AN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING BASED INVESTMENT FINANCE SUPPORT SYSTEM AND METHOD THEREOF

Abstract

The present invention relates to an artificial intelligence (AI) and machine learning (ML) based investment finance support system and method. The system incorporates a hardware-based AI accelerator for rapid execution of financial forecasting models, an edge computing device for preprocessing financial data, a specialized interactive user interface hardware for intuitive user interaction, and a real-time risk monitoring and alert module for assessing financial risks. The invention aims to enhance the speed, accuracy, and reliability of financial forecasting and investment decision-making through an integrated hardware and software approach.

Specification

Description:[013] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[014] Referring now to the drawings, these are illustrated in FIG. 1, the present invention is an AI and ML-based investment finance support system that incorporates specialized hardware components to enhance the speed, reliability, and accuracy of financial forecasting and investment decision-making.
[015] In accordance with another embodiment of the present invention, the system utilizes a hardware-based AI accelerator, which may include either an FPGA or an ASIC, specifically designed to execute AI/ML algorithms for financial forecasting. The AI accelerator is capable of handling computationally intensive operations, such as deep learning, neural networks, and other predictive modeling techniques, to generate accurate investment forecasts in real-time.
[016] In accordance with another embodiment of the present invention, the AI accelerator is interconnected with the central processing unit of the system via a high-speed data bus, allowing for the efficient transfer of data and minimizing latency. The inclusion of a hardware accelerator enhances the computational capabilities of the system, thereby providing faster, more precise, and reliable financial predictions. The FPGA or ASIC is optimized for parallel processing, allowing multiple financial models to run simultaneously, which improves the system's responsiveness to changing market conditions. Additionally, the AI accelerator features built-in security mechanisms to protect sensitive financial data during processing.
[017] In accordance with another embodiment of the present invention, an edge computing device is positioned to preprocess raw financial data collected from various sources, including stock markets, news feeds, and social media platforms. The edge computing device is equipped with specialized processors to filter, normalize, and aggregate incoming data streams, reducing noise and enhancing the accuracy of data fed into the central AI system. By reducing the amount of raw data transmitted to the central processing system, the edge computing device also helps lower network congestion and minimize latency as shown in figure 2.
[018] In accordance with another embodiment of the present invention, The edge computing device features multiple data acquisition interfaces, including Ethernet, Wi-Fi, and 5G connectivity, to ensure reliable and high-speed data collection from various sources. It also includes a dedicated preprocessing unit that uses advanced filtering algorithms to eliminate redundant or irrelevant data, thereby optimizing the quality of information provided to the AI system. The edge device is designed to operate in real-time, ensuring that only the most relevant and up-to-date financial data is transmitted to the central AI system.
[019] The system includes specialized interactive user interface hardware that allows users to interact with the system using touch or voice inputs. The interface includes a microcontroller, a touch-sensitive display, and voice recognition components that facilitate a seamless interaction experience. Additionally, the hardware features haptic feedback to communicate various risk levels associated with investment options, thus enhancing user engagement and providing a tangible indication of market risk.
[020] The interactive user interface is designed with an embedded microcontroller that processes user inputs locally, reducing the need for data transmission to the central system and thereby minimizing response times. The touch-sensitive display provides a graphical representation of investment portfolios, financial forecasts, and risk assessments, allowing users to make informed decisions quickly. Voice recognition capabilities enable hands-free interaction, making the system accessible to users with disabilities or those who prefer voice commands. Haptic feedback is implemented through vibration motors integrated into the interface, providing a physical response to high-risk alerts, which helps users perceive risk levels intuitively.
[021] The system also incorporates a real-time risk monitoring and alert module, which utilizes dedicated signal processors to continuously analyze financial data and evaluate risk metrics. The risk monitoring module is connected to a visual and auditory alert unit, which is capable of providing immediate notifications to the user when a significant risk or market change is detected.
[022] The real-time alert mechanism comprises visual indicators, such as LED lights, and auditory alerts to inform the user of potential market shifts or risks. The dedicated signal processors use advanced risk assessment algorithms to evaluate multiple risk factors, such as market volatility, sector performance, and geopolitical events, in real-time. The alert system includes a multi-channel notification feature that can send alerts via SMS, email, or push notifications to ensure that the user is always informed, regardless of their location.
The risk monitoring hardware also features a data logging component that records historical risk assessments, enabling users to review past alerts and understand the effectiveness of previous investment decisions. This historical data is used by the AI system to refine its predictive models, thereby improving future risk assessments and investment recommendations.
[023] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[024] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. An artificial intelligence (AI) and machine learning (ML) based investment finance support system comprising:
a hardware-based AI accelerator configured to execute financial forecasting models;
an edge computing device configured to preprocess financial data;
a specialized interactive user interface hardware configured to enable user interaction using touch or voice inputs; and
a real-time risk monitoring and alert module configured to assess risk metrics and provide real-time alerts.
wherein the hardware-based AI accelerator comprises a Field-Programmable Gate Array (FPGA) or an Application-Specific Integrated Circuit (ASIC) optimized for parallel processing to enhance computational efficiency in financial forecasting.
wherein the edge computing device includes data acquisition interfaces selected from Ethernet, Wi-Fi, or 5G connectivity to facilitate reliable real-time financial data collection from multiple sources;
wherein the edge computing device further includes a preprocessing unit configured to filter, normalize, and aggregate financial data before transmitting it to the hardware-based AI accelerator;
wherein the specialized interactive user interface hardware has a touch-sensitive display configured to graphically represent financial data; a voice recognition component configured to enable hands-free user interaction; and
a haptic feedback mechanism configured to indicate investment risk levels.
2. The system as claimed in claim 1, wherein the haptic feedback mechanism comprises vibration motors embedded in the interactive user interface hardware to provide a physical indication of varying investment risk levels.
3. The system as claimed in claim 1, wherein the real-time risk monitoring and alert module includes a dedicated signal processor configured to analyze risk metrics in real-time; visual, auditory, or tactile alert units configured to notify the user of significant financial risks; and a data logging component configured to record historical risk data.
4. The system as claimed in claim 1, wherein the alert module includes a multi-channel notification feature configured to send alerts via SMS, email, or push notifications.
5. A method for providing financial forecasting and investment decision support using an artificial intelligence and machine learning-based system, the method comprising:
a) preprocessing financial data using an edge computing device to filter, normalize, and aggregate the data;
b) executing financial forecasting models using a hardware-based AI accelerator;
c) providing user interaction via a specialized interactive user interface hardware; and
d) assessing risk metrics in real-time using a risk monitoring and alert module to notify users of significant market changes;
e) utilizing a hardware-based AI accelerator optimized for parallel processing to handle computationally intensive financial forecasting models for increased speed and accuracy.
6. The method as claimed in claim 5, wherein providing user interaction comprises generating haptic feedback via vibration motors to communicate varying levels of investment risk.
7. The method of claim 9, further comprising transmitting real-time risk alerts via a multi-channel notification feature to ensure user awareness of market volatility.

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