APPLICATION OF AI IN ANTICIPATING DEMANDS OF PEOPLE FROM ALGORITHM

Abstract

ABSTRACT Disclosed herein is a system of dynamic portfolio optimization algorithm 5 for personalized demand anticipation comprises a data acquisition module configured to gather real-time demand data. The system includes a user profile analysis engine configured to analyze user- specific financial demands, periodic demands, preferred goods, and historical performance data. The system also includes a dynamic 10 portfolio optimization algorithm to dynamically adjusts investment allocations based on real-time data, user profile analysis, and market conditions. The system also includes a decision support module configured to provide recommendations and insights to users based on the results of the dynamic portfolio optimization algorithm. The system 15 also includes an interface module configured to present the optimized portfolio recommendations and insights to the user. The system also includes a feedback loop mechanism configured to collect user input and performance data, and update the user profile and optimization parameters to continuously refine the portfolio optimization process.

Specification

Description:APPLICATION OF AI IN ANTICIPATING DEMANDS OF PEOPLE FROM ALGORITHM

FIELD OF DISCLOSURE

[0001] The present disclosure relates generally relates to the field of

5 financial technology. More specifically, it pertains to a system of dynamic portfolio optimization algorithm for personalized wealth management.
BACKGROUND OF THE DISCLOSURE

[0002] In today's rapidly evolving financial markets, individuals and institutions are increasingly seeking advanced solutions for effective wealth
10 management.

[0003] Traditional portfolio management techniques often fall short in addressing the dynamic and personalized needs of investors due to their reliance on static models and limited adaptability to changing market conditions.
15 [0004] Dynamic portfolio optimization is a critical aspect of modern wealth management that aims to balance risk and return by continuously adjusting investment portfolios based on real-time market data and individual investor profiles.
[0005] The increasing complexity of financial markets, coupled with the

20 growing diversity of investment options, necessitates sophisticated algorithms capable of optimizing portfolios dynamically to reflect individual preferences, risk tolerance, and investment goals.



[0006] Personalized wealth management further complicates the optimization process by requiring algorithms to incorporate diverse and often subjective investor preferences.
[0007] Conventional portfolio management systems typically rely on

5 historical data and predefined risk-return parameters, which may not adequately capture the nuanced needs of individual investors.
[0008] Developing and maintaining a dynamic portfolio optimization system can be highly complex and costly. It requires sophisticated algorithms, robust computing infrastructure, and ongoing updates to adapt to changing
10 market conditions.

[0009] Managing personal financial data poses significant privacy and security risks. Any breaches or misuse of data could lead to financial loss and reputational damage.
[0010] Dynamic portfolio optimization algorithms rely on historical data and

15 market trends, which may not always predict future market conditions accurately. This can result in suboptimal investment decisions and increased risk.
[0011] Algorithms that are too finely tuned to historical data might not generalize well to new, unseen market scenarios. This could lead to
20 overfitting, where the model performs well on past data but fails in future situations.



[0012] The effectiveness of the algorithm heavily depends on the quality and accuracy of the data used. Inaccurate or incomplete data can lead to poor decision-making and suboptimal portfolio performance.
[0013] The dynamic nature of the algorithm requires continuous monitoring

5 and adjustment to ensure it remains effective as market conditions change.

This adds to the operational burden and costs.

[0014] Financial management systems are subject to various regulatory requirements. Ensuring compliance with these regulations can be challenging and may require additional resources.
10 [0015] While the algorithm aims to provide personalized recommendations, it may not fully capture the nuances of individual preferences, goals, and risk tolerance, leading to a one-size-fits-all approach.
[0016] Relying on complex algorithms and technology may create dependency on specialized skills and tools, which could be a barrier for
15 users who are not technologically adept.

[0017] Algorithms may not fully account for psychological factors and human behaviour that influence investment decisions. This could lead to misalignment between the algorithm's recommendations and the user's actual investment preferences.
20 [0018] Thus, in light of the above-stated discussion, there exists a need for a system of dynamic portfolio optimization algorithm for personalized wealth management.
SUMMARY OF THE DISCLOSURE



[0019] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are
5 appended hereto in order to particularly point out the invention.

