Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a system for assessment of financial assets and particularly to a system for evaluating performance of Socially Responsible Investment (SRI) mutual funds.
Description of Related Art
[002] Socially Responsible Investment (SRI) has seen growing interest among investors who seek ethical and impact-oriented portfolios. These funds claim alignment with social values and long-term sustainability, appealing to individuals and institutions focused on more than just financial returns. Despite the rise in popularity of SRI mutual funds, the investment community lacks consensus on how to verify and evaluate the actual adherence of these funds to socially responsible principles.
[003] Existing financial analysis tools and rating systems offer limited capabilities when applied to SRI mutual funds. Most available solutions concentrate on general financial performance, without accounting for non-financial factors that drive socially responsible decisions. In many cases, the data used for analysis lacks consistency, reliability, or a clear standard for validation. Reports often depend on self-disclosed information, which varies in quality and structure across different funds and markets.
[004] Current industry practices do not address the challenges of verifying the authenticity of such data or comparing reports across regions. Regulatory frameworks remain fragmented, and there is no unified method that enables investors to assess the trustworthiness and impact of SRI mutual funds.
[005] There is thus a need for an improved and advanced system for evaluating performance of Socially Responsible Investment (SRI) mutual funds that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention further provide a method for evaluating performance of Socially Responsible Investment (SRI) mutual funds. The method comprising steps of receiving an analytical input from a user; fetching data from predefined sources relating to the received analytical input; normalizing the fetched data for maintaining a compatibility and a comparison among the data; detecting anomalies in the normalized data by execution of machine learning algorithms; extracting textual data from the normalized data by execution of a natural language processing (NLP) to supplement quantitative analysis with the normalized data; generating automated reports aligned with the normalized data and the analytical input of the user; and enabling a user to view, edit, share, or a combination thereof, the generated automated reports.
[007] Embodiments in accordance with the present invention provide a system for evaluating performance of Socially Responsible Investment (SRI) mutual funds. The system comprising an input unit configured to receive an analytical input from a user. The system further comprising a processing unit connected to the input unit. The processing unit is configured to fetch data from predefined sources including investor sentiments, market trends, financial reports, Environmental-Social-Governance (ESG) data, or a combination thereof; normalize the fetched data for maintaining a compatibility and a comparison among the data; detect anomalies in the normalized data by execution of machine learning algorithms; extract textual data from the normalized data by execution of a natural language processing (NLP) to supplement quantitative analysis with the normalized data; generate automated reports aligned with the normalized data and the analytical input of the user; and enable a user to view, edit, share, or a combination thereof, the generated automated reports, wherein the generated automated reports are displayed on an interactive dashboard installed in an output unit.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for evaluating performance of Socially Responsible Investment (SRI) mutual funds.
[009] Next, embodiments of the present application may provide a system for evaluating performance of SRI mutual funds that enables identification and validation of genuine data sources, thereby reducing the risk of misinformation or manipulation in the performance reporting of SRI mutual funds.
[0010] Next, embodiments of the present application may provide a system for evaluating performance of SRI mutual funds that minimizes manual errors and facilitates the generation of standardized reports that align with global practices.
[0011] Next, embodiments of the present application may provide a system for evaluating performance of SRI mutual funds that allows for comprehensive risk assessment and performance tracking, offering deeper insights into fund behavior and reliability.
[0012] Next, embodiments of the present application may provide a system for evaluating performance of SRI mutual funds that may allow stakeholders to receive timely alerts about anomalies, performance changes, or compliance issues.
[0013] Next, embodiments of the present application may provide a system for evaluating performance of SRI mutual funds that empowers investors by presenting clear, consistent, and transparent data, supporting more informed and confident investment decisions in socially responsible funds.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding 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.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1A illustrates a block diagram of a system for evaluating performance of Socially Responsible Investment (SRI) mutual funds, according to an embodiment of the present invention;
[0018] FIG. 1B illustrates a schematic diagram of the system, according to an embodiment of the present invention. In an embodiment of the present invention; and
[0019] FIG. 2 depicts a flowchart of a method for evaluating the performance of Socially Responsible Investment (SRI) mutual funds, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1A illustrates a block diagram of a system 100 for evaluating performance of Socially Responsible Investment (SRI) mutual funds, according to an embodiment of the present invention. The system 100 may be adapted to scrutinize the SRI mutual funds for inspection of fraud and forgery. Further, the system 100 may be adapted to assess a performance, look into past trends, and predict a future growth of the SRI mutual funds. The system 100 may be deployed on a cloud platform (not shown) to enable a remote access and a scalability of data processing.
[0025] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise an input unit 102, a processing unit 104, and an output unit 106. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0026] In an embodiment of the present invention, the input unit 102 may be adapted to receive an analytical input from a user. The analytical input may be, but not limited to, analysis of insights, a generation of performance graphs, a fraud detection, a performance prediction, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the analytical input, including known, related art, and/or later developed technologies.
