What is Future Wealth AI?

Future Wealth AI (FWAI) is an advanced artificial intelligence-powered trading system that leverages blockchain technology to provide secure, transparent, and efficient financial services. By harnessing the power of AI and distributed ledger technology, FWAI aims to revolutionize the way people invest, trade, and manage their wealth.

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Project Vision and Mission





The vision of the Future Wealth AI project is to create a more efficient, transparent, and accessible financial system that empowers individuals to achieve their financial goals. By leveraging the power of AI and blockchain technology, FWAI aims to democratize access to wealth-building opportunities and provide users with a more rewarding and empowering financial experience

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Main Features of FWAI Platform



AI Trading Signal Decision System

Programmatic and AI Trading

Trend trading system

Risk Management

Transparency and Traceability

Accessibility

Meet Our Team and Contributors

The Future Wealth AI project is backed by a diverse and experienced team of professionals from various fields, including finance, artificial intelligence, blockchain technology, and entrepreneurship.

Founder of the Academy

George Rosen Smith

Founder of the Academy

Prof. Smith graduated from Columbia Business School with a Ph.D. in Finance and has more than thirty years of experience in financial investment! Prof. Smith is the initiator of the Future Wealth AI program, as well as the leader and decision maker of the program. He is responsible for the strategic planning, operations and management of the entire program, and has a very in-depth understanding of market trends and industry dynamics.

CTO

Dr. Mohanmed Khan

Chief Technology Officer

Dr. Mohanmed Khan is a renowned expert in the field of blockchain technology, having obtained his Ph.D. in Distributed Systems from the Massachusetts Institute of Technology (MIT). He has previously worked as the head of the blockchain research lab at a leading technology company, where he led the development of several innovative blockchain-based solutions.

Head of Data Science

Micheal Gonzalez

Head of Data Science

Micheal Gonzalez is a seasoned data scientist with a Master's degree in Statistics from the University of California, Berkeley. He has over 10 years of experience in building and deploying data-driven solutions for the financial sector, with a particular focus on using machine learning and natural language processing techniques to extract insights from large datasets.

Token Economic Model

The Future Wealth AI project has developed a token economic model that aligns the incentives of all stakeholders within the FWAI ecosystem. The FWAI token (FWAI) serves as the primary medium of exchange and value transfer within the platform, enabling users to participate in the platform's services and share in its growth.

Token Name: FWAI     Total Supply: 300 million

Token Allocation Strategy:

• IDO: 15%-----will be allocated to the initial decentralized offering, providing early access to the FWAI platform and its services.
• Scientific research: 20%-----will be allocated to funding ongoing research and development efforts related to AI and blockchain technologies.
• Foundation (including shareholder dividends): 20%-----will be allocated to the FWAI Foundation, which will be responsible for managing the platform's operations, community engagement, and shareholder dividend distributions.
• Operations team: 25%-----will be allocated to the FWAI operations team, including developers, data scientists, and business professionals, as a reward for their contributions to the project.
• QTCPcoin Exchange: 5%-----will be allocated to the QTCPcoin Exchange, which will serve as the primary exchange for FWAI tokens.
• Charity: 5%-----will be allocated to charitable causes and initiatives that align with the FWAI project's mission of promoting financial inclusion and empowerment.
• FFIA Academy Benefit: 10% of the total supply will be allocated to FFIA Academy participants who contribute to the Future Wealth AI program, with the exact number of academy allocations based on those published by FFIA Academy.
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