Financial business analysis method and device based on artificial intelligence, equipment and medium

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a financial business analysis method and device based on artificial intelligence, equipment and a medium. The method comprises the steps of obtaining questionnaire information of a user, determining a to-be-analyzed service combination selected by the user according to the questionnaire information, calling first to-be-analyzed data and a first to-be-analyzed model matched with a to-be-analyzed service according to the to-be-analyzed service for any to-be-analyzed service in the to-be-analyzed service combination, and analyzing the first to-be-analyzed data and the first to-be-analyzed model. And analyzing the first to-be-analyzed data by using the first to-be-analyzed model to obtain a first analysis result corresponding to the to-be-analyzed service. According to the method, the to-be-analyzed business combination is selected, the businesses in the to-be-analyzed business combination are sequentially analyzed in the same platform, platform switching is not needed, personalized combination analysis of multiple financial businesses is achieved, various possibilities are maximized, and new service types are continuously created.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence-based financial business analysis method, device, equipment and medium. Background Art

[0002] In the financial industry, AI technology is already being applied to many business scenarios, but there are no AI-based sandbox platforms that can drive positive improvements in digital employee services. Existing AI platforms typically focus on a single key business capability, such as product recommendations. When multiple different functions are needed, switching platforms is often necessary. Currently, manual processing is still required to connect the dots. Therefore, how to integrate multiple different functions within a single AI platform has become an urgent challenge. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a financial business analysis method, apparatus, device, and medium based on artificial intelligence to solve the problem that an AI platform cannot use multiple different functions.

[0004] In a first aspect, an embodiment of the present invention provides a financial business analysis method based on artificial intelligence, the financial business analysis method comprising: Obtaining questionnaire information from the user, and determining a business combination to be analyzed selected by the user based on the questionnaire information; For any business to be analyzed in the combination of businesses to be analyzed, according to the business to be analyzed, retrieve first data to be analyzed and a first model to be analyzed that match the business to be analyzed; The first data to be analyzed is analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

[0005] In a second aspect, an embodiment of the present invention provides an artificial intelligence-based financial business analysis device, the financial business analysis device comprising: An acquisition module, configured to acquire questionnaire information from a user and determine a business combination to be analyzed selected by the user based on the questionnaire information; a calling module, configured to call, for any business to be analyzed in the business combination to be analyzed, first data to be analyzed and a first model to be analyzed that matches the business to be analyzed according to the business to be analyzed; The analysis module is configured to analyze the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

[0006] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer device is used to execute the financial business analysis method described in any one of the first aspects.

[0007] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the financial business analysis method described in any one of the first aspects.

[0008] Compared with the prior art, the present invention has the following beneficial effects: Obtain the user's questionnaire information, determine the business combination to be analyzed selected by the user based on the questionnaire information, and for any business to be analyzed in the business combination to be analyzed, retrieve the first data to be analyzed and the first model to be analyzed that matches the business to be analyzed based on the business to be analyzed, analyze the first data to be analyzed using the first model to be analyzed, and obtain a first analysis result corresponding to the business to be analyzed. In this application, a business combination to be analyzed is selected, and the businesses in the business combination to be analyzed are analyzed sequentially on the same platform without switching platforms, thereby achieving personalized combination analysis of multiple financial businesses, maximizing various possibilities, and continuously creating new types of services. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0010] Figure 1 This is an application environment of an artificial intelligence-based financial business analysis method provided by an embodiment of the present invention; Figure 2 This is a flowchart of a financial business analysis method based on artificial intelligence provided by one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an artificial intelligence-based financial business analysis device provided by one embodiment of the present invention; Figure 4 It is a structural diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0012] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0014] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] Embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0019] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0020] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0021] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0022] An embodiment of the present invention provides an artificial intelligence-based financial business analysis method that can be applied in Figure 1In an application environment, clients communicate with servers. Clients include, but are not limited to, PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs), and other computer devices. Servers can be standalone servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0023] See also Figure 2 , is a flow chart of a financial business analysis method based on artificial intelligence provided by an embodiment of the present invention. The financial business analysis method based on artificial intelligence can be applied to Figure 1 The server in Figure 2 As shown, the financial business analysis method based on artificial intelligence may include the following steps.

[0024] S201: Obtaining questionnaire information from the user, and determining the business combination to be analyzed selected by the user based on the questionnaire information.

[0025] In step S201, the user's questionnaire information is obtained, wherein the questionnaire information is the information filled in on the interactive page, which may include user portrait information and preference information, etc. The business combination to be analyzed is a combination of various businesses to be analyzed, that is, the businesses to be analyzed in multiple scenarios are analyzed on the same platform.

[0026] In this embodiment, an AI-based financial business analysis method is applied to a sandbox platform, which can call upon various models in the model library and data in the data warehouse. The sandbox platform is also connected to the RPA (Robotic Process Automation) platform.

[0027] Users fill out questionnaire information on the interactive page of the Shahe platform. The questionnaire information may include user portrait information and preference information, etc. It may also include data information of corresponding products in financial services, so that users can understand the corresponding products themselves.

[0028] It should be noted that the sandbox platform also includes an employee login portal for collecting employee information to facilitate digital management of employees.

[0029] In this embodiment, the business combination to be analyzed selected by the user is determined based on the questionnaire information. The business combination to be analyzed is a combination of multiple businesses in different scenarios, such as macro analysis business, investment strategy business, marketing planning business, risk monitoring business, transaction settlement business, etc., and each business can be analyzed and processed in sequence. Specifically, the macro analysis business can analyze the user's questionnaire information based on the user's questionnaire information to determine the financial products that the user can select. The investment strategy business analyzes the corresponding financial products and determines the corresponding investment strategy. The marketing planning business analyzes the financial products and determines the corresponding marketing plan. The risk monitoring business performs risk analysis on the financial products.

[0030] In this embodiment, the businesses to be analyzed are combined, that is, different businesses to be analyzed are concentrated on one platform for analysis and processing. There is no need to use multiple platforms to analyze and process them separately and then use manual integration, which reduces the complexity of processing multiple businesses to be analyzed.

[0031] Optionally, based on the questionnaire information, determining the business combination to be analyzed selected by the user includes: Determine the current business to be analyzed selected by the user based on the questionnaire information; According to the current business to be analyzed, the next business to be analyzed that can be combined with the current business to be analyzed is determined to obtain a business combination to be analyzed.

[0032] In this embodiment, the questionnaire information includes the corresponding business that the user needs to handle. When the user selects one of the current businesses to be analyzed, the next business to be analyzed that can be combined with the current business to be analyzed is determined based on the current business to be analyzed. That is, after the user selects the current business to be analyzed, the interactive page of the sandbox platform displays the businesses to be analyzed that can be combined with the current business to be analyzed. The user selects the corresponding business to be analyzed to obtain the business combination to be analyzed.

[0033] It should be noted that, after the user selects the current service to be analyzed, services to be analyzed related to the current service to be analyzed are determined, and the next service to be analyzed is selected from the services to be analyzed related to the current service to be analyzed.

[0034] It should be noted that when the user selects the current business to be analyzed, the sandbox platform can automatically filter out the businesses to be analyzed that are related to the current business to be analyzed, and the user selects the next business to be analyzed from the filtered businesses to be analyzed.

[0035] S202: For any business to be analyzed in the business combination to be analyzed, first data to be analyzed and a first model to be analyzed that match the business to be analyzed are retrieved according to the business to be analyzed.

[0036] In step S202, for any business to be analyzed in the business combination to be analyzed, the first data to be analyzed and the first model to be analyzed that match the business to be analyzed are retrieved according to the business to be analyzed, wherein the first data to be analyzed and the first model to be analyzed that match the business to be analyzed are retrieved from the data warehouse and the model library through the corresponding interface of the sandbox platform.

[0037] In this embodiment, for any business to be analyzed in the business portfolio to be analyzed, first data to be analyzed and a first model to be analyzed that matches the business to be analyzed are retrieved based on the business to be analyzed. The first data to be analyzed may be data from multiple databases, and the first model to be analyzed may be multiple corresponding models. For example, if the business portfolio to be analyzed includes macroeconomic analysis, investment strategy, and risk monitoring, when a user wishes to invest in a financial product, a macroeconomic analysis is first performed on the user. During the macroeconomic analysis, with the user's consent, user-retained information such as the user's deposit information, preferences, hot spots, and other investment information may be retrieved. The first model to be analyzed may be a matching model that matches the user's preferred financial products based on the user's preference information. The first model to be analyzed may also include a classification model that analyzes suitable financial products for the user based on the retrieved data to be analyzed. When performing investment strategy analysis, the first data to be analyzed may be investment data for various financial products. The first model to be analyzed may be a return prediction model that, based on the corresponding investment data, predicts the investment returns of financial products corresponding to different investment strategies, thereby determining the user's investment strategy for the financial product. When conducting risk monitoring, the first data to be analyzed may be monitoring data of a corresponding financial product, and the first model to be analyzed may be a risk prediction model, which predicts the monitoring data to determine whether the corresponding financial product has corresponding risks.

[0038] It should be noted that when calling the first data to be analyzed in the data warehouse, the data warehouse can be a TiDB data warehouse, and when calling the model in the model library, the model library can be a rho+ model library, etc.

[0039] S203: Analyze the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

[0040] In step S203, the first data to be analyzed is analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed, wherein the first analysis result is a result obtained based on the user's questionnaire information, which can be reflected in the corresponding interactive page of the sandbox platform, or the corresponding first analysis result can be sent to the user's mailbox or account.

[0041] In this embodiment, the first data to be analyzed is analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed. For example, if the business to be analyzed is a corresponding financial product recommendation business, the corresponding first data to be analyzed may include user profile data, preference data, information about various financial products, etc., and the corresponding first model to be analyzed may be the corresponding recommendation model. The corresponding recommendation model is used to analyze the user profile data, preference data, and information about various financial products to recommend suitable financial products to the user.

[0042] Optionally, after obtaining the user's questionnaire information, the following steps are also included: Obtain the preset standard business portfolio to be analyzed; Based on the questionnaire information, determine the target standard business portfolio to be analyzed selected by the user; According to the target standard business portfolio to be analyzed, retrieve the standard data to be analyzed and the standard model to be analyzed that matches the target standard business portfolio to be analyzed; The standard data to be analyzed is analyzed using the standard model to be analyzed to obtain a second analysis result corresponding to the standard business combination to be analyzed.

[0043] In this embodiment, the sandbox platform also includes pre-assembled standard business combinations for analysis. These combinations combine related businesses for analysis and serve as standard business combinations for immediate use, eliminating the need for further grouping. Selections can be made directly based on these standard business combinations. Examples include a full-process risk monitoring and early warning combination, and a product valuation and liquidation combination.

[0044] It should be noted that the businesses to be analyzed in the standard portfolio of businesses to be analyzed are related, meaning that the analysis results of one business to be analyzed are correlated with the next. For example, risk monitoring and early warning services: After risk monitoring of a financial product, the results of risk monitoring are used to issue early warnings for that financial product. There is a corresponding correlation between the risk monitoring and early warning services, and the technology for analyzing these two services is mature. Therefore, these businesses can be considered standard portfolios of businesses to be analyzed. For example, product valuation and liquidation services: The valuation results of financial products are correlated with their liquidation. Liquidation of financial products is based on the valuation results. Therefore, the product valuation and liquidation services can be considered standard portfolios of businesses to be analyzed.

[0045] After obtaining the preset standard business combination to be analyzed, the user's target standard business combination to be analyzed is determined based on the questionnaire information. Based on the target standard business combination to be analyzed, the standard data to be analyzed and the standard model to be analyzed that match the target standard business combination to be analyzed are retrieved. The standard data to be analyzed is analyzed using the standard model to be analyzed to obtain a second analysis result corresponding to the standard business combination to be analyzed. The standard data to be analyzed may be data from multiple databases, and the standard model to be analyzed may be multiple corresponding models.

[0046] For example, the entire risk monitoring and early warning process involves obtaining monitoring data for corresponding financial products from the data warehouse's monitoring database. This includes daily price monitoring of financial products, obtaining price fluctuation data for these products, and monitoring price trends. The standard model to be analyzed could be a price prediction model, which predicts future price trends based on historical prices of financial products. Based on the prediction results, the early warning service determines whether they meet preset requirements. If they do not, a corresponding warning message is issued to the user.

[0047] In this embodiment, a standard to-be-analyzed model is used to analyze the standard to-be-analyzed data to obtain a second analysis result corresponding to the standard to-be-analyzed business portfolio. For example, in the product valuation and liquidation business portfolio, the standard to-be-analyzed model may be a product valuation model and a liquidation model. The standard to-be-analyzed data may include sales information of financial products in the data warehouse and customer preferences for these financial products. The product valuation model is used in conjunction with the sales information and customer preferences to value the financial products and obtain a corresponding valuation result. The corresponding liquidation model is then used in conjunction with the corresponding valuation result to liquidate the financial products and obtain a corresponding liquidation result, i.e., a second analysis result. The corresponding second analysis result is reflected in the interactive test of the sandbox platform or sent to the user.

[0048] In this embodiment, related businesses to be analyzed are combined, which solves the problem of multiple calls to data from the data warehouse and improves the analysis efficiency of the businesses to be analyzed.

[0049] Optionally, after obtaining the user's questionnaire information, the following steps are also included: Determine the target business to be analyzed selected by the user based on the questionnaire information; According to the target business to be analyzed, retrieve second data to be analyzed and a second model to be analyzed that match the target business to be analyzed; The second data to be analyzed is analyzed using the second model to be analyzed to obtain a third analysis result corresponding to the target business to be analyzed.

[0050] In this embodiment, a single business to be analyzed can also be selected in the sandbox platform for analysis. If the business to be analyzed is a corresponding financial product recommendation business, the corresponding second data to be analyzed is the user's portrait data, preference data, information on various financial products, etc. The corresponding second model to be analyzed is the corresponding recommendation model. The corresponding recommendation model is used, combined with the user's portrait data, preference data and information on various financial products, to recommend financial products suitable for the user through analysis, that is, the third analysis result.

[0051] Optionally, after analyzing the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed, the method further includes: Based on the first analysis result, a risk assessment is performed on the analysis capability of the business to be analyzed to obtain a first assessment result; If the first evaluation result is less than the preset threshold, it is determined that the analysis capability of the business to be analyzed is passed.

[0052] In this embodiment, a risk assessment is performed on the analytical capabilities of the business to be analyzed based on the first analysis result. That is, after obtaining the first analysis result, the first analysis result is reviewed by artificial intelligence to assess whether the first analysis result has an error risk. If the first analysis result contains a result that violates the law or basic common sense, the corresponding analysis result is considered to have an error risk. After the risk assessment of the analytical capabilities of the business to be analyzed is performed, a first assessment result is obtained. If the first assessment result is less than a preset threshold, the analytical capabilities of the business to be analyzed are determined to pass. That is, if the first assessment result is less than the preset threshold, the corresponding analysis result is considered to have no error risk, the analytical capabilities of the business to be analyzed are determined to pass, and the sandbox platform can be used to analyze the business to be analyzed.

[0053] It should be noted that, the larger the value of the first evaluation result is, the greater the error risk is considered to be, and the smaller the value of the first evaluation result is, the smaller the error risk is considered to be.

[0054] Optionally, based on the first analysis result, a risk assessment is performed on the analysis capability of the business to be analyzed. After obtaining the first assessment result, the method further includes: If the first assessment result is not less than the preset threshold, the risk of the business to be analyzed is eliminated to obtain the first assessment result after elimination; If the first evaluation result after elimination is less than the preset threshold, it is determined that the analysis capability of the business to be analyzed is passed.

[0055] In this embodiment, a risk assessment is performed on the analytical capability of the business to be analyzed based on the first analysis result. After obtaining the first assessment result, if the first assessment result is not less than a preset threshold, i.e., the first analysis result has a corresponding error risk, the risk of the business to be analyzed is eliminated. This risk elimination can be performed on the first data to be analyzed. For example, if a certain data item to be analyzed has a risk, the data item to be analyzed is filtered out during the risk elimination process, i.e., the corresponding data to be analyzed is not retrieved.

[0056] After the risk of the business being analyzed has been eliminated, the sandbox platform is used to reanalyze the business to obtain the corresponding analysis results. This analysis result is then subjected to a risk assessment to obtain a first assessment result after elimination. If the first assessment result after elimination is less than the preset threshold, indicating that the risk of the business being analyzed has been eliminated, the analysis capability of the business being analyzed is determined to pass.

[0057] Optionally, after eliminating the risks of the business to be analyzed, the following steps may also be performed: If the first evaluation result after elimination is not less than the preset threshold, it is determined that the analysis capability of the business to be analyzed is failed.

[0058] In this embodiment, if the first assessment result after elimination is not less than a preset threshold, the analysis capability of the service being analyzed is determined to be unsatisfactory. That is, when analyzing the service being analyzed, the corresponding risk cannot be eliminated, and the analysis capability of the service being analyzed is determined to be unsatisfactory. The service being analyzed is marked or removed to prevent users from using it.

[0059] Obtain the user's questionnaire information, determine the business combination to be analyzed selected by the user based on the questionnaire information, and for any business to be analyzed in the business combination to be analyzed, retrieve the first data to be analyzed and the first model to be analyzed that matches the business to be analyzed based on the business to be analyzed, analyze the first data to be analyzed using the first model to be analyzed, and obtain a first analysis result corresponding to the business to be analyzed. In this application, a business combination to be analyzed is selected, and the businesses in the business combination to be analyzed are analyzed sequentially on the same platform without switching platforms, thereby achieving personalized combination analysis of multiple financial businesses, maximizing various possibilities, and continuously creating new types of services.

[0060] See also Figure 3 , is a structural diagram of a financial business analysis device based on artificial intelligence provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown. Figure 3 The financial business analysis device 30 includes: an acquisition module 31, a calling module 32, and an analysis module 33.

[0061] The acquisition module 31 is used to obtain the user's questionnaire information and determine the business combination to be analyzed selected by the user based on the questionnaire information.

[0062] The retrieving module 32 is configured to retrieve, for any business to be analyzed in the business combination, first data to be analyzed and a first model to be analyzed that match the business to be analyzed according to the business to be analyzed.

[0063] The analysis module 33 is configured to analyze the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

[0064] Optionally, the financial business analysis device 30 further includes: The second acquisition module is used to acquire a preset standard business combination to be analyzed.

[0065] The determination module is used to determine the target standard business combination to be analyzed selected by the user based on the questionnaire information.

[0066] The second retrieval module is used to retrieve the standard data to be analyzed and the standard model to be analyzed that match the target standard business combination to be analyzed according to the target standard business combination to be analyzed.

[0067] The second analysis module is used to analyze the standard data to be analyzed using the standard model to be analyzed to obtain a second analysis result corresponding to the standard business combination to be analyzed.

[0068] Optionally, the financial business analysis device 30 further includes: The second determination module is used to determine the target business to be analyzed selected by the user based on the questionnaire information.

[0069] The third retrieving module is configured to retrieve, according to the target business to be analyzed, second data to be analyzed and a second model to be analyzed that match the target business to be analyzed.

[0070] The third analysis model is used to analyze the second data to be analyzed using the second model to be analyzed to obtain a third analysis result corresponding to the target business to be analyzed.

[0071] Optionally, the acquisition module 31 includes: The first determining unit is configured to determine the current business to be analyzed selected by the user according to the questionnaire information.

[0072] The second determining unit is configured to determine, based on the current service to be analyzed, a next service to be analyzed that can be combined with the current service to be analyzed, to obtain a service combination to be analyzed.

[0073] Optionally, the financial business analysis device 30 further includes: The evaluation module is used to perform a risk evaluation on the analysis capability of the business to be analyzed based on the first analysis result to obtain a first evaluation result.

[0074] The first judgment module is configured to determine that the analysis capability of the business to be analyzed is passed if the first evaluation result is less than a preset threshold.

[0075] Optionally, the financial business analysis device 30 further includes: The elimination module is used to eliminate the risk of the business to be analyzed if the first evaluation result is not less than a preset threshold, and obtain the first evaluation result after elimination.

[0076] The second judgment module is configured to determine that the analysis capability of the business to be analyzed is passed if the first evaluation result after elimination is less than a preset threshold.

[0077] Optionally, the financial business analysis device 30 further includes: The third judgment module is configured to determine that the analysis capability of the business to be analyzed is failed if the first evaluation result after elimination is not less than a preset threshold.

[0078] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0079] Figure 4 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned financial business analysis method embodiments are implemented. The computer device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 4 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include a network interface, etc.

[0080] The processor may be a CPU, other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0081] Memory includes readable storage media, internal memory, and the like. Internal memory can be the internal memory of a computer device, providing an environment for the operation of the operating system and computer-readable instructions stored in the readable storage medium. The readable storage medium can be the computer device's hard drive. In other embodiments, it can also be an external storage device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both the computer device's internal storage unit and external storage devices. Memory is used to store the operating system, application programs, boot loaders, data, and other programs, such as the program code of computer programs. Memory can also be used to temporarily store data that has been output or is about to be output.

[0082] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include at least: any entity or device capable of carrying computer program code, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunications signals.

[0083] The present invention may implement all or part of the processes in the above-mentioned method embodiments, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0084] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0085] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0086] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A financial business analysis method based on artificial intelligence, characterized in that: The financial business analysis method includes: Obtaining questionnaire information from the user, and determining a business combination to be analyzed selected by the user based on the questionnaire information; For any business to be analyzed in the combination of businesses to be analyzed, according to the business to be analyzed, retrieve first data to be analyzed and a first model to be analyzed that match the business to be analyzed; The first data to be analyzed is analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

2. The financial business analysis method according to claim 1, characterized in that: After obtaining the user's questionnaire information, the method further includes: Obtain the preset standard business portfolio to be analyzed; Determining the target standard business combination to be analyzed selected by the user based on the questionnaire information; According to the target standard business combination to be analyzed, retrieving standard data to be analyzed and a standard model to be analyzed that matches the target standard business combination to be analyzed; The standard data to be analyzed is analyzed using the standard model to be analyzed to obtain a second analysis result corresponding to the standard business combination to be analyzed.

3. The financial business analysis method according to claim 1, wherein: After obtaining the user's questionnaire information, the method further includes: Determining the target business to be analyzed selected by the user based on the questionnaire information; According to the target business to be analyzed, retrieving second data to be analyzed and a second model to be analyzed that match the target business to be analyzed; The second data to be analyzed is analyzed using the second model to be analyzed to obtain a third analysis result corresponding to the target business to be analyzed.

4. The financial business analysis method according to claim 1, wherein: The determining, based on the questionnaire information, the business combination to be analyzed selected by the user includes: Determining the current business to be analyzed selected by the user based on the questionnaire information; According to the current service to be analyzed, a next service to be analyzed that can be combined with the current service to be analyzed is determined to obtain a service combination to be analyzed.

5. The financial business analysis method according to claim 1, wherein: After analyzing the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed, the method further includes: Performing a risk assessment on the analysis capability of the business to be analyzed based on the first analysis result to obtain a first assessment result; If the first evaluation result is less than a preset threshold, it is determined that the analysis capability of the business to be analyzed is passed.

6. The financial business analysis method according to claim 5, characterized in that: After performing risk assessment on the analysis capability of the business to be analyzed based on the first analysis result and obtaining the first assessment result, the method further includes: If the first assessment result is not less than a preset threshold, the risk of the business to be analyzed is eliminated to obtain a first assessment result after elimination; If the first evaluation result after elimination is less than the preset threshold, it is determined that the analysis capability of the service to be analyzed is passed.

7. The financial business analysis method according to claim 6, characterized in that: After eliminating the risk of the business to be analyzed, the method further includes: If the first evaluation result after elimination is not less than the preset threshold, it is determined that the analysis capability of the service to be analyzed is failed.

8. A financial business analysis device based on artificial intelligence, characterized in that: The financial business analysis device includes: An acquisition module, configured to acquire questionnaire information from a user and determine a business combination to be analyzed selected by the user based on the questionnaire information; a calling module, configured to call, for any business to be analyzed in the business combination to be analyzed, first data to be analyzed and a first model to be analyzed that matches the business to be analyzed according to the business to be analyzed; The analysis module is configured to analyze the first data to be analyzed using the first model to be analyzed to obtain a first analysis result corresponding to the business to be analyzed.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the financial business analysis method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the financial business analysis method according to any one of claims 1 to 7 is implemented.

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