Data processing method and device based on scoring system, electronic equipment and medium

Abstract

The embodiment of the invention provides a data processing method and device based on a scoring system, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: obtaining an investment portfolio, and obtaining research result data of a plurality of fund companies according to a preset period; in response to the credit scoring instruction, performing data analysis on index data in the investigation result data through a scoring system to obtain a company score corresponding to the fund company; screening all company scores to obtain score quantiles; for each fund company, setting a position scale parameter according to the score quantile, and setting a rating grade according to the position scale parameter and the company score; generating a position monitoring condition according to the rating grade and the position scale parameter, and performing data monitoring on the investment portfolio based on the position monitoring condition; and when the portfolio does not meet the position monitoring condition, generating prompt information. According to the embodiment of the invention, the position scale of a company can be tracked, and potential risk of position of the company can be prompted in time.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a data processing method, device, electronic device and medium based on a scoring system. Background Art

[0002] In recent years, the number of public funds issued has increased, the fund scale has continued to grow, and the returns of fund products have become increasingly unstable. The company mainly invests in financial products by subscribing to public funds. Due to the increasing volatility of the macro environment, some public fund companies with weak credit research capabilities have been significantly impacted.

[0003] Traditional fund company credit scoring is mainly based on the performance of fund companies' past investment portfolios, which is less comprehensive and less dimensional. In addition, the current fund company credit scoring system uses static thresholds to assess credit risk and cannot continuously track the size of fund companies' holdings, resulting in the inability to promptly alert fund companies to potential compliance risks. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a data processing method, device, electronic device and medium based on a scoring system, which can dynamically track the size of each company's holdings and promptly remind the company of the potential risks of its holdings.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a data processing method based on a scoring system, the method comprising: Acquire investment portfolios and research results data of multiple fund companies according to a preset period, wherein the research results data includes multiple indicator data; In response to the credit scoring instruction, the scoring system performs data analysis on the indicator data in the survey result data to obtain a company score corresponding to the fund company; Screening all the company scores to obtain the score quantile, wherein the score quantile is used to measure the size of the holdings; For each of the fund companies, a holding size parameter is set according to the score quantile, and a rating level is set according to the holding size parameter and the company score; Generate a holding monitoring condition according to the rating level and the holding size parameter, and perform data monitoring on the investment portfolio of the fund company based on the holding monitoring condition; When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated.

[0006] In some embodiments, before analyzing the indicator data in the survey result data by a scoring system to obtain a company score corresponding to the fund company, the method further includes: Obtaining performance data of a plurality of fund companies; Performing data verification on the performance data and the survey result data through a scoring system; When the performance data and the survey result data pass the data verification, the performance data and the survey result data are imported into the scoring system.

[0007] In some embodiments, the performing of data analysis on the indicator data in the survey result data by a scoring system to obtain a company score corresponding to the fund company includes: For each indicator data, obtaining the data information type of the indicator data; Score the indicator data using a scoring system to obtain a score combination of the survey result data, wherein the score combination includes a plurality of scores, each of which corresponds to the data information type; Setting a plurality of scoring indicators and a plurality of scoring rules according to the score combination and the data information type; The fund company is credit-scored according to the score combination, the scoring index and the scoring rule to obtain a company score corresponding to the fund company.

[0008] In some embodiments, the setting of multiple scoring indicators and multiple scoring rules according to the score combination and the data information type includes: When the data information type is management scale information, determining a first score corresponding to the management scale information according to the score combination, and setting a first scoring indicator and a first scoring rule according to the first score and the scale of the management scale information; When the data information type is team member information, determining a second score corresponding to the team member information according to the score combination, and setting a second scoring indicator and a second scoring rule according to the second score and the team member information; When the data information type is performance evaluation information, determining a third score corresponding to the performance evaluation information according to the score combination, and setting a third scoring indicator and a third scoring rule according to the third score and the fixed income performance of the performance evaluation information; When the data information type is risk assessment information, a fourth score corresponding to the risk assessment information is determined according to the score combination, and a fourth scoring indicator and a fourth scoring rule are set according to the fourth score and the number of evaluations of the risk assessment information.

[0009] In some embodiments, the screening of all the company scores to obtain the score quantiles includes: Sorting all the company scores according to a preset sorting rule to obtain a score sequence; The target company score at a preset position is screened on the score sequence, and the target company score is used as the score quantile.

[0010] In some embodiments, the setting of the rating level according to the position size parameter and the company score includes: Setting a plurality of judgment standard values ​​and a plurality of upper limit constraint values ​​according to the company score; Divide the position size parameter according to all the determination standard values ​​to obtain multiple position size intervals; The rating level is set according to the position size range and the upper limit constraint value.

[0011] In some embodiments, the data monitoring of the investment portfolio based on the position monitoring condition includes: Calculate the target percentile value and portfolio size value of the fund company in the investment portfolio; Determine the target rating level of the investment portfolio, and determine the target holding size range and target upper limit constraint value corresponding to the target rating level; Determine the interval endpoint values ​​of the target position size interval; Comparing the target quantile value with the interval endpoint value, and comparing the combined size value with the target upper limit constraint value; The investment portfolio fails to meet the position monitoring condition, including at least one of the following: The target percentile value is not within the target position size range; or, The combined size value is greater than the target upper limit constraint value.

[0012] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a data processing device based on a scoring system, the device comprising: A data acquisition module is used to acquire an investment portfolio and obtain research result data of multiple fund companies according to a preset period, wherein the research result data includes multiple indicator data; A data analysis module, configured to respond to a credit scoring instruction and perform data analysis on the indicator data in the survey result data through a scoring system to obtain a company score corresponding to the fund company; A scoring screening module, used to screen all the company scores to obtain the score quantile, wherein the score quantile is used to measure the size of the holdings; A parameter setting module, for setting a holding size parameter for each fund company according to the score quantile, and setting a rating level according to the holding size parameter and the company score; A condition generation module, used to generate a position monitoring condition according to the rating level and the position size parameter, and perform data monitoring on the investment portfolio based on the position monitoring condition; The information prompt module is used to generate prompt information when the investment portfolio does not meet the position monitoring conditions.

[0013] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the data processing method based on the scoring system as described in the first aspect when executing the computer program.

[0014] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data processing method based on the scoring system as described in the first aspect is implemented.

[0015] The data processing method, device, electronic device and storage medium based on the scoring system proposed in the present application first obtain an investment portfolio and obtain the survey result data of multiple fund companies according to a preset period, wherein the survey result data includes multiple indicator data, which is convenient for the subsequent multi-dimensional data evaluation of the fund company. In response to the credit scoring instruction, the indicator data in the survey result data is analyzed by the scoring system to obtain the company score corresponding to the fund company. The fund company can be analyzed from a multi-dimensional perspective, so as to obtain the company credit score of the fund company, which is convenient for managing the investment portfolio of the fund company through the company score. Then, all company scores are screened to obtain the score percentile, which is convenient for the subsequent measurement of the company's holding size. For each fund company, the holding size parameter is set according to the score percentile, and the liquidity of the investment portfolio can be maintained through the percentile, and the holding size parameter and The company score sets the rating level, so that the grade can be given according to the percentile of the specific score, avoiding the potential completeness and accuracy problems due to a single data source, thereby ensuring that the rating results are more reasonable. Finally, the position monitoring conditions are generated according to the rating level and position size parameters, so that different position monitoring conditions can be set for different rating levels and position size parameters. By setting specific monitoring conditions for assets with different ratings, fund companies can manage the risks of various types of assets more finely, and monitor the investment portfolio based on the position monitoring conditions, so as to dynamically track the proportion of each company's holdings in the portfolio and realize real-time monitoring of the position size. When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated, which can promptly remind the fund company of the potential risks of the holding portfolio, so that the fund company can promptly discover potential risk points and avoid further accumulation of risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a data processing method based on a scoring system provided in an embodiment of the present application; Figure 2 A flowchart of a data processing method based on a scoring system provided in another embodiment of the present application; Figure 3 A flow chart of a method for analyzing indicator data in survey result data by using a scoring system provided in an embodiment of the present application; Figure 4 yes Figure 3 Flow chart of step S303 in FIG. Figure 5 yes Figure 1 Flow chart of step S103 in FIG. Figure 6 A flow chart of a method for setting rating levels according to position size parameters and company ratings provided in an embodiment of the present application; Figure 7 A flow chart of a method for monitoring data of an investment portfolio based on position monitoring conditions provided in an embodiment of the present application; Figure 8 is a schematic diagram of the structure of a data processing device based on a scoring system provided in an embodiment of the present application; Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0020] First, some nouns involved in this application are analyzed: Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0021] The data processing method and device based on the scoring system, the electronic device and the storage medium provided in the embodiments of the present application can dynamically track the size of each company's holdings and promptly remind the company of the potential risks of its holdings.

[0022] The following examples are used to illustrate the data processing method based on the scoring system in the embodiments of the present application.

[0023] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0024] The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, module management technology of online meeting systems, natural language processing technology, and machine learning / deep learning.

[0025] The data processing method based on the scoring system provided in the embodiment of the present application relates to the field of data processing technology. The data processing method based on the scoring system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides 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, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a data processing method based on a scoring system, etc., but is not limited to the above forms.

[0026] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data information types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0027] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0028] In recent years, the number of public funds issued has increased, the fund scale has continued to grow, and the returns of fund products have become increasingly unstable. The company mainly invests in financial products by subscribing to public funds. Due to the increasing volatility of the macro environment, some public fund companies with weak credit research capabilities have been significantly impacted.

[0029] Traditional fund company credit scoring is mainly based on the performance of fund companies' past investment portfolios, which is less comprehensive and less dimensional. In addition, the current fund company credit scoring system uses static thresholds to assess credit risk and cannot continuously track the size of fund companies' holdings, resulting in the inability to promptly alert fund companies to potential compliance risks.

