GB2624640A

GB2624640A - Methods and systems of providing a valuation for businesses

Info

Publication number: GB2624640A
Application number: GB2217481.7A
Authority: GB
Inventors: Babayev Dmytro; Gorgadze Tatiane
Original assignee: Upswot Inc
Current assignee: Upswot Inc
Priority date: 2022-11-22
Filing date: 2022-11-23
Publication date: 2024-05-29
Also published as: GB202217481D0; CA3184864A1

Abstract

Business valuation method and system 300 comprises: receiving a request for assessing a value of a business from a device 302; analysing the request 304; identifying business-related data sources associated with the business based on the analysing of the request 306; determining a connection status (e.g. a connected state or an unconnected state) of each of the plurality of business-related data sources based on the identifying 308; executing a business valuation model selection algorithm, wherein the business valuation model selection algorithm is configured for selecting one of a plurality of business valuation models for assessing the value of the business based on the connection status of each of the business-related data sources 310; generating a business valuation report for the business under the business valuation models based on the selecting (312, Fig. 3B); transmitting the report to the device (314, Fig. 3B); and storing the report (316, Fig. 3B). The report may be provided to the user (1212, Fig. 12). The business-related data sources may comprise a blockchain ledger.

Description

TITLE

METHODS AND SYSTEMS OF PROVIDING A VALUATION FOR

BUSINESSES

FIELD OF DISCLOSURE

The present disclosure generally relates to data processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory, or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory, or forecasting purposes. More specifically, the present disclosure relates to methods and systems of providing a valuation for businesses.

BACKGROUND

Generally, business value is a vital parameter for business owners and managers. Increasing this value is a target for many companies, as a profit is just an increase in the company's book value. Investors, banks, and insurers are very interested in this calculation to understand, whether this company is worth dealing with or not. Doing the right things to increase a company's value is one of the most essential items that have to be a top priority for modern business owners. But to increase this value, they should first of all measure it.

However, the process of business valuation is complex and time-consuming. In most cases, a business valuation is performed manually by an analyst, or a certified person, using a combination of judgment, knowledge, experience, and an understanding of generally accepted valuation principles. While performing a business valuation, the analyst may select a valuation type and one or more methods to determine the business value. For example, the reliance on the analyst's selection places a heavy reliance on the knowledge and experience of the analyst. Since the process is data-dependent, the analyst has to spend a great deal of time identifying, extracting, aggregating, verifying, and interpreting the data from various sources as part of the valuation process. Further, a small change in the input data may result in significant changes in the value of the business. Furthermore, the analyst needs to work on electronic spreadsheets and prepare complicated models to solve problems associated with business valuation.

The field relating to resources, workflows, human or project management, e.g. organizing, planning, scheduling or allocating time, human or machine resources; Enterprise planning; organizational models is technologically important to several industries, business organizations, and/or individuals. Existing techniques of providing a valuation for businesses are deficient in several ways. Thus, there exists a need for a transparent and automated computer-based method that mitigates at least some of the disadvantages of state-of-the-art.

Therefore, there is a need for methods and systems of providing a valuation for businesses that may overcome one or more of the above-mentioned problems and/or limitations.

SUMMARY OF DISCLOSURE

This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter's scope.

The present disclosure provides a method of providing a valuation for businesses. Further, the method may include receiving, using a communication device, one or more requests for assessing a value of one or more businesses from one or more devices. Further, the method may include analyzing, using a processing device, the one or more requests. Further, the method may include identifying, using the processing device, two or more business-related data sources associated with the one or more businesses based on the analyzing of the one or more requests. Further, the method may include determining, using the processing device, a connection status of each of the two or more business-related data sources based on the identifying. Further, the method may include executing, using the processing device, a business valuation model selection algorithm. Further, the business valuation model selection algorithm may be configured for selecting one of two or more business valuation models for assessing the value of the one or more businesses based on the connection status of each of the two or more business-related data sources. Further, the method may include generating, using the processing device, one or more business valuation reports for the one or more businesses under one of the two or more business valuation models based on the selecting. Further, the method may include transmitting, using the communication device, the one or more business valuation reports to the one or more devices. Further, the method may include storing, using a storage device, the one or more business valuation reports.

The present disclosure provides a system for providing a valuation for businesses. Further, the system may include a communication device. Further, the communication device may be configured for receiving one or more requests for assessing a value of one or more businesses from one or more devices. Further, the communication device may be configured for transmitting one or more business valuation reports to the one or more devices. Further, the system may include a processing device. Further, the processing device may be configured for analyzing the one or more requests. Further, the processing device may be configured for identifying two or more business-related data sources associated with the one or more businesses based on the analyzing of the one or more requests. Further, the processing device may be configured for determining a connection status of each of the two or more business-related data sources based on the identifying. Further, the processing device may be configured for executing a business valuation model selection algorithm. Further, the business valuation model selection algorithm may be configured for selecting one of two or more business valuation models for assessing the value of the one or more businesses based on the connection status of each of the two or more business-related data sources. Further, the processing device may be configured for generating the one or more business valuation reports for the one or more businesses under one of the two or more business valuation models based on the selecting. Further, the system may include a storage device that may be configured for storing the one or more valuation reports.

Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

BRIEF DESCRIPTIONS OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure.

Fig. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure.

Fig. 2 is a block diagram of a computing device 200 for implementing the methods disclosed herein, in accordance with some embodiments.

Fig. 3A illustrates a flowchart of a method 300 of providing a valuation for businesses, in accordance with some embodiments.

Fig. 33 illustrates a continuation of the flowchart of the method 300 of providing a valuation for businesses, in accordance with some embodiments.

Fig. 4 illustrates a flowchart of a method 400 of providing a valuation for businesses including receiving, using the communication device 1002, at least one business-related data from at least one of the plurality of business-related data sources based on the establishing of the connection, wherein the business valuation model selection algorithm is further configured for analyzing the at least one business-related data, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one business-related data, in accordance with some embodiments.

Fig. 5 illustrates a flowchart of a method 500 of providing a valuation for businesses including analyzing the at least one value of the at least one model selecting parameter based on the generating of the at least one value for the at least one model selecting parameter, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one value of the at least one model selecting parameter, in accordance with some embodiments.

Fig. 6 illustrates a flowchart of a method 600 of providing a valuation for businesses including calculating, using the processing device 1004, at least one valuation parameter associated with the value of the at least one business under one of the plurality of business valuation models using the at least one calculation formula and the at least one business-related source data, wherein the generating of the at least one business valuation report for the at least one business is further based on the calculating, in accordance with some embodiments.

Fig. 7 illustrates a flowchart of a method 700 of providing a valuation for businesses including receiving, using the communication device 1002, at least one authorization for connecting to the at least one business-related data source from the at least one device, wherein the establishing of the connection to at least one of the plurality of business-related data sources comprises establishing the connection to the at least one business-related data source based on the at least one authorization, in accordance with some embodiments.

Fig. 8 illustrates a flowchart of a method 800 of providing a valuation for businesses including transmitting, using the communication device 1002, the accuracy to the at least one device, in accordance with some embodiments.

Fig. 9 illustrates a flowchart of a method 900 of providing a valuation for businesses including receiving, using the communication device 1002, at least one input corresponding to the at least one input prompt from the at least one device, wherein the business valuation model selection algorithm is further configured for analyzing the at least one input, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one input, in accordance with some embodiments.

Fig. 10 illustrates a block diagram of a system 1000 of providing a valuation for businesses, in accordance with some embodiments.

Fig. 11 illustrates a block diagram of the system 1000 of providing a valuation for businesses, in accordance with some embodiments.

FIG. 12 illustrates a flowchart of a method 1200 for providing an estimated business valuation of businesses, in accordance with some embodiments.

FIG. 13 illustrates a flow diagram of an algorithm 1300 for business valuation model selection for providing the estimated business valuation of the business, in accordance with some embodiments.

FIG. 14 illustrates a block diagram of an application 1400 for providing the estimated business valuation of the business, in accordance with some embodiments.

FIG. 15 illustrates a workflow of a token-based authorization method 1500 for secure data transfer, in accordance with some embodiments.

FIG. 16 illustrates a valuation report for providing the valuation of the business, in accordance with an exemplary embodiment.

DETAILED DESCRIPTION OF DISCLOSURE

As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being "preferred" is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and/or issuing here from that does not explicitly appear in the claim itself.

Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein as understood by the ordinary artisan based on the contextual use of such term differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

Furthermore, it is important to note that, as used herein, "a" and "an" each generally denotes "at least one," but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, "or" denotes "at least one of the items," but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, 'and" denotes "all of the items of the list." The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and/or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

S

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of the disclosed use cases, embodiments of the present disclosure are not limited to use only in this context.

In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device and at least one sensor. Examples of the one or more client devices and/or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smartphone, an Internet of Things (loT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g., a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server, etc.), a quantum computer, and so on. Further, one or more client devices and/or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g., Windows, Mac OS, Unix, Linux, Android, etc.) in order to provide a user interface (e.g., GUI, touch-screen based interface, voice-based interface, gesture-based interface, etc.) for use by the one or more users and/or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, assessing, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third-party database, a public database, a private database, and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and/or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and/or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role-based access control, and so on.

Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled and/or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human-readable secret data (e.g., username, password, passphrase, PIN, secret question, secret answer, etc.) and/or possession of a machine readable secret data (e.g., encryption key, decryption key, bar codes, etc.) and/or possession of one or more embodied characteristics unique to the user (e.g., biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and/or possession of a unique device (e.g., a device with a unique physical and/or chemical and/or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP/MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g., transmitting and/or receiving) with one or more sensor devices and/or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human-readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.

Further, one or more steps of the method may be automatically initiated, maintained and/or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, and identity of a user associated with a device (e.g., the server computer, a client device, etc.) corresponding to the performance of the one or more steps, environmental variables (e.g., temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and/or physiological state and/or psychological state of the user, physical state (e.g., motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and/or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and/or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g., a real-time clock), a location sensor (e.g., a GPS receiver, a GLONASS receiver, an indoor location sensor, etc.), a biometric sensor (e.g., a fingerprint sensor), an environmental variable sensor (e.g., temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g., a power sensor, a voltage/current sensor, a switch-state sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).

Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.

Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more and devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and/or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and/or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated using the processing device, based on the correlation.

Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data therebetween corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and/or a derivative thereof may be performed at the client device.

Overview: The present disclosure describes methods and systems of providing a valuation for businesses Further, the present disclosure describes methods and systems for estimated business valuation.

Further, the present disclosure relates to the field of data processing, in particular, to apparatuses, methods, and storage mediums associated with determining a valuation of one or more businesses (companies) based on economic performance metric values.

Further, the present disclosure describes a valuation method. Further, the valuation method provides an easy and almost instant way of understanding the estimated value in exchange for the information business owners provide from the business data sources used to run their business method is transparent and based on long-term research.

The valuation process tells the owner the current value of their business by analyzing all aspects of the business, which could include the company's management, capital structure, future earnings, and the market value of its assets. When a company is ready to go through a business valuation, there are three significant approaches. Each has benefits to consider, so it's wise to evaluate which is best for you and your business.

An asset-based approach totals up all of the investments in the company to determine the value of the business. When a business owner chooses an asset-based approach, all of the investments will be totaled up in one of two ways. The first way concerns the asset-based approach, also known as book value, which will review your company's balance sheet, list the business' total assets and subtract its total liabilities.

A liquidation asset-based approach is used when determining the liquidation value or net cash value of your business if all your assets were sold and liabilities paid off. This is a common approach for business owners who are looking to sell their business or get out from under it. The earning value approach evaluates businesses based on their ability to produce wealth in the future. This approach is generally used for a company that is looking to buy or merge with another company. There are two types of earning value approaches: Capitalizing past earnings and discounted future earnings.

Capitalizing past earnings method reports the company's usage of past earnings, normalizes them, then multiplies the expected normalized cash flows by a capitalization factor. This rate is what a reasonable purchaser would expect in their investment in the business.

Discounted future earnings. This approach averages the trend of predicted future earnings for the company, then divides it by the same capitalization factor. Market value approaches when assessing the market value of their business, owners establish what the business is worth based on similar businesses that have recently been sold. This sometimes leads to a business being under or overvalued. If the business and its assets are worth about $5 million, but similar companies have been sold in the $ 2 million range, the owner may lose money on the sale. Earning value approaches are the most popular means of business valuation, but that doesn't mean it's the right choice for users. The best way to get the fairest valuation is to hire an experienced business valuator to advise you on the best methods of how to evaluate your business. Combining these three methods may be the best way to get a fair and accurate value for your company.

Relatively easy to be confused with all the above-mentioned methods, so described method proposes business owners look at the public market with a lot of everyday deals with purchasing shares similar to their companies. So to evaluate, we analyze these deals and just give them an abstract in industry multipliers to their primary financial data, such as sales, EBITDA, etc. With the understanding that there are no totally similar companies, described method and system give users as well an easy way to find a more accurate peer throughout the public market with a comparison algorithm.

Further, the present disclosure describes a computer-implemented method of providing an estimated business valuation. The method may include connecting external information data sources to a computer system. The method may further include receiving a business-related data by the computer system. The method may further include determining an applied business valuation model depending on the received business-related data and according to a business valuation model selection algorithm. The method may further include business valuation calculating based on received business-related data and applied business valuation model. The method may further include providing a business valuation report to the user.

Further, the business valuation model selection algorithm in a first iteration provides for checking the connected external business-related data sources and, if no data sources are connected, displaying approximate valuation with sales and industry from initial data, receiving a user-input Price/Sales and industry data, and then applies a Price/Sales valuation model.

Further, the business valuation model selection algorithm in a second iteration includes checking the connected external business-related data sources, and if at least one e-commerce data source is connected, the business valuation model selection algorithm provides for determining a revenue rate, and If received revenue rate is positive then applies the Price/Sales valuation model with exact revenue rate. If received churn revenue is, the business valuation model selection algorithm provides for valuation using a DCF valuation model with manual revenue forecast from the user.

Further, the business valuation model selection algorithm in a third iteration includes checking the connected external business-related data sources, and if at least one accounting data source is connected, the business valuation model selection algorithm provides for determining a net profit margin rate, and if received net profit margin rate is positive then applies an EV/EBITDA model. If the received net profit margin rate is negative, the algorithm returns to the step of determining the revenue rate, and if the received revenue rate is positive, then applies the Price/Sales valuation model. If received a churn revenue rate, then applies the DCF valuation model; Further, the method describes a calculation formula for approximate valuation with sales and industry from initial data as follows: (Median value for Revenue range) * (Average value of a Price/Sales ratio for similar businesses in the same industry sector). Further, an average value of the Price/Sales ratio for similar businesses in the same industry sector retrieves from open sources.

Further, the calculation formula for the Price/Sales valuation model is as follows: (Revenue range) * (Average value of P/S ratio for similar businesses in the same industry sector). Further, an average value of the Price/Sales ratio for similar businesses in the same industry sector retrieves from open sources.

Further, the method describes a calculation formula for the DCF valuation model as follows: (Sum of all forecasted values for three years period) * (Discount Rate) + Terminal value as Sales of the final year * Discount rate of the last year *Current Price/Sales rate.

Further, the calculation formula for EV/EBITDA valuation model is as follows: EBITDA * EV/EBITDA ratio.

Further, the restructuring of the accessed business-related data from external data sources into a unified format includes analyzing a class, type, or subtype of each account from a plurality of accounts and recording the data into universal reference values.

