Due to first emerging to the public as the technology behind Bitcoin, the common opinion of Blockchain relies on the success, reliability and usefulness of Bitcoin and all other projects that market themselves as Blockchain related. Therefore the success of cryptocurrencies is in the interest of all Blockchain projects and communities.

Currently, the cryptocurrency market is currently worth over 400 Billion USD[1] and it's future may hold much higher value. However there are still areas of interest that generate traction in it's growth. One of the central problems in accessing the mass commercial market and traditional banking are struggles with

regulatory compliance. Operating in this new context requires the application of the same standards as banks and other financial institutions.

Building institutional infrastructures similar to traditional banking systems would normally be a very long and complicated process. However, it seems possible that this process for cryptocurrencies can be largely driven by effective data usage and be more democratic as all participants of the ecosystem could deliver input and participate[2].

This approach will be based on two interconnected environments. First, it will be focused on streamlining, gathering, processing and evaluating data submitted voluntary by people and entities participating in the cryptocurrency ecosystem. Using various tools for determining group consensus including the advance use of machine learning makes processing constant streams of data more accurate, effective and less arbitrary than the traditional systems. What is central to this approach is providing additional incentive for system participants - using a dedicated Blockchain token that can be used to purchase services or sold on the market.

The second one is based on sharing already gathered data with system participants who have valuable skills in AML including investigators, crypto-enthusiasts and data scientists. They will be able to create and share algorithms and heuristics[4] that can be used to verify the viability of means of payment and detect undesirable parties in system. This environment will also need scrutiny in evaluation of participants input and will also offer token compensation.

During this talk basic architecture and solutions of this system will be described, including already working elements and plans for future development. This will include methodology of evaluation and definition of group consensus, classification and clustering ML models and Blockchain based "proof of data".

The speaker, Paweł Wojtkiewicz is a PHD student at the Warsaw School of Economics and a Senior Data Scientist at Coinfirm. His speech will be based on his work with the Coinfirm team and his own research.

[1] https://coinmarketcap.com/charts/

[2] F. J. Cabrerizo, J. M. Moreno, I. J. Pérez, Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks

[3] https://amlt.coinfirm.io/

[4] Lean Yu, Shouyang Wang, Kin Keung Lai, An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring