Credit Risk Analytics Integration for Fraxlend

Author: RociFi

Submission date: January 21, 2023

Key terms

  • Credit scores - numerical scores representing a user’s (wallet address) creditworthiness and trustworthiness based upon their on-chain transaction history. The scale is 1 to 10 with 1 being the lowest risk (best score) and 10 being the highest risk (worst score). The scores are calculated looking at numerous DeFi protocols across 8 blockchains - not simply Fraxlend.

RociFi is a DeFi protocol enabling under-collateralized lending via on-chain credit scores. Since launch, we have issued 35K credit scores and 1300 under-collateralized loans (natively without off-chain info) with an 82% repayment rate. We want to increase capital efficiency and risk-monitoring for all DeFi protocols with our scores. To-date, we have integrated our scores with CyberConnect, Relation Labs, and ConvoSpace; and received grants from Lens and Aave to explore the same.

Real-world effectiveness examples include:

  1. Capital efficiency examples see in comments.

  2. User level risk monitoring example here.

  3. Example output of scores and analytics for a current Fraxlend borrowers:

{‘CreditScore’: 2, ‘Address’: ‘0x7a16ff8270133f063aab6c9977183d9e72835428’, ‘features’: {‘count_borrow’: 209, ‘total_borrow’: 146723742.708027, ‘total_repay’: 141486335.92446977, ‘count_repay’: 123, ‘count_liquidation’: 0, ‘total_liquidation’: 0}

Integration of our credit scores via API offer three key benefits:

  1. Could allow Fraxlend to optimize capital efficiency, thus driving more revenue to the treasury based upon changes to loan-to-value ratios in certain lending pools.

  2. Risk monitoring on a user level granularity for pre-emptive spotting of red flag situations.

  3. Potential to open up new lending markets like under-collateralized loans via Fraxlend’s OTC customized term sheets; driving product differentiation and revenue.

Capital efficiency and risk management are at the core of all DeFi lending platforms. Simply put, if protocols have better information, they can make better decisions for their stakeholders, which can lead to more revenue and less risk.

Having access to user level credit risk metrics could allow Fraxlend to become safer and more capital efficient, while building the foundation for differentiated future products like under-collateralized lending.

The integration of credit risk analytics into Fraxlend could drive greater revenue to the treasury and less risk to depositors. It further could allow Frax to differentiate itself in the future with new product offerings like under-collateralized lending, which fits with the ultimate vision of growing the utility of the Frax ecosystem.

If approved, RociFi is requesting a 6-month pilot for both parties to verify fit before committing to a longer term engagement. The proposal covers the following deliverables:

  1. Credit Analytics: Dedicated instance and custom API made available for the protocol and stakeholders to query in near, real-time, of credit scores and other metrics for current borrowers. Plus, continued maintenance.

  2. Data integration: Analytics data into Fraxlend’s existing dashboard or 3rd party created by RociFi; pending protocol’s desires for real-time monitoring.

  3. Periodic Parameter Recommendations: If requested, we can make parameter recommendations by using Frax’s existing pool parameters and our simulations of expected liquidations based upon credit score composition in the pool; subject to price volatility that may cause problems for liquidators.

  4. Documentation of key terms and metrics.

These deliverables will be split into four main milestones detailed below.

Implementation Timeline and Cost
Project team: Full-stack developer, Data Engineer, Data Scientist, and Project Manager.

Monthly cost of $25k with a 6-month term. All milestones must be approved by the protocol before moving onto the subsequent.


  1. Advance of first month’s payment of $25k to cover setup costs

  2. Credit Analytics API, Setup and Testing, 2-4 weeks

  3. Data Integration, Setup and Testing; 1-2 weeks

  4. Documentation and Parameter Recommendation, 1 week

Once all deliverables are approved, the second month’s payment of $25k shall be remitted.

Total Budget: $150,000 USD ($25k * 6 months)

We’d like to gauge interest before moving onto a formal vote.

For: Write FOR in the comments

Against: Write AGAINST in the comments

We are open to feedback or questions regarding the proposal. Thanks!

Capital efficiency examples here and here.

Hi there, Interesting stuff here. Under-collateralized lending is a big segment in Tradfi that can be implemented in defi. The modular design of Fraxlend allows us to cover that someday. However, there are still lots of untouched opportunities in collateralized lending in defi which we are planning to cover by Fraxlend with much less risk involved so I think this proposal can be considered on a long-term horizon and not in the near future.

100% agree that under-collateralized lending is a longer term play. That’s the reason we stated it as a ‘future product’ segment.

In my opinion, the lowest hanging fruit is risk monitoring and possible capital efficiency enhancement via the credit analytics. Both will allow Frax to continually distinguish itself as a leader in DeFi while gradually eating into over-collateralized lending market share.

I’ve ranked and bolded the priorities to make it more apparent. Thanks for the comment!

Integration of credit scores offer three key benefits, ranked in priority of short to longer term vision:

1. Could allow Fraxlend to optimize capital efficiency, thus driving more revenue to the treasury based upon changes to loan-to-value ratios in certain lending pools.

2. Risk monitoring on a user level granularity for pre-emptive spotting of red flag situations, thus making the protocol safer.