Further, the restructuring of the accessed business-related data from the plurality of different data sources into the unified format further includes storing the restructured data in a universal, denormalized data structure.

Further, the stored restructured business-related data is categorized by external data source category in a columnar database.

Further, a valuation report provides information about sales, EBITDA, net income, free cash flow, book value, and net debt ratios.

Further, the valuation report provides an integral part of online banking services.

Further, the valuation report provides an integral part of the fintech services.

Further, the present disclosure describes a system for estimated business valuation. The system may include at least one processing unit and physical memory comprising computer-executable instructions (a programming module) that, when executed by the physical processor, cause the physical processor to receive business-related data from external data sources; determine the applied business valuation model depending on the received business-related data and according to the business valuation model selection algorithm; calculate business valuation based on received data and applied business valuation model and provide a business valuation report to the user.

Further, physical memory is a read-only type of memory comprising the operating system and program data.

Further, physical memory is a random-access type of memory comprising an operating system; and program data.

Further, the physical memory comprises a removable storage; a non-removable storage; input device(s), output device(s), and communication connection(s).

Further, the programming module comprises an application that contains a data accessing module, a data restructuring module, a data storage module, a calculation module, and an administration module.

The embodiments herein disclose methods and systems for estimated business valuation based on business-related data. The term "business valuation" as described herein refers to one or more of the company's (business) worth assessment, economic/intrinsic value of the company, capital markets, and expectation valuation, price-earnings valuation, stock valuation, value augmentation, decryption of price-earnings multiple of the company, decryption of price-earnings multiple of an index, share buyback, economic breakeven margin, value impact, etc. According to the official information, there were more than 4000 commercial banks and savings institutions in the United States. Massive competition in a conservative banking sector creates a need for banks to offer additional services to their clients in order not to lose them. Moreover, embedded financial services (modules) are becoming quite demanding and popular among financial institutions. Clients who own small and medium-sized businesses are especially interested in the claimed invention because attracting professional auditors is a relatively expensive service.

Thus, in the preferred embodiment, described method and the system can be provided to clients as an integral part of online banking services, fintech services, accounting, e-commerce, or other software used by the business owners (e.g., as a white-label solution from a third-party provider or provided directly). The system proposes that the client provide access to the business-related data and receive an estimated assessment of his business.

Further, the present disclosure describes a disclosed computer-implemented method for estimated business valuation. Further, the method may include connecting external information data sources to the computer system and receiving the business-related data. The method may include determining the applied business valuation model depending on the received data and, according to the business valuation model selection algorithm, the business valuation calculation based on the received data and applied business valuation model. Still, the method may include providing the business valuation report to the user.

FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. By way of a non-limiting example, the online platform 100 may be hosted on a centralized server 102, such as, for example, a cloud computing service. The centralized server 102 may communicate with other network entities, such as, for example, a mobile device 106 (such as a smartphone, a laptop, a tablet computer, etc.), other electronic devices 110 (such as desktop computers, server computers, etc.), databases 114, and sensors 116 over a communication network 104, such as, but not limited to, the Internet. Further, users of the online platform 100 may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers, and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

A user 112, such as the one or more relevant parties, may access online platform 100 through a web-based software application or browser. The web-based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 200.

With reference to FIG. 2, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device 200. In a basic configuration, computing device 200 may include at least one processing unit 202 and a system memory 204. Depending on the configuration and type of computing device, system memory 204 may comprise, but is not limited to, volatile (e.g., random-access memory (RAM)), non-volatile (e.g., read-only memory (ROM)), flash memory, or any combination. System memory 204 may include operating system 205, one or more programming modules 206, and may include a program data 207. Operating system 205, for example, may be suitable for controlling computing device 200's operation. In one embodiment, programming modules 206 may include an image-processing module, a machine-learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and are not limited to any particular application or system. This basic configuration is illustrated in FIG. 2 by those components within a dashed line 208.

Computing device 200 may have additional features or functionality. For example, computing device 200 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 2 by a removable storage 209 and a non-removable storage 210. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory 204, removable storage 209, and non-removable storage 210 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 200. Any such computer storage media may be part of device 200. Computing device 200 may also have input device(s) 212 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s) 214 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

Computing device 200 may also contain a communication connection 216 that may allow device 200 to communicate with other computing devices 218, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 216 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

As stated above, a number of program modules and data files may be stored in system memory 204, including operating system 205. While executing on processing unit 202, programming modules 206 (e.g., application 220 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 202 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.

Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

Embodiments of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the disclosure.

Fig. 3A and Fig. 3B illustrate a flowchart of a method 300 of providing a valuation for businesses, in accordance with some embodiments.

Accordingly, the method 300 may include a step 302 of receiving, using a communication device 1002, one or more requests for assessing a value of one or more businesses from one or more devices. Further, the one or more devices may include a computing device, a client device, etc. Further, the method 300 may include a step 304 of analyzing, using a processing device 1004, the one or more requests. Further, the method 300 may include a step 306 of identifying, using the processing device 1004, two or more business-related data sources associated with the one or more businesses based on the analyzing of the one or more requests. Further, the two or more business-related data sources may include one or more databases, one or more storage devices, one or more client device, one or more servers, etc. Further, the two or more business-related data sources may include two or more business-related data. Further, the two or more business-related data may include accounting data, e-commerce data, business industry type data, etc. Further, the method 300 may include a step 308 of determining, using the processing device 1004, a connection status of each of the two or more business-related data sources based on the identifying. Further, the connection status may include a connected state and an unconnected state. Further, data from the two or more business-related data sources may be accessible in the connected state and the data from the two or more business-related data sources may not be accessible in the unconnected state. Further, the method 300 may include executing, using the processing device 1004, a business valuation model selection algorithm. Further, the business valuation model selection algorithm may be configured for selecting one of two or more business valuation models for assessing the value of the one or more businesses based on the connection status of each of the two or more business-related data sources. Further, the method 300 may include a step 312 of generating, using the processing device 1004, one or more business valuation reports for the one or more businesses under one of the two or more business valuation models based on the selecting. Further, the method 300 may include a step 314 of transmitting, using the communication device 1002, the one or more business valuation reports to the one or more devices. Further, the one or more business valuation reports may include information about sales, EBITDA, net income, free cash flow, book value, and net debt ratios. Further, the method 300 may include a step 316 of storing, using a storage device 1006, the one or more business valuation reports. Further, the storing of the one or more business valuation reports may include storing the one or more business valuation reports in a blockchain ledger.

In some embodiments, the method 300 may further include initializing, using the processing device 1004, a connection with each of the two or more business-related data sources based on the identifying of the two or more business-related data sources. Further, the initializing the connection may include requesting an access to each of the two or more business-related data sources. Further, the two or more business-related data sources may accept the request based on a protocol of each of the two or more business-related data sources making the connection status, the connected state. Further, the two or more business-related data sources may reject the request based on the protocol of each of the two or more business-related data sources making the connection status, the unconnected state. Further, the determining of the connection status of each of the two or more business-related data sources is further based on the initializing.

Further, the method 400 may include a step 402 of establishing, using the processing device 1004, the connection with one or more of the two or more business-related data sources based on the initializing. Further, the establishing the connection may include setting up one or more data channels for communicating with one or more of the two or more business-related data sources. Further, the method 400 may include a step 404 of receiving, using the communication device 1002, one or more business-related data from one or more of the two or more business-related data sources based on the establishing of the connection. Further, the business valuation model selection algorithm may be configured for analyzing the one or more business-related data. Further, the selecting of one of the two or more business valuation models may be based on the analyzing of the one or more business-related data. Further, in an embodiment, the executing of the business valuation model selection algorithm may include executing at least one machine learning model. Further, the at least one machine learning model may be trained for the selecting of one of the two or more business valuation models for the assessing of the value of the one or more businesses based on the one or more business-related data. Further, the at least one machine learning model may include the business valuation model selection algorithm. Further, the one or more business-related data may have multiple data types. Further, the one or more business-related data may be unstructured data not having a pre-defined data model or not being organized in a pre-defined manner.

Further, the business valuation model selection algorithm in a step 502 may be configured for generating one or more values for one or more model selecting parameters based on the analyzing of the one or more business-related data. Further, the one or more value may be yes, no, etc. Further, the one or more model selecting parameters may include a positiveness of revenue rate, a positiveness of net profit marging, etc. of the one or more businesses. Further, the business valuation model selection algorithm in a step 502 may be configured for analyzing the one or more values of the one or more model selecting parameters based on the generating of the one or more values for the one or more model selecting parameters. Further, the selecting of one of the two or more business valuation models may be further based on the analyzing of the one or more values of the one or more model selecting parameters.

In some embodiments, the method 500 may further include structuring, using the processing device 1004, two or more data types of each of the one or more business-related data into a unified format. Further, the analyzing of the one or more business-related data is further based on the structuring. Further, the structuring may include restructuring the two or more data types.

Further, the method 600 may include a step 602 of identifying, using the processing device 1004, one or more calculation formulas for the assessing of the value of the one or more businesses based on the selecting of one of the two or more business valuation models. Further, the method 600 may include a step 604 of calculating, using the processing device 1004, one or more valuation parameters associated with the value of the one or more businesses under one of the two or more business valuation models using the one or more calculation formulas and the one or more business-related source data. Further, the one or more valuation parameters may include sales, EBITDA, net income, free cash flow, book value, net debt ratios, etc. Further, the generating of the one or more business valuation reports for the one or more business may be based on the calculating.

Further, the method 700 may include a step 702 of identifying, using the processing device 1004, one or more business-related data sources from the two or more business-related data sources based on the connection status of each of the two or more business-related data sources. Further, the method 700 may include a step 704 of generating, using the processing device 1004, one or more connection requests for connecting to the one or more business-related data sources based on the identifying of the one or more business-related data sources. Further, the method 700 may include a step 706 of transmitting, using the communication device 1002, the one or more connection requests to the one or more devices. Further, the method 700 may include a step 708 of receiving, using the communication device 1002, one or more authorizations for connecting to the one or more business-related data sources from the one or more devices. Further, the establishing of the connection to one or more of the two or more business-related data sources includes establishing the connection to the one or more business-related data sources based on the one or more authorizations.

Further, the method 800 may include a step 802 of analyzing, using the processing device 1004, the connection status of each of the two or more business-related data sources. Further, the method 800 may include generating, using the processing device 1004, an accuracy of the assessing based on the analyzing of the connection status. Further, the accuracy corresponds to a lack of access to a number of the one or more business-related data sources. Further, the identifying of the one or more business-related data sources may be based on the accuracy. Further, the method 800 may include a step 806 of transmitting, using the communication device 1002, the accuracy to the one or more devices.

Further, the business valuation model selection algorithm may be configured for generating one or more input prompts based on the connection status of each of the two or more business-related data sources. Further, the one or more input prompts may include approximate valuation with sales and industry from initial data. Further, the method 900 may include a step 902 of transmitting, using the communication device 1002, the one or more input prompts to the one or more devices. Further, the method 900 may include a step 904 of receiving, using the communication device 1002, one or more inputs corresponding to the one or more input prompts from the one or more devices. Further, the one or more inputs may include user-input Price/Sales and industry data associated with the one or more businesses. Further, the business valuation model selection algorithm may be configured for analyzing the one or more inputs. Further, the selecting of one of the two or more business valuation models may be based on the analyzing of the one or more inputs.

In some embodiments, one or more of the two or more business-related data sources may include one or more blockchain ledgers. Further, the one or more blockchain ledgers immutably store one or more business-related data.

Accordingly, the system 1000 may include a communication device 1002. Further, the communication device 1002 may be configured for receiving one or more requests for assessing a value of one or more businesses from one or more devices 1102, as shown in FIG. 11. Further, the communication device 1002 may be configured for transmitting one or more business valuation reports to the one or more devices 1102. Further, the system 1000 may include a processing device 1004 communicatively coupled with the communication device 1002. Further, the processing device 1004 may be configured for analyzing the one or more requests. Further, the processing device 1004 may be configured for identifying two or more business-related data sources associated with the one or more businesses based on the analyzing of the one or more requests. Further, the processing device 1004 may be configured for determining a connection status of each of the two or more business-related data sources based on the identifying. Further, the processing device 1004 may be configured for executing a business valuation model selection algorithm. Further, the business valuation model selection algorithm may be configured for selecting one of two or more business valuation models for assessing the value of the one or more businesses based on the connection status of each of the two or more business-related data sources. Further, the processing device 1004 may be configured for generating the one or more business valuation reports for the one or more businesses under one of the two or more business valuation models based on the selecting. Further, the system 1000 may include a storage device 1006 communicatively coupled with the processing device 1004. Further, the storage device 1006 may be configured for storing the one or more valuation reports.

In some embodiments, the processing device 1004 further may be configured for initializing a connection with each of the two or more business-related data sources based on the identifying of the two or more business-related data sources. Further, the determining of the connection status of each of the two or more business-related data sources is based on the initializing.

In some embodiments, the processing device 1004 further may be configured for establishing the connection with one or more of the two or more business-related data sources based on the initializing. Further, the communication device 1002 may be configured for receiving one or more business-related data from one or more of the two or more business-related data sources based on the establishing of the connection. Further, the business valuation model selection algorithm may be configured for analyzing the one or more business-related data. Further, the selecting of one of the two or more business valuation models is further based on the analyzing of the one or more business-related data.

Further, in some embodiments, the business valuation model selection algorithm may be configured for generating one or more values for one or more model selecting parameters based on the analyzing of the one or more business-related data. Further, the business valuation model selection algorithm may be configured for analyzing the one or more values of the one or more model selecting parameters based on the generating of the one or more values for the one or more model selecting parameters. Further, the selecting of one of the two or more business valuation models may be based on the analyzing of the one or more values of the one or more model selecting parameters.

In some embodiments, the processing device 1004 further may be configured for structuring two or more data types of each of the one or more business-related data into a unified format. Further, the analyzing of the one or more business-related data is further based on the structuring.

Further, in some embodiments, the processing device 1004 may be configured for identifying one or more calculation formulas for the assessing of the value of the one or more business based on the selecting of one of the two or more business valuation models. Further, the processing device 1004 may be configured for calculating one or more valuation parameters associated with the value of the one or more business under one of the two or more business valuation models using the one or more calculation formulas and the one or more business-related source data. Further, the generating of the one or more business valuation reports for the one or more businesses may be based on the calculating.

Further, in some embodiments, the processing device 1004 may be configured for identifying one or more business-related data sources from the two or more business-related data sources based on the connection status of each of the two or more business-related data sources. Further, the processing device 1004 may be configured for generating one or more connection requests for connecting to the one or more business-related data sources based on the identifying of the one or more business-related data sources. Further, the communication device 1002 may be configured for transmitting the one or more connection requests to the one or more devices 1102. Further, the communication device 1002 may be configured for receiving one or more authorizations for connecting to the one or more business-related data sources from the one or more devices 1102. Further, the establishing of the connection to one or more of the two or more business-related data sources includes establishing the connection to the one or more business-related data sources based on the one or more authorizations.

Further, in some embodiments, the processing device 1004 may be configured for analyzing the connection status of each of the two or more business-related data sources. Further, the processing device 1004 may be configured for generating an accuracy of the assessing based on the analyzing of the connection status. Further, the identifying of the one or more business-related data sources may be further based on the accuracy. Further, the communication device 1002 may be configured for transmitting the accuracy to the one or more devices 1102.

Further, in some embodiments, the business valuation model selection algorithm may be configured for generating one or more input prompts based on the connection status of each of the two or more business-related data sources. Further, the communication device 1002 may be configured for transmitting the one or more input prompts to the one or more devices 1102. Further, the business valuation model selection algorithm may be configured for generating one or more input prompts based on the connection status of each of the two or more business-related data sources. Further, the communication device 1002 may be configured for receiving one or more inputs corresponding to the one or more input prompts from the one or more devices 1102. Further, the business valuation model selection algorithm may be configured for analyzing the one or more inputs. Further, the selecting of one of the two or more business valuation models may be further based on the analyzing of the one or more inputs.

In some embodiments, one or more of the two or more business-related data sources may include one or more blockchain ledgers.

FIG. 12 illustrates a flowchart of a method 1200 for providing an estimated business valuation of businesses, in accordance with some embodiments. Accordingly, at 1202, the method 1200 may include connecting external information data sources to the system. Further, at 1204, the method 1200 may include receiving the business-related data. Further, at 1206, the method 1200 may include restructuring the different data types of business-related data into a standard, unified data format. Further, at 1208, the method 1200 may include determining the applied business valuation model depending on the received data. Further, at 1210, the method 1200 may include business valuation performing calculations based on received data and applied business valuation model. Further at 1212, the method 1200 may include providing a business valuation report to the user.

FIG. 13 illustrates a flow diagram of an algorithm 1300 for business valuation model selection for providing the estimated business valuation of the business, in accordance with some embodiments. Accordingly, at 1302, the algorithm 1300 starts. Further, the algorithm 1300 moves to 1304 after 1302. Further, the algorithm 1300 at 1304 checks connected data sources. If no data sources are connected in 1304, then the algorithm 1300 moves to 1306, and the algorithm 1300 at 1306 displays approximate valuation with sales and industry from initial data. Further, the algorithm 1300 moves to 1308 after 1306, and the algorithm 1300 at 1308 receives a user input price/sales and industry data. Further, the algorithm 1300 moves to 1310 after 1308, and the algorithm 1300 at 1310 selects the price/sales valuation model. Further, the algorithm 1300 moves to 1312 after 1310, and the algorithm 1300 terminates at 1310. Further, if the E-commerce data source is connected at 1304, then the algorithm 1300 moves to 1314 after 1304, and the algorithm 1300 at 1314 determines whether revenue rate is positive. If yes, the algorithm 1300 moves to 1310 after 1314. If no, the algorithm 1300 moves to 1316 after 1314, and the algorithm 1300 at 1316 forecasts user revenue. Further, the algorithm 1300 moves to 1318 after 1316, and the algorithm 1300 at 1318 selects the DCF valuation model. Further, the algorithm 1300 moves to 1312 after 1318. If the Accounting data source is connected at 1304, the algorithm 1300 moves to 1320 after 1304, and the algorithm 1300 at 1320 determines whether the net profit margin is positive. If no, the algorithm 1300 moves to 1314 after 1320. If yes, the algorithm 1300 moves to 1322 after 1320, and the algorithm 1300 at 1322 selects the EV/EBITDA valuation model. Further, the algorithm 1300 moves to 1310 after 1322.

FIG. 14 illustrates a block diagram of an application 1400 for providing the estimated business valuation of the business, in accordance with some embodiments. Further, the application 1400 may include a data accessing module 1402, a data restructuring module 1403, a data storage module 1404, a calculation module 1405, and an administration module 1406. Further, the data accessing module 1402 may access various types of data from an external data sources 1401.

With reference to FIGs. 12, 13, and 14, the method 1200 starts with connecting external business-related data sources to the system at 1202. Business-related data is needed to assess the value of the business and determine the valuation model and refers to information such as sales data, cash flow drivers, balance sheet information such as cash and securities, investment and other assets, debt and obligations, carry forward losses, outstanding shares, debt equity ratio, cost of debt, etc. It has to be noted that the value of one or more value drivers may be a prior period value or maybe a predicted value.

The business-related data including accounting, e-commerce, and business industry type data, that in the preferred embodiment may be provided by third-party data sources or input manually by the user.

Accounting data may include sales ratio, EBITDA, net income, free cash flow, book value, net debt ratio, carry forward losses, outstanding shares, investment, and other assets. Accounting data sources may be presented as third-party applications (e.g., but not limited to Bexio, Expensify, FreeAgent, FreshBooks, Invoiced, Kashoo, Practice Panther, QuickBooks, QuickBooks Desktop, Sage Business Accountancy, Wave, Xero applications, etc.).

E-commerce data may include price/sales ratio information, and E-Commerce data sources may be presented as third-party applications (e.g., but not limited to Big Cartel, 3dcart, Amazon, BigCommerce, eBay, Ecwid, Mercadolibre, Shopify, Squarespace, WooCommerce, etc.) Connecting data sources 1401 to the system at 1202 and receiving the business-related data at 1204 is realized by data accessing module 1402 of the system (as shown in FIG. 14). The data accessing module 1402 may access various types of business-related data from different data sources 1401. Within this rubric, connecting data sources may be realized by token-based authorization protocol (e.g., 0Auth 2.0 protocol) that enables business-related information from external data sources 1401 to be used by the computer system without exposing the user's data sources (application accounts) credentials to the computer system.

Upon accessing business-related data, the data restructuring module 1403 may restructure the different data types of business-related data into a standard, unified data format (at 1206). As will be understood, the various data sources 1401 may collect, organize, and store data in different formats that lack common accessibility. Accordingly, the data restructuring module 1403 may restructure some or all of the business-related data into a unified format.

After receiving the business-related data and restructuring it into a standard, unified data format, the calculation module 1405 determines the applied business valuation model depending on the received business-related data (at 1208). The business valuation model selection algorithm may apply three various business valuation models: Price-to-Sales (P/S) 1310, Enterprise value to earning before interest, taxes, depreciation, and amortization (EV/EBITDA) 1322, and Discounted cash flow (DCF) 1318 model.

Price-to-Sales (P/S) valuation model 1310 refers to the businesses at a late stage and is appropriate to be used if the business has managed to generate some sales for a few years. Thus, is a valuation ratio that compares a business's stock price to its revenues. The P/S model is a key analysis and valuation tool for investors and analysts. The ratio shows how much investors are willing to pay per dollar of sales. It can be calculated either by dividing the company's market capitalization by its total sales over a designated period (usually twelve months) or on a per-share basis by dividing the stock price by sales per share. The P/S ratio is most relevant when used to compare companies in the same sector. A low ratio may indicate the stock is undervalued, while a ratio significantly above the average may suggest overvaluation. The typical 12-month period used for sales in the P/S ratio is generally the past four quarters or the most recent or current fiscal year. A P/S ratio based on forecast sales for the current year is called a forward P/S ratio. To determine the P/S ratio, one must divide the current stock price by the sales per share.

Enterprise value to earning before interest, taxes, depreciation, and amortization (EV/EBITDA) 1322 valuation model is another known way to determine the value of a business. EV/EBITDA looks at a business the way a potential acquirer would by considering the business debt. Of course, to apply the EV/EBITDA valuation model, the company must have positive earnings. What's considered a "good" or "bad" enterprise multiple will depend on the industry. Investors mainly use a company's enterprise multiple to determine whether a company is undervalued or overvalued. A low ratio relative to peers or historical averages indicates that a company might be undervalued, and a high ratio indicates that the company might be overvalued. An enterprise multiple is helpful for international comparisons because it ignores the distorting effects of individual countries' taxation policies. It's also used to find attractive takeover candidates since enterprise value includes debt and is a better metric than market capitalization for merger and acquisition purposes. Enterprise multiples can vary depending on the industry. It is reasonable to expect higher enterprise multiples in high-growth industries (e.g., biotech) and lower multiples in industries with slow growth (e.g., railways). Enterprise value (EV) is a measure of the economic value of a company. It is frequently used to determine the value of the business if it is acquired. It is considered a better valuation measure for M&A than a market cap since it includes the debt an acquirer would have to assume and the cash they'd receive. To apply EV/EBITDA model 1322 system automatically retrieves the earnings and other financial data from the accounting data source 1401. In one embodiment, the amount of the sales may be denominated in mln of USD. The system automatically retrieves the industry sector, or the user has the possibility to input this information if the system finds no data for capturing, automatically retrieves the corresponding EV/EBITDA ratio per industry sector from open sources or internal database, and automatically retrieves the number of peer companies and automatically derive the value of the business based on the formula = EBITDA* EV/EBITDA ratio.

Discounted cash flow (DCF) 1318 is a valuation model used to estimate the value of an investment based on its expected future cash flows. DCF valuation model is usually used at a very early stage of investment or even before the company is started. DCF analysis attempts to figure out the value of an investment today, based on projections of how much money it will generate in the future. To apply this model, accounting data sharing is required. DCF analysis estimates the value of return that investment generates after adjusting for the time value of money. The DCF is often compared with the initial investment. If the DCF is greater than the present cost, the investment is profitable. The higher the DCF, the greater return the investment generates. Apply DCF 1318 valuation model system prompts the user to estimate the sales ratio for the next three years and estimate the profitability. Entered values are to be discounted using the sum of Risk-free Market Risk Premium rates. The calculation formula is as follows: Risk-free Rate + Market Risk Premium. The rates store in an internal database through data storage module 1404. The Risk-free Rate is the interest rate on Treasury bills (T-Bills) issued by the US government and published daily on the official government source (e.g., https://home.treasury. gov/resource-center/data-chart-center/interest-rates/TextView?type-dailytr easury_yield_curve&field_tdr_date_value=2022) from where can be obtained by the system. Thus, according to valuation theory, the Risk-Free Rate must be in accordance with the period in which Cash Flows are expected. In this case, decided that a 10-year Risk-free rate is appropriate, and the system may use the rate for the last month. Market Risk Premium is the difference between the expected return of the market portfolio and the Risk-free Rate. Risk-free Rates may be published in open sources (e.g., https://www.statista. com/statistics/664840/average-market-risk-premium-usa/). Within this rubric, the business value is calculated by the formula = (Sum of all forecasted values for three years period)* (Discount Rate) + Terminal value as Sales of the final year * Discount rate of the last year " Current P/S rate.

Each of the valuation models below has advantages and disadvantages, and the most optimal choice of model depends on the business-related data the system received from the client. The algorithm of a business valuation model selection is illustrated in FIG. 13. Thus, algorithm 1300 of a business valuation model selection is a computer-readable instruction, the main task of which is to apply the most appropriate method for business value estimating, depending on the business-related data available to the system.

Thus, there are three categories of clients: (a) those who haven't connected any data source; (b) who haven't connected their accounting data sources to the system and haven't provided access to their historical finances but connected other sources comprised revenues/sales data (e.g., e-commerce, Google Analytics, etc.); (c) those who have connected their accounting data sources to the system and provided access to their historical financial data. Generally, different business valuation models are offered for each type of client, defined above.

Within this rubric, the main idea of algorithm 1300 is to gradually move from less accurate to more accurate business-related data so that with each subsequent iteration of valuation, the system may provide a more and more accurate business valuation calculation based on received data and applied business valuation model.

Further, in the first iteration (at 1208), if the user does not provide any connection of business-related data and no data sources are connected, the system provides an approximate valuation with sales and industry from initial data (at 1304). For this purpose, the system, through the output device (e.g., user interface) and input device, proposes to input revenue range and industry-type data. Users may have at least two input fields for revenue range and industry sector for user-input Price/Sales and industry sector data (at 1306). Input fields may either be pre-filled if the system user has already input data during the onboarding/company registration process, or they may be filled manually, and both fields may have drop-down lists for respective values. For example, a drop-down list for revenue fields may display the following values (in one of the embodiments in USD): c 50,000; 50,001 -100,000; 100,001 -200,000; 200,001 -300,000; 300,001 -400,000; 400,001 -500,000; 500,001 -750,000; 750,001 -1,000,000; 1,000,001 -$3,000,000; 3,000,001 -$7,000,000; 7,000,000. Thus, such ranges fit small and medium businesses and may be changed by the system administrator through administration module 1406 if it is required.

In one of the embodiments, a drop-down list for the industry may display the sectors per NAICS or SIC classification. At this step, to increase the accuracy, calculation module 1405 may automatically retrieve and calculate an average value of the P/S ratio for the corresponding industry sector (at 1208). The calculation formula for approximate business value is as follows: (Median value for Revenue range)* (Average value of P/S ratio for similar businesses in the same industry sector). The average value of the P/S ratio for similar businesses in the same industry sector retrieves by the system from open sources (e.g., https://pages.stern.nyu.edu/-adamodar/New_Home_Page/datacurrent.html).

In the second iteration, (at 1208), for more accurate valuation, the system proposes user to connect business-related data sources (at 1302). If the user connects e-commerce data source 1401, related to the user's business, system, through data accessing module 1402, one of the embodiments may receive the exact revenue rate for the last fiscal year (at 1206). Thus, the minimum acceptable period of time for receiving the exact revenue rate is one quarter. If the received revenue rate is positive, the system provides a more accurate valuation using the Price/Sales model. The calculation formula is as follows: (Revenue range) * (Average value of P/S ratio for similar businesses in the same industry sector). The average value of the P/S ratio for similar businesses in the same industry sector retrieves by the system from open sources (e.g., https://pages.stern.nyu.edu/-adamodar/New_Home_Page/datacurrent.html).

If received a churn revenue (at 1208), the system provides valuation using the DCF valuation model 1318 by using manual revenue forecast 1316 from the user. In other embodiment, revenue forecast data may be retrieved by the system from another system or data source through API or any other known method. The system, through the output device (e.g., user interface) and input device, prompts the user to estimate the sales ratio for the next three years and estimate the profitability ratio. For this purpose, users may have two input fields for sales and profitability ratio.

In the third iteration (at 1208), if the user connects accounting data source 1401, related to the user's company and net profit is positive 1320, the system, through data accessing module 1402, may receive earnings before interest, taxes, depreciation, and amortization ratios (EBITDA). After EBITDA ratios are obtained, calculation module 1405 performs calculations by the EV/EBITDA calculation model. If the user connects accounting data source 1401, related to the user's company, and the net profit is negative, the system applies the second iteration of the algorithm, by determining the revenue rate at 1314 and applying the P/S valuation model 1310 or DCF valuation model 1318.

In the fourth iteration (at 1208), if the user connects accounting data source 1401, related to the user's company, system, through data accessing module 1402, receives earnings before interest, taxes, depreciation, and amortization ratios (EBITDA). After EBITDA ratios are obtained, and the net profit margin ratio 1320 received from the accounting data source 1401 is positive, calculation module 1405 performs calculations according to the EV/EBITDA calculation model. If the net profit margin ratio 1320 received from the accounting data source 1401 is negative, the algorithm returns to step 1314 determining of revenue ratio. If the received revenue rate is positive, the system provides the Price/Sales model valuation. If received a churn revenue ratio, the system provides valuation using the DCF valuation model 1318.