[0020] According to illustrative embodiments, the present disclosure focuses on a system of dynamic portfolio optimization algorithm for personalized wealth management which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
10 [0021] An objective of the present disclosure is to develop a dynamic portfolio optimization algorithm that continuously adjusts investment strategies based on real-time market data and individual financial goals. [0022] Another objective of the present disclosure is to enhance personalized wealth management by incorporating individual risk tolerance,
15 investment preferences, and financial goals into the algorithm's decision- making process.
[0023] Another objective of the present disclosure is to create a system that integrates predictive analytics to forecast market trends and inform portfolio adjustments for maximizing returns and minimizing risks.
20 [0024] Another objective of the present disclosure is to ensure the algorithm adapts to changing economic conditions and personal circumstances by incorporating adaptive learning techniques and real-time feedback mechanisms.



[0025] Another objective of the present disclosure is to provide a user- friendly interface that allows investors to easily set their preferences, view real-time portfolio performance, and receive actionable insights and recommendations.
5 [0026] Yet another objective of the present disclosure is to validate the effectiveness of the portfolio optimization algorithm through rigorous back testing and performance analysis against historical data and benchmark indices.
[0027] In light of the above, a system of dynamic portfolio optimization

10 algorithm for personalized wealth management comprises a data acquisition module configured to gather real-time financial data. The system includes a user profile analysis engine configured to analyze user-specific financial goals, risk tolerance, investment preferences, and historical performance data. The system also includes a dynamic portfolio
15 optimization algorithm to dynamically adjusts investment allocations based on real-time data, user profile analysis, and market conditions. The system also includes a decision support module configured to provide recommendations and insights to users based on the results of the dynamic portfolio optimization algorithm. The system also includes an interface
20 module configured to present the optimized portfolio recommendations and insights to the user. The system also includes a feedback loop mechanism configured to collect user input and performance data, and update the user



profile and optimization parameters to continuously refine the portfolio optimization process.
[0028] In one embodiment, the data acquisition module is further configured to integrate with multiple financial data sources, including stock exchanges,
5 economic databases, and financial news feeds, to ensure comprehensive and up-to-date information.
[0029] In one embodiment, the user profile analysis engine incorporates machine learning techniques to enhance the accuracy of user-specific financial goal predictions and risk tolerance assessments.
10 [0030] In one embodiment, the dynamic portfolio optimization algorithm utilizes a multi-objective optimization approach to balance between maximizing returns and minimizing risks according to the user's investment profile.
[0031] In one embodiment, the decision support module includes a scenario

15 analysis component that allows users to view the impact of different market conditions and investment strategies on their portfolio.
[0032] These and other advantages will be apparent from the present application of the embodiments described herein.
[0033] The preceding is a simplified summary to provide an understanding

20 of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction



to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
5 [0034] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to
10 the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.


BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To describe the technical solutions in the embodiments of the

15 present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other
20 implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.



[0036] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0037] FIG. 1 illustrates a flowchart outlining sequential step involved in a

5 system of dynamic portfolio optimization algorithm for personalized wealth management, in accordance with an exemplary embodiment of the present disclosure.
[0038] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
10 [0039] The system of dynamic portfolio optimization algorithm for personalized wealth management, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit
15 the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE

[0040] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are
20 in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications,



equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0041] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the
5 present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0042] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent
10 art have given that term as reflected in printed publications and issued patents at the time of filing.
[0043] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0044] The terms "having", "comprising", "including", and variations thereof

15 signify the presence of a component.

[0045] Referring now to FIG. 1 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in a system of dynamic portfolio optimization algorithm for personalized wealth management, in accordance with an exemplary
20 embodiment of the present disclosure.

[0046] A system of dynamic portfolio optimization algorithm for personalized wealth management comprises a data acquisition module configured to



gather real-time financial data. The data acquisition module is further configured to integrate with multiple financial data sources, including stock exchanges, economic databases, and financial news feeds, to ensure comprehensive and up-to-date information.
5 [0047] The system includes a user profile analysis engine configured to analyze user-specific financial goals, risk tolerance, investment preferences, and historical performance data. The user profile analysis engine incorporates machine learning techniques to enhance the accuracy of user-specific financial goal predictions and risk tolerance assessments.
10 [0048] The system also includes a dynamic portfolio optimization algorithm to dynamically adjusts investment allocations based on real-time data, user profile analysis, and market conditions. The dynamic portfolio optimization algorithm utilizes a multi-objective optimization approach to balance between maximizing returns and minimizing risks according to the user's
15 investment profile.

[0049] The system also includes a decision support module configured to provide recommendations and insights to users based on the results of the dynamic portfolio optimization algorithm. The decision support module includes a scenario analysis component that allows users to view the impact
20 of different market conditions and investment strategies on their portfolio.

