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*****Say What ? Did SMCE issue a reverse split ? cuz dats da only way this scam sees a penny
ENOUGH SAID ? NA !
Our wholly owned subsidiary, Fyniti Global Equities EBT Inc. markets a software-as-a-service (“SaaS”) proprietary platform for Certified Public Accountants (“CPAs”) Financial Institutions and Registered Investment Advisors (“RIAs”) (the “Platform”). The Platform is a SaaS platform enabling users to see the developing market trends and use it to create customizable baskets if applicable.
Currently, the Company has earned only minimal revenue. The SaaS is ready to implement, and we are currently talking to different CPAs and RIAs about beta testing the Platform beginning in the first quarter of 2024.
Pending the results of beta testing, the Company plans on using a model of an initial set up fee with a monthly content fee. Each CPA, Financial Institution and RIA will be charged an initial fee of $50,000 and a monthly maintenance fee for the software of up to $100,000 per month. The Company anticipates signing a beta test contract after its Form 10 Registration Statement is effective.
The Platform’s IQ Engine enables the user to see changes occurring on the underlying indexes daily and provides alternatives based on machine learning (“ML”) and artificial intelligence (“AI”) which the user can incorporate into its decision making process.
Our AI and ML Capabilities:
Fyniti Global Equities EBT employs state-of-the-art AI/ML technologies along with Quant algorithms, to enhance our quantitative investing strategies and wealth management solutions. The primary purpose of our AI/ML capabilities is to optimize trading strategies, risk management, and portfolio allocation.
Operation of the IQ Engine:
The IQ Engine operates by continuously analyzing vast amounts of financial data, market indicators, and historical trading patterns. It employs advanced statistical models and machine learning algorithms to identify trends, correlations, and anomalies in the data. These insights are then used to make data-driven decisions regarding the execution of trades, asset allocation, and risk mitigation. By using the IQ engine, Financial Institutions, RIAs, and CPAs will have more access to information flow with which to make better decisions for their clients.
Datasets Utilized:
Our AI/ML algorithms utilize a wide range of datasets to inform their decision-making processes. These datasets include but are not limited to:
We use both public and paid sources for input. Most of the input is correlated by amassing the collective data points at the end of business everyday., Our AI/ML algorithms utilize a wide range of datasets to inform their decision-making processes. The AL/ML Algorithm captures between 20 and 30 thousand data points every day from different places. These data points comprise but are not limited to:
Market Data: Real-time and historical price data, trading volumes, bid-ask spreads, and order book information.
Economic Indicators: Macro-economic data such as GDP, inflation rates, and interest rates.
News and Sentiment Analysis: News articles, social media sentiment, and other textual data sources to gauge market sentiment.
Fundamental Data: Company financials, earnings reports, and analyst recommendations.
Alternative Data: Non-traditional data sources are also incorporated to enhance the capabilities. As an example, regarding the financial sector. Buy now pay later tracking, Credit card usage, M1distribution, and Fed Data.
We obtain those datasets from paid data providers like financial data publishers as well as public sources like corporate SEC filings Edgar and Bloomberg etc. The nature of these datasets is both structured and unstructured data. Depending on the source of the dataset, the cost varies. Regarding the AI, we use 3 broader types of algorithms, supervised learning, unsupervised learning and reinforcement learning.
The SaaS is used to create a projected weighting on each block. In essence creating a customized index basket that is actionable on by the RIA As an example there are 500 stocks in the SP 600 the AI/ML could customize a block minus oil and gas or minus banks. The institution would have access to alerts on a daily basis showing recommended adjustments within their customized block. The RIA or institution would have the decision to act or not act on the alert.
We use AI in various capacities including (but not limited to) the following use cases: RAG based Information retrieval from unstructured data like SEC filings, quarterly reports etc., Text summarization and classification, Predictive analytics using financial metrics, Automatic data enrichment and predictions, Autonomous agents to continuously analyze data, identify missing features, rank and assign based on metrics v Analyze and enhance news stories, structured inputs like analyst ratings etc. All these datasets, 3rd party inputs are internally used to predict various ratings and analysis, but those data are not shown directly to the end users.
The mix of data points is designed to provide actionable alerts to financial institutions the exact set of data points is proprietary to the SaaS platform and the company would like to not divulge it publicly.
We obtain those datasets from paid data providers like financial data publishers as well as public sources like corporate SEC filings Edgar and Bloomberg etc. The nature of these datasets is both structured and unstructured data. Depending on the source of the dataset, the cost varies. Regarding the AI, we use 3 broader types of algorithms, supervised learning, unsupervised learning and reinforcement learning.
We use AI in various capacities including (but not limited to) the following use cases: RAG based Information retrieval from unstructured data like SEC filings, quarterly reports etc., Text summarization and classification, Predictive analytics using financial metrics, Automatic data enrichment and predictions, Autonomous agents to continuously analyze data, identify missing features, rank and assign based on metrics Analyze and enhance news stories, structured inputs like analyst ratings etc. All these datasets, 3rd party inputs are internally used to predict various ratings and analysis, but those data are not shown directly to the end users.
Third-Party AI Products:
While we primarily rely on our proprietary AI models and Quant algorithms, we also utilize third-party AI products and services for specialized analyses or data enhancements. These third-party tools are carefully vetted to ensure their accuracy, reliability, and compliance with regulatory standards.
We use the comparable and mention the ETF market because we wanted to draw parallel to how these two differ in the ability to cater to same segment of stock investors.
Fyniti Blocks -- as we call it-- may look similar to ETFs, however these two are different financial products, however the end goal is these two helps professional investors in simplifying their investment strategy. Fyniti Blocks offer Ria’s and Hedge Fund managers the ability to leverage real time trends and events, offering customization etc., Our product is a technology product offered as Software-as-a-Service because it leverages latest technologies to help the RIAs/brokers while giving them the controls they need to manage these by themselves and also without changing how these stocks are traded. It helps them in managing their portfolios better. ETF are set in their weighting the software offers institutions the ability to change their weightings as needed to reflect the underlying sentiment of the machine learning protocol.
We do use data like any other typical technology-based stock trading platform would use, but not necessarily would extrapolate data from ETF markets. For the reasons given above, the way it operates is very different and also, it addresses the gaps in customization that the ETFs currently do not provide.