[0027] In another embodiment of the present invention, the input unit 102 may be adapted to receive predefined sources from the user. The predefined sources may be adapted to provide data relating to the received analytical input. The predefined sources may be, but not limited to, investor sentiments, market trends, financial reports, Environmental-Social-Governance (ESG) data, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the predefined sources, including known, related art, and/or later developed technologies. The format of the predefined sources may be, but not limited to, a weblink, an article, an image, an infographic, a document, an internal circular, and so forth. Embodiments of the present invention are intended to include or otherwise cover any format of the predefined sources, including known, related art, and/or later developed technologies.
[0028] The input unit 102 may be, but not limited to, a laptop, a tablet, a smartphone, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the input unit 102, including known, related art, and/or later developed technologies.
[0029] In an embodiment of the present invention, the processing unit 104 may be configured to receive the analytical input from the user. In an embodiment of the present invention, the processing unit 104 may be configured to fetch data from the predefined sources relating to the received analytical input. In an embodiment of the present invention, the processing unit 104 may be configured to normalize the fetched data for maintaining a compatibility and a comparison among the data. The normalization may be adapted to apply standard data cleaning and transformation procedures to harmonize formats and units in the fetched data.
[0030] In an embodiment of the present invention, the processing unit 104 may be configured to detect anomalies in the normalized data by execution of machine learning algorithms. The machine learning algorithms may be configured to detect anomalies in the fetched data to enhance fraud detection. Further, the machine learning algorithms may be configured to conduct a risk assessment of the SRI mutual funds. The machine learning algorithms may be trained using a big data analytics engine. The big data analytics engine may ensure a transparency in operations of the machine learning algorithms, thereby ensuring a transparency in the operations of the system 100.
[0031] In an embodiment of the present invention, the processing unit 104 may be configured to extract textual data from the normalized data by execution of a natural language processing (NLP) to supplement quantitative analysis with the normalized data. In an embodiment of the present invention, the processing unit 104 may be configured to generate automated reports aligned with the normalized data and the analytical input of the user.
[0032] In an embodiment of the present invention, the processing unit 104 may be configured to enable an alert unit 110 to generate notifications relating to a behavior of the SRI mutual funds, a detection of potential compliance breaches, significant performance deviations, and so forth. The notification generated by the alert unit 110 may be transmitted to the output unit 106. The notification received on the output unit 106 may be in a pre-defined form, in an embodiment of the present invention. According to embodiments of the present invention, the pre-defined form of the notification received on the output unit 106 may be, but not limited to a pop-up notification, a flash notification, a ringer notification, a silent notification, a push notification, a hidden notification, an electronic mail notification, a Short Message Service (SMS) notification, an always on-screen notification, and so forth. Embodiments of the present invention are intended to include or otherwise cover any pre-defined form of the notification that may be received on the output unit 106, including known, related art, and/or later developed technologies.
[0033] In an exemplary if a user ‘X’ may want to invest a sum of INR 100000 in the SRI funds to purchase a car after a year. Here, the analytical input may be the sum of INR 100000 and time period of the year. The system 100 may receive the analytical input and may search the predefined sources to fetch data related to an average amount of cars. Further, if the average amount of the car may be INR 700000, then the system 100 may further search the predefined sources a list of the SRI mutual funds that may be capable of turning INR 100000 into INR 700000 in a time period of year.
[0034] The search of the predefined sources may result in graphs, tables, articles, testimonials from past investors, and so forth. The system 100 may collect and normalize the results. Further, upon normalization, the system 100 may detect anomalies (in other words, that segment of normalized data that may be fake, providing false values, appearing too good to be true, and so forth). The anomalies in the normalized data may be eliminated, and the natural language processing (NLP) may be executed to extract text in the normalized data. The extracted text may be used as supplementary data for explaining and supporting conclusions provided by the system to the user ‘X’.
[0035] Further, the system 100 may use the normalized data and may generate an exemplary report containing a graph depicting the list of the SRI mutual funds that may be considered by the user ‘X; for investing INR 100000 that may be tuned into INR 700000 in the time period of the year. Moreover, the exemplary report may further contain a table representing a breakdown of INR 100000 along with monthly and/or quarterly increments that may later be appreciated to INR 700000. The exemplary report may further contain explanatory snippets and informational digests that may explain about terms, concepts, logics, and so forth presented in the exemplary report.