Further, in a preferred embodiment of the computer system application (such as the application 1400). Application may contain a data accessing module 1402, data restructuring module 1403, data storage module 1403, calculation module 1405, and administration module 1406. The data accessing module 1402 may access various types of data from external data sources 1401 (e.g., accounting, e-commerce applications, etc.). Each of these data stores may gather information from various ongoing operations. As such, the data may be live, upto-the-second data. In other cases, the data may be stored, as historical data related to any of the above data categories. Upon accessing business-related data, the data restructuring module 1403 may restructure the different types of data into a common, unified data format. As will be understood, the various external data sources 1401 may collect, organize, and store data in different manners that lack common accessibility. In 1404, the unified format data stored in a universal denormalized structure for storing standardized data is categorized by the data source category (e.g., in a columnar database management system (DBMS)).

FIG. 15 provides a preferred realization of step 1204 of receiving the business-related data. In the preferred embodiment, the token-based authorization method 1500 of FIG. 15 is the token-based authorization (e.g., 0Auth 2.0 protocol) that enables business-related information from one or more different external data sources to be used by the computer system without exposing external data sources 1401 (application accounts) credentials to the computer system. Further, client 1501 may send application data to an administrative computer system 1502 (step 1). The administrative computer system 1502 may redirect the application identifier to the client 1501 (step 2). Further, the client 1501 may then request API parameters (step 3), and the administrative computer system 1502 may redirect the requested API parameters to the client (step 4). Further, the client may then send a request for an authentication uniform resource locator (URL) to a backend API 1503 (step 5).

The backend API may then redirect the authentication URL to the client 1501 (step 6). The client 1501 may then send an authentication request to one or more applications 1504 that share data (step 7). Further, one or more applications 1504 may then provide a secret code to the client 1501 (step 8).

Further, the client 1501 may then redirect the secret code to the backend API 1503 (step 9). The backend API 1503 may then send a request for an authentication token to one or more applications 1504 (step 10). One or more applications 1504 may then return the requested authentication token to the backend API 1503 (step 11). The backend API 1503 may then send the authentication token to the client 1501 (step 12). Upon receiving the authentication token, the client 1501 may redirect the token to the administrative computer system 1502 (step 13). The administrative computer system 1502 may access data using the token through the API (step 14) and from one or more applications 1504 (step 15). Further, one or more applications 1504 may return the requested data through the backend API 1503 (step 16) to the administrative computer system 1502 (step 17). The administrative computer system 1502 may then redirect a congratulation or error page to client (business owner) 1505 (step 18) or send a "Data is received" message to client 1505 (step 19). Further, client 1505 may then request data from the administrative computer system 1502 (step 20), and the administrative computer system 1502 may respond with the requested data (step 21). In this manner, the underlying system may use tokens to safely and securely access information.

At 1404, the accessed business-related data may be stored in a universal denormalized structure for storing standardized data, categorized by the data source category (e.g., in a columnar database management system (DBMS)). In some cases, standardization of an entity's financial statements (e.g., balance sheet, profit, and loss statement) from different systems and different countries into a single, unified format may be performed by analyzing the class, type, and/or subtype of each account from an account source and recording the data into universal reference values.

The calculation module 1405 performs the functions of analyzing available business-related data and determining the applicable business valuation model (Price-to-Sales, EV/EBITDA, or DCF) based on algorithm 1300.

Administrative module 1406 drives the processes of collecting and storing business-related data, choosing of valuation model, changing coefficients, etc. Administrative module 1406 is programmatically linked to all components of the system. The administration module 1406 is typically accessed by a business valuation service provider or other organization that performs administrative functions.

FIG. 16 illustrates a valuation report 1602 for providing the valuation of the business, in accordance with an exemplary embodiment. Further, the valuation report 1602 may be generated when accounting data is provided by the business owner (at 1212). As shown, the report 1602 provides sales, EBITDA, net income, free cash flow, book value, and net debt ratios. In this case, according to algorithm 1300, the system provides valuation according to the EV/EBITDA model 1322. In addition to the valuation information, the system provides comparing the user's business with the peer companies operating in the same industry. A list of peers companies may be supposed to be retrieved from open sources (e.g. https://pages.stern.nyu.edu/-adamodar/New_Home_Page/datacurrent.html). Comparison between standalone metrics with respective ones may be expressed in percentage value as shown in FIG. 16. In case of missing data from the system user's side, an indication may recommend connecting app(s) is supposed to be in place.

According to some aspects, a computer-implemented method for estimated business valuation is disclosed. Further, the computer-implemented method may include connecting external information data sources to a computer system. Further, the computer-implemented method may include receiving business-related data by the computer system. Further, the computer-implemented method may include determining an applied business valuation model depending on the received business-related data and according to a business valuation model selection algorithm. Further, the computer-implemented method may include business valuation calculating based on received business-related data and applied business valuation model. Further, the computer-implemented method may include providing a business valuation report to the user.

Further, in some aspects, the business valuation model selection algorithm in a first iteration provides for checking the connected external business-related data sources and, if no data sources are connected, displaying approximate valuation with sales and industry from initial data, receiving a user-input Price/Sales and industry data, and then applies a Price/Sales valuation model.

Further, in some aspects, the business valuation model selection algorithm in a second iteration includes checking the connected external business-related data sources, and if at least one e-commerce data source is connected, the business valuation model selection algorithm provides for determining a revenue rate, and If received revenue rate is positive then applies the Price/Sales valuation model with exact revenue rate; If received churn revenue, the business valuation model selection algorithm provides for valuation using a DCF valuation model with manual revenue forecast from the user.

Further, in some aspects, the business valuation model selection algorithm in a third iteration includes checking the connected external business-related data sources, and if at least one accounting data source is connected, the business valuation model selection algorithm provides for determining a net profit margin rate, and if received net profit margin rate is positive then applies an EV/EBITDA model; If the received net profit margin rate is negative, the algorithm returns to the step of determining the revenue rate, and if the received revenue rate is positive, then applies the Price/Sales valuation model; If received a churn revenue rate, then applies the DCF valuation model; Further, in some aspects, a calculation formula for approximate valuation with sales and industry from initial data is as follows: (Median value for Revenue range) * (Average value of a Price/Sales ratio for similar businesses in the same industry sector); an average value of the Price/Sales ratio for similar businesses in the same industry sector retrieves from open sources.

Further, in some aspects, the calculation formula for the Price/Sales valuation model is as follows: (Revenue range)* (Average value of P/S ratio for similar businesses in the same industry sector); the average value of the Price/Sales ratio for similar businesses in the same industry sector retrieves from open sources.

Further, in some aspects, a calculation formula for the DCF valuation model is as follows: (Sum of all forecasted values for three years period) * (Discount Rate) + Terminal value as Sales of the final year * Discount rate of the last year " Current Price/Sales rate.

Further, in some aspects, the calculation formula for EV/EBITDA valuation model is as follows: EBITDA " EV/EBITDA ratio.

Further, in some aspects, restructuring the accessed business-related data from external data sources into a unified format includes analyzing a class, type, or subtype of each account from a plurality of accounts and recording the data into universal reference values.

Further, in some aspects, restructuring the accessed business-related data from the plurality of different data sources into the unified format further includes storing the restructured data in a universal, denormalized data structure.

Further, in an aspect, the stored restructured business-related data is categorized by external data source category in a columnar database.

Further, in some aspects, a valuation report provides information about sales, EBITDA, net income, free cash flow, book value, and net debt ratios.

Further, in an aspect, the valuation report provides an integral part of the online banking services.

Further, in an aspect, the valuation report provides an integral part of the fintech services.

According to some aspects, a system comprises at least one processing unit and a physical memory. Further, the physical memory comprises computer-executable instructions (a programming module) that, when executed by the physical processor. Further, the physical processor receives business-related data from external data sources. Further. the physical processor determines the applied business valuation model depending on the received business-related data and according to the business valuation model selection algorithm. Further, the physical processor calculates a business valuation based on received data and the applied business valuation model. Further, the physical processor provides a business valuation report to the user.

Further, in some aspects, physical memory is a read-only type of memory comprising: an operating system and a program data.