[0050] The system also includes an interface module configured to present the optimized portfolio recommendations and insights to the user.



[0051] The system also includes a feedback loop mechanism configured to collect user input and performance data, and update the user profile and optimization parameters to continuously refine the portfolio optimization process.
5 [0052] FIG. 1 illustrates a flowchart outlining sequential step involved in a system of dynamic portfolio optimization algorithm for personalized wealth management.
[0053] The first step in the system is the data acquisition module, which is responsible for collecting real-time financial data. This includes a wide range
10 of information such as current asset prices, market trends, economic indicators, and other relevant financial metrics. This data serves as the foundation for all subsequent analysis and optimization tasks. By continuously gathering and updating this information, the system ensures that it operates with the most current and relevant data available, which is
15 crucial for accurate and effective portfolio management.

[0054] Once the real-time data is collected, the system moves to the user profile analysis phase. Here, the user profile analysis engine examines the financial goals, risk tolerance, investment preferences, and historical performance data of the individual user. This comprehensive analysis
20 creates a personalized investment profile, which reflects the user's unique financial situation and objectives. The personalized profile is essential for tailoring the investment strategy to align with the user's specific needs and preferences.



[0055] With the user profile established and real-time financial data at hand, the system then employs the dynamic portfolio optimization algorithm. This algorithm dynamically adjusts investment allocations based on a combination of real-time data, the user's personalized profile, and prevailing
5 market conditions. The goal is to optimize the portfolio's performance by continually adjusting asset allocations to respond to changing market dynamics and the user's evolving financial situation
[0056] After the optimization process, the decision support module provides actionable recommendations and insights. This module interprets the
10 results generated by the optimization algorithm and translates them into practical advice for the user. It helps the user understand the proposed adjustments to their portfolio, including potential risks and benefits, thus facilitating informed decision-making.
[0057] The optimized portfolio recommendations and insights are then

15 presented to the user through the interface module. This module is designed to deliver the information in a user-friendly manner, allowing the user to interact with the recommendations, view detailed reports, and make adjustments as needed. The interface ensures that the user can easily understand and act upon the optimization insights provided.
20 [0058] The final step involves the feedback loop mechanism, which collects user input and performance data from the implemented portfolio changes. This feedback is used to continuously update the user profile and optimization parameters, allowing the system to refine and improve the



portfolio optimization process over time. By integrating user feedback and performance results, the system enhances its accuracy and effectiveness in achieving personalized wealth management goals.
[0059] While the invention has been described in connection with what is

5 presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
10 [0060] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0061] The foregoing descriptions of specific embodiments of the present

15 disclosure have been presented for purposes of illustration and description.

They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its
20 practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are



contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure. [0062] Disjunctive language such as the phrase "at least one of X, Y, Z,"
5 unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least
10 one of Z to each be present.

[0063] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the
15 present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:

1. A system of dynamic portfolio optimization algorithm for personalized demand anticipation comprises of
a data acquisition module configured to gather real-time demand base;

5 a user profile analysis engine configured to analyze user-specific Previous demands, risk tolerance, investment preferences, and historicalperformance data to create a personalized investment profile; a dynamic portfolio optimization algorithm to dynamically adjusts investment allocations based on real-time data, user profile analysis,
10 and market conditions;

a decision support module configured to provide recommendations and insights to users based on the results of the dynamic portfolio optimization algorithm;
an interface module configured to present the optimized portfolio

15 recommendations and insights to the user;

a feedback loop mechanism configured to collect user input and performance data, and update the user profile and optimization parameters to continuously refine the portfolio optimization process.


20 2. The system (100) as claimed in claim 1, wherein the data acquisition module is further configured to integrate with multiple purchase data sources, including stock exchanges, economic databases, and financial news feeds, to ensure comprehensive and up-to-date information.



3. The system (100) as claimed in claim 1, wherein the user profile analysis engine incorporates machine learning techniques to enhance the accuracy of user-specific financial goal predictions and risk tolerance assessments.
5 4. The system (100) as claimed in claim 1, wherein the dynamic portfolio optimization algorithm utilizes a multi-objective optimization approach to balance between maximizing returns and minimizing risks according to the user's investment profile.
5. The system (100) as claimed in claim 1, wherein the decision support

10 module includes a scenario analysis component that allows users to view the impact of different market conditions and investment strategies on their portfolio.

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