Wash trading is a process whereby a trader buys and sells a security for the express purpose of feeding misleading information to the market.
Wash trading and ill-intentioned behaviours are the leading problems deterring new users from entering the NFT ecosystem.
The most impactful way to strengthen the NFT platform is with wash-trade detection and flagging models because Collectors and Traders are unable to make informed decisions.
We are building a detailed knowledge graph of the complete NFT transaction history including transfers, wallet addresses, and reward token distributions.
With this dataset, we can train models to detect, flag and grade malicious or suspicious traders.
This will drastically increase platform trust by detecting and stopping wash trading in a manner that is fast, reliable and scalable.
We’re going to increase trust and safety across the NFT platform and ecosystem utilizing a combination of knowledge graphs, predictive analytics and deep learning.
Agent Ransack acting as a watchdog that flags the spoofing transactions between the traders that manipulates both volume and price of the assets in the NFT ecosystem.
Built the knowledge graph of all the NFT transactions where we identified multiple (wash trading) patterns within the transactions.
With transaction history, we detect a Loop aka Self trade Cycle (2 or 3 traders),a Cycle with Sub-cycles (more than 3 traders).
Flagged spoofing transactions as suspicious, thereby preventing the marketplace's rewards mining system from sending the rewards to wallets.
We’ve hit a bottleneck in the human capital which provides our platform support concept and business plan.
Receive wash trader data directly from Marketplace support
Deploy data as a knowledge graph
Exploratory Data Analysis
Data wrangling, cleaning and feature engineering
Preprocessing, data pipeline and proof of concept model
Minimum viable product deployment with API
Integration into existing Marketplace support infrastructure
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