# Nomis - Web3 Rating Score By [unchase](https://paragraph.com/@unchase) · 2023-01-15 --- Nomis Protocol -------------- [Nomis Protocol](https://www.notion.so/Nomis-Protocol-a1dbb4c92425422ab3f7b32a9ddf37f4) is a web3 credential and scoring data network. Built on an open and collaborative infrastructure, the protocol helps web3 developers and other protocols leverage credential data to build better products and communities. At the same time, data curators are rewarded when credentials are used in [Nomis Protocol](https://www.notion.so/Nomis-Protocol-a1dbb4c92425422ab3f7b32a9ddf37f4)’s Application Modules, Credential Oracle Engine, and Credential API. 👨🏻‍💻 We are currently at the _MVP stage_ → feel free to test **our MVP** [here](https://nomis.cc/) (get a wallet’s credit score calculation using Ethereum on-chain data), plus check our **GitHub** [here](https://github.com/Nomis-cc) and **pitch deck** [here](http://nomis.cc/deck/vitalii). * * * Table of Content ================ 1. [Introduction](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 2. [Market](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 3. [Problem](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 4. [Solution](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 5. [Key Features](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 6. [Value for the Ecosystem](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 7. [Value for Community](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 8. [Solution Explained](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 9. [The Algorithm](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) 10. [Team](https://www.notion.so/Nomis-Protocol-e859b9e3bae6412fb99d6119a8d2e5a4) * * * Introduction ============ [Nomis Protocol](https://www.notion.so/Nomis-Protocol-a1dbb4c92425422ab3f7b32a9ddf37f4) is an open-source protocol which provides _dynamic APR, utilization rate and collateral size_, which are calculated and balanced based on wallets’ on-chain data. The protocol operates on its own _native token._ * * * Market ====== $209B ----- Total value locked in DeFi as of Feb 11, 2022 +338% ----- TVL growth since last year $30B ---- TVL in _lending protocols_ as of Feb 11, 2022 Source: [defillama.com](https://defillama.com/) * * * Problem ======= A lot of people still don’t have access to capital, especially in countries with _underdeveloped economies_. Also, people don’t want (or don’t understand why they need) to get a loan which is less than the collateral they need to use to back such a loan. * * * Solution ======== We aim to build a protocol which will use math and an AI-based prediction model to enable users with a positive on-chain credit score to take out crypto loans with less collateral. In the _high-level concept_, we want to give access to capital to more people around the world who can’t afford (or don’t want to go with) 110% collateral or more, but at the same time have a good credit score based on their on-chain history. [Nomis Protocol](https://www.notion.so/Nomis-Protocol-a1dbb4c92425422ab3f7b32a9ddf37f4) will be an open-source protocol. Other teams can also build on top of it, and already existing high-TVL protocols can use our on-chain credit scores to balance their interest rates and collateral size individually for different wallets (users). 👨🏻‍💻 We are currently at the _MVP stage_ → feel free to test **our MVP** [here](https://nomis.cc/) (get a wallet’s credit score calculation using Ethereum on-chain data), plus check our **GitHub** [here](https://github.com/fc-ha/Numus) and **pitch deck** [here](https://www.figma.com/proto/yBiPV49nnIZNEFw2KSr9sW/Numus-Slides?page-id=0%3A1&node-id=2%3A2&viewport=387%2C364%2C0.04&scaling=contain) * * * Key Features ============ 🧮 == **Fair Credit Score** Fair and transparent calculated credit score based on on-chain data 💎 == **Optimized** **Collateral** Reliable wallets can use less collateral against their loans 🛠 == **Open Source** The protocol allows other developers build great products on top of it 🌕 == $**NUM** The protocol features a native utility token that is used for governance and rewards 💵 == $**nUSD** Borrowers take loans in stablecoins, backed with collateral and the protocol’s native token ⚖️ == **Protocol Governance** The protocol is fully managed by DAO, and all future updates are made by voting * * * Value for the Ecosystem ======================= 🙋 == **New Users** A fair interest rate and reduced collateral will likely attract borrowers from other protocols and networks 