[0030] In order to solve the above problems, the present embodiment provides a data processing method, device, electronic device and storage medium based on a scoring system. First, an investment portfolio is obtained, and survey result data of multiple fund companies are obtained according to a preset period, wherein the survey result data includes multiple indicator data, which is convenient for subsequent multi-dimensional data evaluation of the fund company. In response to a credit scoring instruction, the indicator data in the survey result data is analyzed by the scoring system to obtain a company score corresponding to the fund company. The fund company can be analyzed from a multi-dimensional perspective, so that the company credit score of the fund company can be obtained, which is convenient for managing the fund company's investment portfolio through the company score. Then, all company scores are screened to obtain the score quantile, which is convenient for subsequent measurement of the company's holding scale. For each fund company, the holding scale parameter is set according to the score quantile, and the liquidity of the investment portfolio can be maintained through the quantile, and the holding scale can be adjusted according to the holding scale. The rating level is set according to the model parameters and company scores, so that the grade can be given according to the percentile of the specific score, avoiding the potential completeness and accuracy problems due to a single data source, thereby ensuring that the rating results are more reasonable. Finally, the position monitoring conditions are generated according to the rating level and position size parameters, so that different position monitoring conditions can be set for different rating levels and position size parameters. By setting specific monitoring conditions for assets with different ratings, fund companies can manage the risks of various types of assets more finely, and monitor the investment portfolio based on the position monitoring conditions, so as to dynamically track the proportion of each company's holdings in the portfolio and realize real-time monitoring of the position size. When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated, which can promptly remind the fund company of the potential risks of the position portfolio, so that the fund company can promptly discover potential risk points and avoid further accumulation of risks.

[0031] The following is a detailed description with reference to the accompanying drawings.

[0032] Figure 1 is an optional flow chart of a data processing method based on a scoring system provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0033] Step S101, obtaining an investment portfolio, and obtaining research result data of multiple fund companies according to a preset period, wherein the research result data includes multiple indicator data.

[0034] In step S101 of some embodiments, an investment portfolio is obtained, and research result data of multiple fund companies are obtained according to a preset period, wherein the research result data includes multiple indicator data. Regular data acquisition can ensure the timeliness of information and reflect the latest operating conditions and market performance of the fund companies. Through regular monitoring, potential risks and problems of the fund companies can be discovered in a timely manner, thereby taking preventive or mitigation measures.

[0035] It should be noted that the investment portfolio in the embodiment of the present application is a combination of holdings of multiple fund companies, and the preset period can be set according to the needs of the user. For example, it can be set to obtain the research results data of multiple fund companies every other day, or every two days.

[0036] In some embodiments, the preset period of the embodiment of the present application can also be set according to the number of times the prompt information is generated. Specifically, the embodiment of the present application will record the time when the prompt information is generated, and calculate the time interval between the prompt information generated this time and the historical prompt information generated last time, and compare the time interval with the preset interval. If the time interval is greater than or equal to the preset interval, the research results of multiple fund companies can continue to be obtained according to the preset period, or the preset period can be extended to obtain the first period, and the research results of multiple fund companies are obtained according to the first period; when the time interval is less than or equal to the preset interval, the embodiment of the present application needs to shorten the preset period to obtain the second period, and subsequently obtain the research results of multiple fund companies according to the second period. By adjusting the preset period, the fund company can be reminded of the potential risks of the portfolio in a timely manner, so that the fund company can promptly discover potential risk points and avoid further accumulation of risks.

[0037] Step S102, in response to the credit scoring instruction, the scoring system performs data analysis on the indicator data in the survey result data to obtain a company score corresponding to the fund company.

[0038] In step S102 of some embodiments, in response to a credit scoring instruction, the indicator data in the survey result data is analyzed by the scoring system, and multiple indicators and rules can be dynamically adjusted according to market changes and new information to maintain the timeliness of the scoring. The performance of the fund company in various dimensions can be more accurately evaluated, and a company score corresponding to the fund company can be obtained, thereby ensuring the comprehensiveness of the evaluation.

[0039] It should be noted that the scoring system in the embodiment of the present application can also query the credit score and grade information of each fund company, query the credit score history of a single fund company and the score details of each indicator. In addition, the main interface of the scoring system can display the fund company credit score distribution column chart and credit score detail table. Selecting a different grade query will only display the fund company score information under that grade; click on the fund company name column in the credit score detail table to jump to the fund company score details page to view the line chart of the fund company score history change and the score detail table of each indicator.

[0040] Step S103, screening all company scores to obtain score quantiles, where the score quantiles are used to measure the size of holdings.

[0041] In step S103 of some embodiments, all company scores are screened to obtain score quantiles to facilitate subsequent measurement of credit risk and position size.

[0042] It should be noted that the scoring quantile in the embodiment of the present application refers to the split point where a set of data is divided into several continuous intervals with the same probability after being arranged in order of size. The quantile can be used to measure credit risk and position size. Fund companies may decide the position size of a certain bond or asset based on the quantile of the credit research score.

[0043] Step S104, for each fund company, setting the holding size parameter according to the score percentile, and setting the rating level according to the holding size parameter and the company score.

[0044] In step S104 of some embodiments, for each fund company, a holding size parameter is set according to the score percentile, so as to limit the holding size of the fund company's holding products, and a rating level is set according to the holding size parameter and the company's score. By setting different rating levels, risk assessment can be performed on the holding sizes of different fund companies. Different rating levels have corresponding configuration upper limit constraints, which can dynamically constrain and track the proportion of each company's holding size in the portfolio.

[0045] Step S105, generating a holding monitoring condition according to the rating level and the holding size parameter, and performing data monitoring on the investment portfolio based on the holding monitoring condition.

[0046] In step S105 of some embodiments, position monitoring conditions are generated according to the rating level and position size parameters, and data monitoring of the investment portfolio is performed based on the position monitoring conditions, which can dynamically track the proportion of each company's holding size in the portfolio and achieve real-time monitoring of the position size.

