We’re going to increase trust and safety across the Rarible platform and NFT ecosystem utilizing a combination of knowledge graphs, predictive analytics and deep learning.
There are great use cases for predictive analytics and AI models to enhance safety, scalability and trust in the Rarible platform.
The most impactful way to strengthen the platform is with wash-trade detection and flagging models. We are building a detailed knowledge graph, with data from over 6,000 verified wash traders. 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 call this malicious trader detection model, SCOUR.
Currently the Rarible team can receive up to a thousand reports/support requests per day. We’ve hit a bottleneck in the human capital which provides our platform support. So let’s relieve that pressure off the Rarible support team so that they can get back to helping people.
In our analysis of 1/3 of confirmed malicious trader wallets; 2.95 million $RARI tokens have been minted to addresses which were involved with malicious trading activity. This represents 14.42% percent of the $RARI token distributions and $17.24 million in value at current prices.
14.42 % of current $RARI rewards represents $324,450 per week in platform reward losses to malicious activity.
- Receive wash trader data directly from Rarible support. ✔️
- Deploy data as a knowledge graph ✔️
- Exploratory data analysis ✔️
- Data wrangling and cleaning ❌
- Feature engineering ❌
- Preprocessing and model pipeline design ❌
- Proof of concept for malicious trading detection and flagging model ❌
- Application programming interface design ❌
- Minimum viable product deployment with API ❌
- Integration into existing Rarible support infrastructure ❌
I am a creative futurist, data and AI tinkerer, and an Ocean Protocol Ambassador. I am from Bellingham, WA in the United States.
The Bitscrunch team provides big data, analytics, AI-solutions and devops. They hail from Munich Germany. Bitscrunch is a rapidly growing team with skills in computer science, devops engineering, data science, bioinformatics and their CEO Vijay Pravin was featured as a TedTalks Data Storyteller speaker.
Our team has been ideating and developing NFT marketplace platform safety features since October of last year. We have put a lot of thought and energy into this solution space.
Here is the image forgery and provenance detection model that we built for the Untitled NFT hackathon last year:
You can test out our image forgery detection model right here:
( live demo https://nsafety.bitscrunch.com/ )
We are requesting 2000 $RARI upfront and 2000 $RARI to be distributed according to milestone completions. The second half of the funds will be deposited into a Gnosis Multisig Safe — with signatory members from all the respective teams.
Four signatories will be on the Gnosis safe, 2 from our team and 2 from the Rarible team and/or community.
A portion of these funds (20%) will be used for expenses and infrastructure. That includes transaction fees and contract deployment fees as well as the procurement of a server that can continuously handle marketplace support prediction requests, data aggregation and model training.
We’ve developed a number of relationships with Rarible team members including Alex Salnikov, Eric Arsenault and Ethan van Ballegooyen. We will be working hand in hand for milestones eight through ten above. It’s very important that the finished product adheres to Rarible’s technical specifications.
Again funding will be distributed according to the completion of the above objective milestones.
Future and Goals
We’re very interested to continue deploying AI-enhanced safety features in the NFT marketplace space — like the image forgery and provenance detection.
Our goal for this MVP is to reduce $RARI tokens rewarded to malicious actors by a minimum of 15.4%