterals. This innovative approach lowers the threshold for users to borrow, but at the same time creates a substantial amount of concerns for borrowers. The latter is because the on-chain data is relatively sparse and more scattered than off-chain data, and the amount of data will directly affect the credibility and comprehension of decentralized credit. However, Litentry’s cross-chain identity aggregation protocol alleviates this problem by retrieving and connecting data from different networks and weighting the data across multiple networks to produce a more reliable and representative user credit score.

3. Are you confident that lending platforms will adopt your solutions? How do we reduce risk in under-collateralized loans?

Catherine (DeCredit): DeCredit will allow lenders to view the borrower’s eligibility. However, DeCredit will also help to avoid over-collateralization when looking to borrow assets. Hence entities will have the ability to put their positive credit scores to use and to access more rewarding opportunities.

Taking into account liquidation risk, cryptocurrency volatility risk and in order to avoid triggering systemic risk, DeCredit sets Loan-To-Value (LTV). Each reserve has a specific Loan-To-Value (LTV), which is calculated as a weighted average of the different items that make up the collateral.

Loan-To-Value (LTV) reflects the collateral lending risk ratio, standing for the ratio of loan-to-value to collateral value, with the maximum LTV acting as the core element of risk management. When the maximum LTV is allocated to a low level, the overall credit loan risk will become lower.

The borrowing position supports two interest rate models, fixed-rate and variable rate. The borrowing time is open-ended, i.e. there is no specific repayment time required and the loan can be repaid (partially or fully) at any time.

Liquidation of a borrowing position may be triggered when prices fluctuate. A liquidation event occurs when the price rises and the collateral falls below a threshold LQ, known as the liquidation threshold. Reaching this ratio leads to a liquidity bonus, which will provide an incentive for liquidators to purchase collateral at a discount. Each reserve has a specific liquidation threshold, the same as LTV. The average liquidation threshold La is calculated using a weighted average of the liquidation thresholds of the underlying assets of the collateral to perform this operation dynamically. At any time, the borrowing position is characterised by a risk management factor function Hf, which is a function of the total collateralized loans. The role of liquidator is introduced in the DeCredit system. The liquidator liquidates credits with a health index of less than 1, for which the liquidator will receive an incentive of a certain amount of the total value of the collateral. This incentive enables the liquidator to liquidate unhealthy loans in a timely manner, thereby safeguarding the financial security of the system as a whole.

4. How do you see DID x DeFi evolve together in the near future?

Catherine (DeCredit): We think highly of DID x DeFi development. The combination of DID x DeFi will definitely promote more derivatives of decentralized financial products, and with more competitive products comes to market, the market will show a prosperous scene, but also, some people will design some financial products to deceive users, so users need to be more carefully to identify products in the market. Of course, we cannot avoid the regulatory policies of the national government, so the compliance and legality of products may soon become a trend.

Mel (Litentry): I believe that users will have more freedom with their identity data, and therefore more tools will emerge to enable them to arbitrarily choose, use, and even abandon DeFi products.

About DeCredit

DeCredit empowers the DeFi market by introducing the credit loan model that links credit authentication nodes and credit Oracle to lending products. With a view to progressively reduce collaterals, their model enables optimized resource allocation and provides liquidity support to a wider range of entities and individuals.

About Litentry

Litentry is a Decentralized Identity Aggregator that enables linking user identities across multiple networks. Featuring a DID indexing protocol and a Substrate-built distributed DID validation blockchain, Litentry provides a decentralized, interoperable identity aggregation service that mitigates the difficulty of resolving agnostic DID mechanisms. Litentry provides a secure vehicle through which users manage their identities and dApps obtain real-time DID data of an identity owner across different blockchains.

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