Further, in some aspects, physical memory is a random-access type of memory comprising: an operating system; and a program data.

Further, in some aspects, the physical memory comprises: a removable storage, a non-removable storage, input device(s), output device(s), and communication connection(s).

Further, in some aspects, the programming module comprises an application that contains a data accessing module, a data restructuring module, a data storage module, a calculation module, and an administration module.

Although the invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention as hereinafter claimed.

Claims

CLAIMS1. A method of providing a valuation for businesses, wherein the method comprises: receiving, using a communication device, at least one request for assessing a value of at least one business from at least one device; analyzing, using a processing device, the at least one request; identifying, using the processing device, a plurality of business-related data sources associated with the at least one business based on the analyzing of the at least one request; determining, using the processing device, a connection status of each of the plurality of business-related data sources based on the identifying; executing, using the processing device, a business valuation model selection algorithm, wherein the business valuation model selection algorithm is configured for selecting one of a plurality of business valuation models for assessing the value of the at least one business based on the connection status of each of the plurality of business-related data sources; generating, using the processing device, at least one business valuation report for the at least one business under one of the plurality of business valuation models based on the selecting; transmitting, using the communication device, the at least one business valuation report to the at least one device; and storing, using a storage device, the at least one business valuation report.
2. The method of claim 1 further comprising initializing, using the processing device, a connection with each of the plurality of business-related data sources based on the identifying of the plurality of business-related data sources, wherein the determining of the connection status of each of the plurality of business-related data sources is further based on the initializing.
3. The method of claim 2 further comprises: establishing, using the processing device, the connection with at least one of the plurality of business-related data sources based on the initializing; and receiving, using the communication device, at least one business-related data from at least one of the plurality of business-related data sources based on the establishing of the connection, wherein the business valuation model selection algorithm is further configured for analyzing the at least one business-related data, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one business-related data.
4. The method of claim 3 wherein the business valuation model selection algorithm is further configured for: generating at least one value for at least one model selecting parameter based on the analyzing of the at least one business-related data; and analyzing the at least one value of the at least one model selecting parameter based on the generating of the at least one value for the at least one model selecting parameter, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one value of the at least one model selecting parameter.
5. The method of claim 3 further comprising structuring, using the processing device, a plurality of data types of each of the at least one business-related data into a unified format, wherein the analyzing of the at least one business-related data is further based on the structuring.
6. The method of claim 3 further comprises: identifying, using the processing device, at least one calculation formula for the assessing of the value of the at least one business based on the selecting of one of the plurality of business valuation models; and calculating, using the processing device, at least one valuation parameter associated with the value of the at least one business under one of the plurality of business valuation models using the at least one calculation formula and the at least one business-related source data, wherein the generating of the at least one business valuation report for the at least one business is further based on the calculating.
7. The method of claim 3 further comprises: identifying, using the processing device, at least one business-related data source from the plurality of business-related data sources based on the connection status of each of the plurality of business-related data sources; generating, using the processing device, at least one connection request for connecting to the at least one business-related data source based on the identifying of the at least one business-related data source; transmitting, using the communication device, the at least one connection request to the at least one device; and receiving, using the communication device, at least one authorization for connecting to the at least one business-related data source from the at least one device, wherein the establishing of the connection to at least one of the plurality of business-related data sources comprises establishing the connection to the at least one business-related data source based on the at least one authorization.
8. The method of claim 7 further comprises: analyzing, using the processing device, the connection status of each of the plurality of business-related data sources; generating, using the processing device, an accuracy of the assessing based on the analyzing of the connection status, wherein the identifying of the at least one business-related data source is further based on the accuracy; and transmitting, using the communication device, the accuracy to the at least one device.
9. The method of claim 1 wherein the business valuation model selection algorithm is further configured for generating at least one input prompt based on the connection status of each of the plurality of business-related data sources, wherein the method further comprises: transmitting, using the communication device, the at least one input prompt to the at least one device; and receiving, using the communication device, at least one input corresponding to the at least one input prompt from the at least one device, wherein the business valuation model selection algorithm is further configured for analyzing the at least one input, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one input.
10. The method of claim 1 wherein at least one of the plurality of business-related data sources comprises at least one blockchain ledger.
11. A system for providing a valuation for businesses, the system comprising: a communication device configured for: receiving at least one request for assessing a value of at least one business from at least one device; and transmitting at least one business valuation report to the at least one device; a processing device configured for: analyzing the at least one request; identifying a plurality of business-related data sources associated with the at least one business based on the analyzing of the at least one request; determining a connection status of each of the plurality of business-related data sources based on the identifying; executing a business valuation model selection algorithm, wherein the business valuation model selection algorithm is configured for selecting one of a plurality of business valuation models for assessing the value of the at least one business based on the connection status of each of the plurality of business-related data sources; and generating the at least one business valuation report for the at least one business under one of the plurality of business valuation models based on the selecting; and a storage device configured for storing the at least one valuation report.
12. The system of claim 11 wherein the processing device is further configured for initializing a connection with each of the plurality of business-related data sources based on the identifying of the plurality of business-related data sources, wherein the determining of the connection status of each of the plurality of business-related data sources is further based on the initializing.
13. The system of claim 12 wherein the processing device is further configured for establishing the connection with at least one of the plurality of business-related data sources based on the initializing, wherein the communication device is further configured for receiving at least one business-related data from at least one of the plurality of business-related data sources based on the establishing of the connection, wherein the business valuation model selection algorithm is further configured for analyzing the at least one business-related data, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one business-related data.
14. The system of claim 13 wherein the business valuation model selection algorithm is further configured for: generating at least one value for at least one model selecting parameter based on the analyzing of the at least one business-related data; and analyzing the at least one value of the at least one model selecting parameter based on the generating of the at least one value for the at least one model selecting parameter, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one value of the at least one model selecting parameter.
15. The system of claim 13 wherein the processing device is further configured for structuring a plurality of data types of each of the at least one business-related data into a unified format, wherein the analyzing of the at least one business-related data is further based on the structuring.
16. The system of claim 13 wherein the processing device is further configured for: identifying at least one calculation formula for the assessing of the value of the at least one business based on the selecting of one of the plurality of business valuation models; and calculating at least one valuation parameter associated with the value of the at least one business under one of the plurality of business valuation models using the at least one calculation formula and the at least one business-related source data, wherein the generating of the at least one business valuation report for the at least one business is further based on the calculating.
17. The system of claim 13 wherein the processing device is further configured for: identifying at least one business-related data source from the plurality of business-related data sources based on the connection status of each of the plurality of business-related data sources; and generating at least one connection request for connecting to the at least one business-related data source based on the identifying of the at least one business-related data source, wherein the communication device is further configured for: transmitting the at least one connection request to the at least one device; and receiving at least one authorization for connecting to the at least one business-related data source from the at least one device, wherein the establishing of the connection to at least one of the plurality of business-related data sources comprises establishing the connection to the at least one business-related data source based on the at least one authorization.
18. The system of claim 17 wherein the processing device is further configured for: analyzing the connection status of each of the plurality of business-related data sources; and generating an accuracy of the assessing based on the analyzing of the connection status, wherein the identifying of the at least one business-related data source is further based on the accuracy, wherein the communication device is further configured for transmitting the accuracy to the at least one device.
19. The system of claim 11 wherein the business valuation model selection algorithm is further configured for generating at least one input prompt based on the connection status of each of the plurality of business-related data sources, wherein the communication device is further configured for: transmitting the at least one input prompt to the at least one device; and receiving at least one input corresponding to the at least one input prompt from the at least one device, wherein the business valuation model selection algorithm is further configured for analyzing the at least one input, wherein the selecting of one of the plurality of business valuation models is further based on the analyzing of the at least one input.
20. The system of claim 11 wherein at least one of the plurality of business-related data sources comprises at least one blockchain ledger.