[0047] Step S106: When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated.

[0048] In step S106 of some embodiments, when the investment portfolio does not meet the position monitoring conditions, it means that the fund company's position size may exceed the corresponding credit rating configuration upper limit, and a prompt message needs to be generated to remind the user that the current investment portfolio has credit risk, so that the fund company can discover potential risk points in time and avoid further accumulation of risks.

[0049] It should be noted that the embodiment of the present application will conduct a comprehensive monitoring of the investment portfolio. Specifically, the proportion of the trading bond holdings of each fund company on T-1 day will be monitored daily. If it exceeds the upper limit constraint of the corresponding level configuration, the system will automatically send an email reminder and display a rolling reminder on the interface.

[0050] In some embodiments, when the investment portfolio meets the position monitoring conditions, the investment portfolio continues to be monitored in real time.

[0051] See also Figure 2 , Figure 2 This is a flowchart of a data processing method based on a scoring system provided in another embodiment of the present application. Figure 2 The method may include but is not limited to steps S201 to S203.

[0052] It should be noted that steps S201 to S203 occur before the indicator data in the survey result data is analyzed by the scoring system to obtain the company score corresponding to the fund company.

[0053] Step S201, obtaining performance data of multiple fund companies.

[0054] Step S202: verify the performance data and the survey result data through the scoring system.

[0055] Step S203: When the performance data and the survey result data pass the data verification, the performance data and the survey result data are imported into the scoring system.

[0056] In steps S201 to S203 of some embodiments, before performing data analysis on the indicator data in the survey result data through the scoring system, the embodiments of the present application will also obtain performance data of multiple fund companies, and then perform data verification on the performance data and the survey result data through the scoring system. Specifically, the scoring system adds verification rules to the performance data, and implements data verification of the performance data and the survey result data through operations such as verifying the table header and verifying the data value type. The verification ensures the accuracy and consistency of the data, reduces errors and abnormal data, and implements automatic verification of the data. When the performance data and the survey result data pass the data verification, the performance data and the survey result data are imported into the scoring system. The data in the scoring system is updated by importing the performance data and the survey result data, ensuring that the data in the scoring system is the latest data.

[0057] It should be noted that in the process of performing data verification on the performance data and the survey result data through the scoring system, the embodiment of the present application first determines the data fields of the performance data and the survey result data to be imported and the data type of each field, such as integers, floating point numbers, dates, etc. At this time, the scoring system will create an Excel template with preset column names and data formats so that users can fill in the data as required, and then use the Easy Excel Java library or other tools to write code to perform format and data verification on the imported Excel file. After that, the performance data and the survey result data are header-checked to check whether the column name of the imported Excel file is consistent with the template to ensure the consistency and accuracy of the data, and then verify whether the type of each column of data conforms to the preset data model, for example, to ensure that the numeric column does not contain text, to ensure that the text column does not include special characters, etc., thereby completing the entire data verification process, being able to discover and correct data problems in advance, and avoiding erroneous data from affecting subsequent analysis and decision-making, and the automated verification process can save time and cost for manual inspection.

[0058] In some embodiments, when the performance data or survey result data fails to pass the data verification, the embodiment of the present application will generate a specific error message indicating that the verification failed, thereby reminding the user to check the information.

[0059] It can be understood that the performance data in the embodiments of the present application include but are not limited to the fund net asset value growth rate, total return rate, fund dividends, etc. Obtaining performance data can help investors and analysts evaluate the fund's historical performance and future potential returns, and the performance data can be used as a benchmark for comparing the performance of different fund companies or fund products.

[0060] See also Figure 3 , Figure 3A flow chart of a method for performing data analysis on indicator data in survey result data through a scoring system provided in an embodiment of the present application, the method including but not limited to steps S301 to S304.

[0061] Step S301: for each indicator data, obtain the data information type of the indicator data.

[0062] Step S302, divide the indicator data into scores through a scoring system to obtain a score combination of the survey result data, wherein the score combination includes multiple scores, each score corresponding to a data information type.

[0063] Step S303, setting multiple scoring indicators and multiple scoring rules according to the score combination and the data information type.

[0064] Step S304, credit scoring the fund company according to the score combination, scoring index and scoring rules, and obtaining a company score corresponding to the fund company.

[0065] In steps S301 to S304 of some embodiments, in the process of performing data analysis on the indicator data in the survey result data through the scoring system, for each indicator data, the data information type of the indicator data is obtained, and then the indicator data is scored through the scoring system, so that different scores can be assigned according to the importance of the indicator data, and the score combination of the survey result data is obtained. By assigning higher scores to more important indicators, the impact of each indicator on the overall risk can be more accurately reflected, thereby performing a more accurate risk assessment. Afterwards, multiple scoring indicators and multiple scoring rules are set according to the score combination and the data information type to ensure the comprehensiveness of the assessment. Different scoring rules can adapt to different types of data and different assessment needs, thereby improving the flexibility and applicability of the scoring system. Finally, the fund company is credit-scored according to the score combination, scoring indicators and scoring rules to obtain a company score corresponding to the fund company. Multiple indicators and rules can be dynamically adjusted according to market changes and new information to maintain the timeliness of the scoring, and can more accurately evaluate the performance of the fund company in various dimensions.

