The scoring system is not only utilized in traditional finance as a usual measure but also has gained importance in the crypto market. In this article, we will find out the approach and method of the scoring system in the traditional finance and crypto market as well as have a deep view of the crypto scoring system developed by Centic.
Introduction of the scoring system
In the world of data and technology, there is little space for gut-feeling-based solutions. Instead, most decision-making processes, no matter in the field of technology, medicine, education, etc., need assistance from scoring systems.
In the finance sector, these models help individual investors and institutions to evaluate the financial statuses of various entities (individuals, organizations, assets, etc.) and make reasonable data-driven decisions.
Throughout the developing history of traditional finance, the most popular scoring system is credit scores, which represent an individual’s creditworthiness based on their credit history. For now, this model is gradually utilized in the crypto world, which is developing vastly and promising to thrive much further in the next few years with a huge number of transactions and total value locked. Similar to Traditional Finance, in Crypto, credit score also plays the role of evaluating the ability and possibility to pay the loan of the borrowers in the market.
Centic, as a player in this new field, provides a comprehensive scoring system that bridges the gap between users with the blockchain world. In more detail, we track and analyze transactions and financial statuses of every blockchain entity, which could be individuals, DeFi protocols, GameFi projects, etc, then dynamically score and rank them based on that.
For example, with the DeFi projects, we evaluate their token health or the wallet reputation and credit scores of their users. Meanwhile, regarding GameFi applications, the active score of DApps, NFT marketplace index, NFT collection health scores, etc., will be taken into account.
What are these scores are used for?
Based on each project and its operation, these scores can be utilized in Decentralized Autonomous Organizations (DAO) allowing users with high reputations, to own higher voting power. It is also helpful in lending protocols as proof of trust, letting users lend the undercollateralized loan.
Moreover, with the promising integration between crypto and traditional finance, more and more people hold both fiats and cryptos. In this case, traditional financial institutions could also consider crypto credit scores as a way to know more about their customers (risk acceptance ability, trustworthiness, digital asset balance, etc.). Based on that, they can diversify their services (lending fiat money based on crypto scores, for example) to meet the need of various customer segments.
Methods of the existing scoring system
The most used scoring method in finance is the Scorecard approach with multiple factors to evaluate the entities’ scores. In traditional finance, many score types have been put on financial reports for both businesses and individuals, such as FICO score, Business Payment Inde, Business Credit Risk score, etc. Applying to the crypto world, these scores are linked to protocols/DApps and wallets (individuals).
Besides that, due to the development of Artificial intelligence, AI-based credit scoring is promising to be a leap. Specifically, AI credit scoring decisions are based on a huge data amount, such as total income, credit history, and transaction analysis. As a result, credit scoring using AI provides more sensitive, individualized credit score assessments based on an array of additional real-time factors, giving access to finance to more people with income potential.
Methodology for Centic Scoring system
Data Model
Knowledge Graphs (KGs) have emerged as a compelling abstraction for organizing the world’s structured knowledge, and as a way to integrate information extracted from multiple data sources. A knowledge graph is a directed labeled graph in which we have associated domain-specific meanings with nodes and edges. In the blockchain space, the Nodes represent the entities such as Wallets, Projects, Smart contracts, and tokens. Meanwhile, The edges represent the relationship between entities in the blockchain space: like Transfer, Call Smart Contract, is Part of, etc. This data model is very useful for Blockchain data. Based on this type of modeling, we can organize knowledge of the Blockchain Data and apply AI, Machine learning for data mining.
Scorecard approach
At first, we are applying the Scorecard approach for Token Health and Credit scoring for wallets. In the case of Wallet’s Credit score, we applied the FICO Score based on the following factors:
In case of Token health, we applied the formula based on following factors:
The Future of Centic scoring system
Currently, there are about 30 protocols with many DApps in this field and the number is still increasing. Therefore, we are strategizing to collect more on-chain data for the Knowledge Graph as well as off-chain data on social networks such as Twitter, Telegram, etc. with the aim of giving the score of the entire blockchain world.
Moreover, to improve the accuracy and the performance of the Centic scoring system, we are developing cutting-edge AI models namely Reinforcement Learning and Online Learning, etc. This will process and acquire deep insights from the enormous amount of blockchain data more efficiently.