💰 == **Network’s TVL Goes Wild** More new users will bring more capital to the network, which will benefit all the network participants 🏦 == **Ecosystem Development** Current ecosystem dApps can borrow capital for liquidity and development with better rate ✨ = **Ecosystem Growth** If the protocol will finds its market fit, more devs will be interested in building new dApps and protocols on top of it * * * Value for Community =================== 📈 == **Mass Adoption** Hopefully, crypto loans will become more accessible and affordable 💫 == **Access to Capital** Users from underdeveloped economies will get access to affordable capital 💻 == \*\*Jobs for Developers \*\* More liquidity should attract more teams to build new products for the ecosystem * * * Solution Explained ================== ℹ️ The functional purpose of the solution is to _calculate the reliability of a crypto borrower_ by analyzing the automatically collected _digital credentials_ of the crypto wallet To predict a _crypto borrower’s reliability_ on the basis of a wallet’s data and transaction history, we propose to use a trained artificial intelligence model, built on _hierarchy analysis_ and _pairwise comparisons_ methods. The choice of these methods is justified by insufficiency of data on defaults of crypto loans for construction of mathematical models of forecasting based on statistical approaches. The _hierarchy analysis method_ seems to be more reasonable than the linear logic approach, as it solves _multi-criteria problems_ in complex environments with hierarchical structures involving both _tangible_ and _intangible_ _factors_. Also, the method of hierarchy analysis is a closed logical construction, providing, with the help of simple rules, analysis of complex problems in all their diversity and leading to the best answer. Moreover, this method will make it possible to include _all the knowledge we have_ on the problem in question in the dataset. This approach, from our point of view, is a balanced way of solving a quite _difficult problem_: forecasting when to make credit decisions under conditions of insufficient statistical data. * * * The Algorithm ============= At the first stage, we make a _matrix of paired comparisons of parameters_ that affect the _reliability_ of the crypto borrower in order to assess the importance of, among others, the parameters listed in the table below: The _DAO members_ evaluate each pair of parameters against each other. Using a comparison technique, they must compare each parameter to the others and assign a value according to _Saaty's pairwise comparison technique_ The use of _DAO voting principles_ will make it possible to balance risk assessments using the _paired comparison method_. Implementating a DAO will create a revolution in the classical approach described by the mathematician _Saaty_ in the construction of expert assessments using the _pairwise comparisons_ method for making a balanced decision. This is because the experts are market participants themselves, and their interest in minimizing risks will be directly reflected in the mathematical model by voting when comparing parameters. Founding Team ============= ### Fedor Cherepanov Math & Blockchain Dev * PhD in Computer Science * Author/co-author of more than 40 scientific publications on AI * Holder of 8 patent certificates of artificial neural network softwares * Developed multiple blockchain solutions on Solidity (Etherium, Polygon), and Exonum (Rust) framework. ### Alex Barabash [LinkedIn](https://cz.linkedin.com/in/alexander-barabash/ru?trk=people-guest_people_search-card) • [Telegram](https://t.me/alexbarab) * MSс in Mathematics and Economics * Deep AI and ML skills * 15 years of experience in neural network and expert systems research * CTO — 5 years * Founder: GoRecruit, Vetlan, Modus ### Arty Shatilov [Twitter](https://twitter.com/ArtemShatilov) • [LinkedIn](https://www.linkedin.com/in/artemshatilov/) • [Telegram](http://t.me/artemshtlv) * MSc in International Business Development * Product Manager — 3 years * UI/UX designer — 4 years * 4+ years in blockchain and web3 * Founder of KREO — AR/VR studio; clients: Huawei, Yandex, Mazda, RedBull * Ex. co-founder, Product Designer and Product Manager at KickCity — web3 event/ticketing platform (Ethereum) ### Nickolay Chebotov Backend Developer * Degree in CS * .NET developer * Active open source contributor * 5 times blockchain hackathons awardee --- *Originally published on [unchase](https://paragraph.com/@unchase/nomis-web3-rating-score)*