[0066] It should be noted that the indicator data in the embodiments of the present application include but are not limited to management scale, team members, performance evaluation, risk evaluation, etc.

[0067] See also Figure 4 In some embodiments, step S303 may also include but is not limited to steps S401 to S404.

[0068] Step S401, when the data information type is management scale information, determine a first score corresponding to the management scale information according to the score combination, and set a first scoring indicator and a first scoring rule according to the first score and the scale of the management scale information.

[0069] In step S401 of some embodiments, when the data information type is management scale information, since the scoring system divides the corresponding scores according to different data information types, the embodiment of the present application will first determine the first score corresponding to the management scale information based on the score combination, and set the first scoring indicator and the first scoring rule according to the first score and the scale of the management scale information, so as to be able to set the scoring indicators and scoring rules in a targeted manner.

[0070] It should be noted that the scale of the management scale information in the embodiment of the present application is used to characterize the total assets managed by the fund company. In the process of setting the first scoring indicator and the first scoring rule according to the first score and the scale of the management scale information, the embodiment of the present application first sets the first scoring indicator, specifically, sets the first scoring indicator according to the scale ranking quantile, and sets the first scoring rule according to the first score and the scale ranking quantile, for example, the higher the scale ranking quantile, the higher the score.

[0071] Step S402, when the data information type is team member information, determine a second score corresponding to the team member information according to the score combination, and set a second scoring indicator and a second scoring rule according to the second score and the team member information.

[0072] In step S402 of some embodiments, when the data information type is team personnel information, a second score corresponding to the team personnel information is determined based on the score combination, wherein the team personnel information includes scale-personnel matching, investment experience, and personnel support, and a second scoring indicator and a second scoring rule are set based on the second score and the team personnel information to achieve the formulation of scoring indicators and scoring rules for the team personnel information.

[0073] It should be noted that the second score is the sum of all information in the team personnel information. For example, the second score is 30 points. The team personnel information includes scale-personnel matching, investment experience, and personnel support. At this time, the scale-personnel matching, investment experience, and personnel support can be set to 10 points each, or the scale-personnel matching can be set to 5 points, the investment experience to 10 points, the personnel support to 15 points, and so on. The embodiments of this application do not make specific restrictions.

[0074] Specifically, in the process of setting the second scoring indicator and the second scoring rule according to the second score and team personnel information, it is necessary to set the indicator according to the different personnel information in the team personnel information. Taking the team personnel information including three kinds of information, namely, scale personnel matching, investment experience and personnel support, as an example, the embodiment of the present application sets the first indicator according to the scale of scale personnel matching and the total number of personnel, that is, the first indicator is set according to the proportion of scale to the total number of personnel, for example, the higher the ranking percentile, the lower the score; the second indicator is set according to the years of experience in investment experience, for example, the higher the ranking percentile of the years of experience, the higher the score; the third indicator is set according to the number of supporters in personnel support, for example, the higher the ranking percentile of the number of supporters, the higher the score, and so on. The embodiment of the present application does not make specific restrictions.

[0075] Step S403, when the data information type is performance evaluation information, determine the third score corresponding to the performance evaluation information according to the score combination, and set the third scoring indicator and the third scoring rule according to the third score and the fixed income performance of the performance evaluation information.

[0076] In step S403 of some embodiments, when the data information type is performance evaluation information, a third score corresponding to the performance evaluation information is determined based on the score combination, and a third scoring indicator and a third scoring rule are set based on the third score and the fixed income performance of the performance evaluation information to achieve the formulation of scoring indicators and scoring rules for the performance evaluation information.

[0077] It should be noted that the fixed income performance of the performance evaluation information in the embodiments of the present application may be the fixed income performance within a preset period of time, for example, the fixed income performance in the past three years, the fixed income performance in the past year, and so on.

[0078] Specifically, the performance evaluation information in the embodiment of the present application may include fixed-income performance of multiple durations. In this case, the third score is the total score of fixed-income performance of different durations. For example, the performance evaluation information includes the first fixed-income performance of the past three years and the second fixed-income performance of the past year. The third score is 15 points. In this case, the score of the first fixed-income performance can be set to 5 points, the score of the second fixed-income performance can be set to 10 points, etc. And the third scoring indicator can be set according to the ranking percentile of the fixed-income performance. For example, the higher the ranking percentile, the higher the score. Finally, the third scoring rule is set according to the third scoring indicator and the ranking percentile.

[0079] Step S404, when the data information type is risk assessment information, determine a fourth score corresponding to the risk assessment information according to the score combination, and set a fourth scoring indicator and a fourth scoring rule according to the fourth score and the number of evaluations of the risk assessment information.

[0080] In step S404 of some embodiments, when the data information type is risk assessment information, a fourth score corresponding to the risk assessment information is determined based on the score combination, and a fourth scoring indicator and a fourth scoring rule are set based on the fourth score and the number of evaluations of the risk assessment information, thereby realizing the formulation of scoring indicators and scoring rules for the risk assessment information.

[0081] It should be noted that the risk assessment information in the embodiment of the present application may include the number of evaluations in the past few years. For example, the risk assessment information includes the number of evaluations in the past three years, the number of evaluations in the past year, and so on. The embodiment of the present application can set the fourth scoring indicator according to the number of evaluations of the risk assessment information, and then set the fourth scoring rule based on the number of evaluations, for example, deduct five points for each scoring, deduct three points for each scoring, etc. The embodiment of the present application does not make specific restrictions.

[0082] It is worth noting that in the embodiments of the present application, different scores can be assigned according to the data of the indicator data. For example, the first score of the management scale information is set to 10 points, the second score of the team personnel information is set to 25 points, the third score of the performance evaluation information is set to 15 points, and the fourth score of the risk assessment information is set to 50 points, etc. The embodiments of the present application do not make specific restrictions.

[0083] See also Figure 5 In some embodiments, step S103 may also include but is not limited to steps S501 to S502.

[0084] Step S501, sorting all company scores according to a preset sorting rule to obtain a score sequence.

[0085] Step S502, screening the target company score at a preset position in the score sequence, and using the target company score as the score quantile.

[0086] In steps S501 to S502 of some embodiments, in the process of screening all company scores to obtain score percentiles, the embodiments of the present application first sort all company scores according to a preset sorting rule, for example, sorting in ascending or descending order according to the company scores to obtain a score sequence, thereby achieving an ordered sorting of the company scores. Afterwards, the target company scores at a preset position are screened in the score sequence, and the target company scores are used as score percentiles to facilitate subsequent measurement of credit risk and position size.

[0087] It should be noted that the preset sorting rules in the embodiment of the present application can be sorting in ascending order by company score or sorting in descending order by company score. In the case of sorting in ascending order by company score, the preset position can be the target company score of the last N positions in the score sequence; in the case of sorting in descending order by company score, the preset position can be the target company score of the first N positions in the score sequence, where N is a positive integer, and the embodiment of the present application does not make specific restrictions.

[0088] See also Figure 6 , Figure 6 A flow chart of a method for setting rating levels according to position size parameters and company scores provided in an embodiment of the present application, the method includes but is not limited to steps S601 to S603.

[0089] Step S601, setting a plurality of judgment standard values ​​and a plurality of upper limit constraint values ​​according to the company score.

[0090] Step S602, dividing the position size parameters according to all the judgment standard values ​​to obtain multiple position size intervals.

[0091] Step S603, setting the rating level according to the position size range and the upper limit constraint value.

[0092] In steps S601 to S603 of some embodiments, in the process of setting the rating level according to the holding size parameter and the company score, the embodiment of the present application first sets a plurality of judgment standard values ​​and a plurality of upper limit constraint values ​​according to the company score, wherein the upper limit constraint value is used to characterize the maximum proportion of the company's holding size in the investment portfolio, and realizes the proportion constraint of the company's holding size, and then divides the holding size parameter according to all the judgment standard values ​​to obtain a plurality of holding size intervals. By dividing the holding size intervals, it can help investors refine their investment strategies according to the credit score of the fund company, and finally set the rating level according to the holding size interval and the upper limit constraint value. Specifically, each holding size interval is set with a different upper limit constraint value. The embodiment of the present application can conduct risk assessment on the holding size of different fund companies by setting different rating levels, and can dynamically constrain and track the proportion of each company's holding size in the portfolio.

[0093] It is worth noting that the judgment standard values ​​in the embodiments of the present application can be set according to the company scores. Specifically, after all company scores are sorted according to the preset sorting rules to obtain the score sequence, the score interval composed of the company scores is determined according to the score sequence, and then multiple judgment standard values ​​are set in the score interval. For example, the first judgment standard value is set at one-quarter of the score sequence, the second judgment standard value is set at two-quarters of the score sequence, the third judgment standard value is set at three-quarters of the score sequence, and so on. The embodiments of the present application do not make specific restrictions.

[0094] In some embodiments, after setting the judgment standard value, the embodiment of the present application divides the position size parameter according to the judgment standard value, that is, the company score is in the top 25% percentile is divided into the first position size interval, the company score is in the 25% to 50% percentile is divided into the second position size interval, the company score is in the 50% to 75% percentile is divided into the third position size interval, the company score is in the bottom 25% percentile is divided into the fourth position size interval, etc. After dividing the position size interval, for the first position size interval, the embodiment of the present application sets the first upper limit constraint value to the transaction bond portfolio size less than or equal to 15%; for the second position size interval, the second upper limit constraint value is set to the transaction bond portfolio size less than or equal to 7.5%; for the third position size interval, the third upper limit constraint value is set to the transaction bond portfolio size less than or equal to 2%; for the fourth position size interval, the third upper limit constraint value is set to the transaction bond portfolio size less than or equal to 1%. Finally, the rating level is set according to the divided position size intervals and the upper limit constraint values. For example, the first position size interval with a company score in the top 25% percentile and a trading bond portfolio size less than or equal to 15% is set to level A; the second position size interval with a company score in the 25% to 50% percentile and a trading bond portfolio size less than or equal to 7.5% is set to level B; the third position size interval with a company score in the 50% to 75% percentile and a trading bond portfolio size less than or equal to 2% is set to level C; the fourth position size interval with a company score in the bottom 25% percentile and a trading bond portfolio size less than or equal to 1% is set to level D. The embodiment of the present application does not impose any specific restrictions on the setting of rating levels.

[0095] See also Figure 7 , Figure 7 A flow chart of a method for monitoring data of an investment portfolio based on position monitoring conditions provided in an embodiment of the present application, the method including but not limited to steps S701 to S704.

[0096] Step S701, calculate the fund company's target percentile value and portfolio size value in the investment portfolio.

[0097] Step S702, determine the target rating level of the investment portfolio, and determine the target holding size range and target upper limit constraint value corresponding to the target rating level.

[0098] Step S703, determine the interval endpoint values ​​of the target position size interval.

[0099] Step S704, comparing the target percentile value and the interval endpoint value, and comparing the combined scale value and the target upper limit constraint value.

[0100] In steps S701 to S704 of some embodiments, in the process of data monitoring of the investment portfolio based on the position monitoring conditions, the embodiments of the present application first calculate the target percentile value and the portfolio size value of the fund company in the investment portfolio, and then compare the target percentile value with multiple rating levels to determine the target rating level of the investment portfolio, and determine the target holding size interval corresponding to the target rating level and the target upper limit constraint value corresponding to the target holding size interval. After that, the interval endpoint values ​​of the target holding size interval, that is, the maximum and minimum values ​​of the percentile values ​​of the target holding size interval are determined. Finally, the target percentile value and the interval endpoint values ​​are compared, and the portfolio size value and the target upper limit constraint value are compared to determine whether the proportion of the fund company's holding scale in the investment portfolio exceeds the range. Differentiated position scale control can be implemented for fund companies with different credit management capabilities, and real-time monitoring of the proportion of the fund company's holding scale in the portfolio can be achieved. Fund companies can be reminded of potential risks of the holding portfolio in a timely manner, so that fund companies can discover potential risk points in a timely manner and avoid further accumulation of risks.

[0101] In some embodiments, the investment portfolio does not meet the holding monitoring conditions including the target percentile value not being in the target holding size interval; or the portfolio size value being greater than at least one of the target upper limit constraint values. When the target percentile value is not in the target holding size interval, it means that the target percentile value is less than the minimum endpoint value in the target holding size interval, or the target percentile value is greater than the maximum value in the target holding size interval. When the target percentile value is not in the target holding size interval or / and the portfolio size value is greater than the target upper limit constraint value, it indicates the potential risk of the fund company's holding ratio in the investment portfolio.

[0102] See also Figure 8 The embodiment of the present application also provides a data processing device based on the scoring system, the device comprising: The data acquisition module 801 is used to acquire an investment portfolio and obtain research result data of multiple fund companies according to a preset period, wherein the research result data includes multiple indicator data; The data analysis module 802 is used to respond to the credit scoring instruction and perform data analysis on the indicator data in the survey result data through the scoring system to obtain the company score corresponding to the fund company; The score screening module 803 is used to screen all company scores to obtain score quantiles, wherein the score quantiles are used to measure the size of holdings; The parameter setting module 804 is used to set the holding size parameter for each fund company according to the score quantile, and to set the rating level according to the holding size parameter and the company score; The condition generation module 805 is used to generate the position monitoring conditions according to the rating level and the position size parameters, and to monitor the investment portfolio data based on the position monitoring conditions; The information prompt module 806 is used to generate prompt information when the investment portfolio does not meet the position monitoring conditions.

[0103] In some embodiments, the data processing device based on the scoring system also includes a data import module, which is used to obtain performance data of multiple fund companies; perform data verification on the performance data and survey results data through the scoring system; and when the performance data and survey results data pass the data verification, import the performance data and survey results data into the scoring system.

[0104] The specific implementation of the data processing device based on the scoring system is basically the same as the specific implementation of the data processing method based on the scoring system described above, and will not be described in detail here.

[0105] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned data processing method based on the scoring system. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0106] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the data processing method based on the scoring system in the embodiment of this application; Input / output interface 903, used to implement information input and output; Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904); The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0107] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the data processing method based on the scoring system is implemented.

[0108] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The data processing method, device, electronic device and storage medium based on the scoring system provided in the embodiment of the present application first obtain an investment portfolio and obtain the survey result data of multiple fund companies according to a preset period, wherein the survey result data includes multiple indicator data, which is convenient for the subsequent multi-dimensional data evaluation of the fund company. In response to the credit scoring instruction, the indicator data in the survey result data is analyzed by the scoring system to obtain the company score corresponding to the fund company. The fund company can be analyzed from a multi-dimensional perspective, so as to obtain the company credit score of the fund company, which is convenient for managing the investment portfolio of the fund company through the company score. Then, all company scores are screened to obtain the score percentile, which is convenient for the subsequent measurement of the company's holding size. For each fund company, the holding size parameter is set according to the score percentile, so that the liquidity of the investment portfolio can be maintained through the percentile, and the holding size parameter can be used to adjust the liquidity of the investment portfolio. And the rating level is set according to the company's score, so that the grade judgment can be given according to the percentile of the specific score, avoiding the potential completeness and accuracy problems due to a single data source, so as to ensure that the rating results are more reasonable. Finally, the position monitoring conditions are generated according to the rating level and the position size parameters, so that different position monitoring conditions can be set according to the rating level and the position size parameters. By setting specific monitoring conditions for assets with different ratings, the fund company can manage the risks of various types of assets more finely, and monitor the investment portfolio based on the position monitoring conditions, so as to dynamically track the proportion of each company's holdings in the portfolio and realize real-time monitoring of the position size. When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated, which can promptly remind the fund company of the potential risks of the holding portfolio, so that the fund company can promptly discover potential risk points and avoid further accumulation of risks.

[0110] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0111] It can be understood by those skilled in the art that Figure 1-9 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0112] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0114] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0115] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, 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.

[0117] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] 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 technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0120] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A data processing method based on a scoring system, characterized in that: The method comprises: Acquire an investment portfolio, and acquire research result data of multiple fund companies according to a preset period, wherein the research result data includes multiple indicator data; In response to the credit scoring instruction, the scoring system performs data analysis on the indicator data in the survey result data to obtain a company score corresponding to the fund company; Screening all the company scores to obtain the score quantile, wherein the score quantile is used to measure the size of the holdings; For each of the fund companies, a holding size parameter is set according to the score quantile, and a rating level is set according to the holding size parameter and the company score; Generate a position monitoring condition according to the rating level and the position size parameter, and perform data monitoring on the investment portfolio based on the position monitoring condition; When the investment portfolio does not meet the position monitoring conditions, a prompt message is generated.

2. The data processing method based on the scoring system according to claim 1, characterized in that: Before analyzing the indicator data in the survey result data by a scoring system to obtain a company score corresponding to the fund company, the method further includes: Obtaining performance data of a plurality of fund companies; Performing data verification on the performance data and the survey result data through a scoring system; When the performance data and the survey result data pass the data verification, the performance data and the survey result data are imported into the scoring system.

3. The data processing method based on the scoring system according to claim 1, characterized in that: The scoring system is used to analyze the indicator data in the survey result data to obtain a company score corresponding to the fund company, including: For each indicator data, obtaining the data information type of the indicator data; Score the indicator data using a scoring system to obtain a score combination of the survey result data, wherein the score combination includes a plurality of scores, each of which corresponds to the data information type; Setting a plurality of scoring indicators and a plurality of scoring rules according to the score combination and the data information type; The fund company is credit-scored according to the score combination, the scoring index and the scoring rule to obtain a company score corresponding to the fund company.

4. The data processing method based on the scoring system according to claim 3 is characterized in that: The setting of multiple scoring indicators and multiple scoring rules according to the score combination and the data information type includes: When the data information type is management scale information, determining a first score corresponding to the management scale information according to the score combination, and setting a first scoring indicator and a first scoring rule according to the first score and the scale of the management scale information; When the data information type is team member information, determining a second score corresponding to the team member information according to the score combination, and setting a second scoring indicator and a second scoring rule according to the second score and the team member information; When the data information type is performance evaluation information, determining a third score corresponding to the performance evaluation information according to the score combination, and setting a third scoring indicator and a third scoring rule according to the third score and the fixed income performance of the performance evaluation information; When the data information type is risk assessment information, a fourth score corresponding to the risk assessment information is determined according to the score combination, and a fourth scoring indicator and a fourth scoring rule are set according to the fourth score and the number of evaluations of the risk assessment information.

5. The data processing method based on the scoring system according to claim 1, characterized in that: The scores of all the companies are screened to obtain the score quantiles, including: Sorting all the company scores according to a preset sorting rule to obtain a score sequence; The target company score at a preset position is screened on the score sequence, and the target company score is used as the score quantile.

6. The data processing method based on the scoring system according to claim 1, characterized in that: The setting of the rating level according to the position size parameter and the company score includes: Setting a plurality of judgment standard values ​​and a plurality of upper limit constraint values ​​according to the company score; Divide the position size parameter according to all the determination standard values ​​to obtain multiple position size intervals; The rating level is set according to the position size range and the upper limit constraint value.

7. The data processing method based on the scoring system according to claim 6 is characterized in that: The data monitoring of the investment portfolio based on the position monitoring condition includes: Calculate the target percentile value and portfolio size value of the fund company in the investment portfolio; Determine the target rating level of the investment portfolio, and determine the target holding size range and target upper limit constraint value corresponding to the target rating level; Determine the interval endpoint values ​​of the target position size interval; Comparing the target quantile value with the interval endpoint value, and comparing the combined size value with the target upper limit constraint value; The investment portfolio fails to meet the position monitoring condition, including at least one of the following: The target percentile value is not within the target position size range; or, The combined size value is greater than the target upper limit constraint value.

8. A data processing device based on a scoring system, characterized in that: The device comprises: A data acquisition module is used to acquire an investment portfolio and obtain research result data of multiple fund companies according to a preset period, wherein the research result data includes multiple indicator data; A data analysis module, configured to respond to a credit scoring instruction and perform data analysis on the indicator data in the survey result data through a scoring system to obtain a company score corresponding to the fund company; A scoring screening module, used to screen all the company scores to obtain the score quantile, wherein the score quantile is used to measure the size of the holdings; A parameter setting module, for setting a holding size parameter for each fund company according to the score quantile, and setting a rating level according to the holding size parameter and the company score; A condition generation module, used to generate a position monitoring condition according to the rating level and the position size parameter, and perform data monitoring on the investment portfolio based on the position monitoring condition; The information prompt module is used to generate prompt information when the investment portfolio does not meet the position monitoring conditions.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the data processing method based on the scoring system according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data processing method based on the scoring system according to any one of claims 1 to 7 is implemented.

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