[0036] FIG. 1B illustrates a schematic diagram of the system, according to an embodiment of the present invention. In an embodiment of the present invention. As shown in FIG. 1B, the input unit 102 may enable the reception of data from the predefined sources such as the financial reports, the ESG data, the market trends, and/or the investor sentiments, which collectively form the analytical foundation for the system 100. The processing unit 104 may utilize the received data to perform multiple core functions, including risk assessment, big data analytics, machine learning, and natural language processing (NLP). These capabilities enable the system 100 to identify patterns, detect potential fraud, and assess fund performance against risk parameters. The processing unit 104 also facilitates data normalization to ensure consistency across sources and supports integration with global standards for better alignment with compliance and regulatory frameworks. The normalized and enriched data is then used for performance measurement and automated report generation within the processing unit 104, the results of which are transferred to the output unit 106. The output unit may include the interactive dashboard 108, that may present summary insights or investment suggestions; compliance reporting tools for regulatory alignment; and performance metrics dashboards to evaluate fund behavior over time. Additionally, the alert unit 110 may generate the real-time notifications related to anomalies, compliance breaches, or significant deviations from expected performance. These notifications may be delivered in various formats, such as pop-ups, emails, or push notifications, ensuring that the user remains informed and responsive to critical insights generated by the system 100.
[0037] FIG. 2 depicts a flowchart of a method 200 for evaluating performance of the SRI mutual funds, according to an embodiment of the present invention.
[0038] At step 202, the system 100 may receive the analytical input from the user.
[0039] At step 204, the system 100 may fetch the data from the predefined sources relating to the received analytical input.
[0040] At step 206, the system 100 may normalize the fetched data for maintaining the compatibility and the comparison among the data.
[0041] At step 208, the system 100 may detect the anomalies in the normalized data by execution of the machine learning algorithms.
[0042] At step 210, the system 100 may extract the textual data from the normalized data by executing the natural language processing (NLP) to supplement quantitative analysis with the normalized data.
[0043] At step 212, the system 100 may generate the automated reports aligned with the normalized data and the analytical input of the user.
[0044] At step 214, the system 100 may enable the user to view, edit, or share the generated automated reports.
[0045] At step 216, the system 100 may generate the notifications relating to the behavior of the SRI mutual funds, a detection of potential compliance breaches, significant performance deviations, and so forth.
[0046] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to 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.
[0047] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A method (200) for evaluating performance of Socially Responsible Investment (SRI) mutual funds using a system (100), the method (200) is characterized by steps of:
receiving an analytical input from a user using an input unit (102);
fetching data from predefined sources relating to the received analytical input;
normalizing, using a processing unit (104), the fetched data for maintaining a compatibility and a comparison among the data;
detecting anomalies in the normalized data by execution of machine learning algorithms;
extracting textual data from the normalized data by execution of a natural language processing (NLP) to supplement quantitative analysis with the normalized data;
generating, using an output unit (106), automated reports aligned with the normalized data and the analytical input of the user; and
enabling a user to view, edit, share, or a combination thereof, the generated automated reports.
2. The method (200) as claimed in claim 1, comprising a step of generating notifications relating to a behavior of the SRI mutual funds, a detection of potential compliance breaches, significant performance deviations, or a combination thereof.
3. The method (200) as claimed in claim 1, wherein the analytical input is selected from analysis of insights, a generation of performance graphs, a fraud detection, a performance prediction, or a combination thereof.
4. The method (200) as claimed in claim 1, wherein the predefined sources are selected from investor sentiments, market trends, financial reports, Environmental-Social-Governance (ESG) data, or a combination thereof.
5. The method (200) as claimed in claim 1, wherein the generated automated reports comprise performance metrics, a predictive performance, or a combination thereof relating to the SRI mutual funds.
6. The method (200) as claimed in claim 1, wherein the machine learning algorithms are trained to detect anomalies in the fetched data to enhance fraud detection.
7. The method (200) as claimed in claim 1, wherein the normalization is adapted to apply standard data cleaning and transformation procedures to harmonize formats and units in the fetched data.
8. A system (100) for evaluating performance of Socially Responsible Investment (SRI) mutual funds, the system (100) comprising:
an input unit (102) configured to receive an analytical input from a user; and
a processing unit (104) connected to the input unit (102), characterized in that the processing unit (104) is configured to:
fetch data from predefined sources including investor sentiments, market trends, financial reports, Environmental-Social-Governance (ESG) data, or a combination thereof;
normalize the fetched data for maintaining a compatibility and a comparison among the data;
detect anomalies in the normalized data by execution of machine learning algorithms;
extract textual data from the normalized data by execution of natural language processing (NLP) to supplement quantitative analysis with the normalized data;
generate automated reports aligned with the normalized data and the analytical input of the user; and
enable a user to view, edit, share, or a combination thereof, the generated automated reports, wherein the generated automated reports are displayed on an interactive dashboard (108) installed in an output unit (106).
9. The system (100) as claimed in claim 8, comprising an alert unit (110) adapted to generate notifications relating to a behavior of the SRI mutual funds, a detection of potential compliance breaches, significant performance deviations, or a combination thereof.
10. The system (100) as claimed in claim 8, wherein the machine learning algorithms are trained to detect anomalies in fetched data to enhance fraud detection.
Date: April 22, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant