Method for generating credit rating system, credit rating method

Abstract

The present application discloses a method for generating a credit rating system and a credit rating method, belonging to the field of data processing technology. The method comprises: in response to a data source selection operation, determining a target data source from a pre-configured data source set, wherein different data sources in the data source set are used to provide different credit rating reference data; in response to a rating component selection operation, determining at least one rating component from a pre-configured rating component set, wherein different rating components in the rating component set correspond to different credit rating types; in response to a rating parameter configuration request, configuring the credit rating parameters of each pre-configured parameter component, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters; in response to a system generation request, generating a credit rating system based on the selected rating component and the configured credit rating parameters of each parameter component.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for generating a credit rating system and a credit rating method. Background Art

[0002] In recent years, my country's financial market has seen a series of credit bond defaults or sharp declines, causing overall market turmoil. This has exposed the problem of inflated ratings in my country's credit rating industry.

[0003] At present, banks and exchange markets are gradually canceling the mandatory rating supervision requirements for bond issuance, which means that the reliance of banks and exchange markets on credit ratings of third-party rating companies is gradually decreasing. At the same time, it is particularly important for asset management companies to improve the quality of internal credit ratings, improve rating management systems, and strengthen investment risk control within each subsidiary and the group company as a whole. However, the credit rating needs of different subsidiaries will vary. Developing a credit rating system for each subsidiary will obviously lead to duplicate development, which will waste time and manpower costs.

[0004] It can be seen that there is a problem in the relevant technology that different subsidiaries have different credit rating requirements and it is difficult to quickly develop a credit rating system that meets the needs of each subsidiary. Summary of the invention

[0005] The embodiments of the present application provide a method for generating a credit rating system and a credit rating method, which are used to solve the problem in the related art that different subsidiaries have different credit rating requirements and it is difficult to quickly develop a credit rating system that meets the requirements of each subsidiary.

[0006] In a first aspect, an embodiment of the present application provides a method for generating a credit rating system, comprising:

[0007] In response to the data source selection operation, selecting a target data source from a pre-configured data source set, wherein different data sources in the data source set are used to provide different credit rating reference data; and

[0008] In response to a rating component selection operation, selecting at least one rating component from a preconfigured rating component set, wherein different rating components in the rating component set correspond to different credit rating types;

[0009] In response to the rating parameter configuration request, configure the credit rating parameters of each pre-configured parameter component, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters;

[0010] In response to the system generation request, a credit rating system is generated based on the selected rating components and the credit rating parameters of the configured parameter components.

[0011] In a second aspect, an embodiment of the present application provides a credit rating method, which is applied to a credit rating system, wherein the credit rating system includes at least one rating component and parameter components configured with credit rating parameters, wherein different rating components correspond to different credit rating types, and different parameter components correspond to different types of credit rating parameters, and the method includes:

[0012] In response to the parameter value configuration request, configuring the parameter value of the credit rating parameter of each parameter component, wherein the configured parameter value is determined based on the credit rating data of the object to be rated;

[0013] Based on the credit characterization data of the object to be rated, the rating components and the parameter values ​​of the configured parameter components, the object to be rated is credit rated, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

[0014] In a third aspect, an embodiment of the present application provides a device for generating a credit rating system, including:

[0015] A data source selection module, configured to select a target data source from a pre-configured data source set in response to a data source selection operation, wherein different data sources in the data source set are used to provide different credit rating reference data;

[0016] A rating component selection module, configured to select at least one rating component from a pre-configured rating component set in response to a rating component selection operation, wherein different rating components in the rating component set correspond to different credit rating types;

[0017] A business parameter configuration module, configured to configure the credit rating parameters of each pre-configured parameter component in response to a rating parameter configuration request, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters;

[0018] The generation module is used to generate a credit rating system in response to a system generation request based on the selected rating components and the credit rating parameters of the configured parameter components.

[0019] In a fourth aspect, an embodiment of the present application provides a credit rating device, which is applied to a credit rating system, wherein the credit rating system includes at least one rating component and parameter components configured with credit rating parameters, wherein different rating components correspond to different credit rating types, and different parameter components correspond to different types of credit rating parameters, and the device includes:

[0020] A parameter value configuration module, configured to configure the parameter value of the credit rating parameter of each parameter component in response to a parameter value configuration request, wherein the configured parameter value is determined based on the credit rating data of the object to be rated;

[0021] The rating module is used to perform credit rating on the object to be rated based on the credit characterization data of the object to be rated, the rating component and the parameter values ​​of each configured parameter component, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

[0022] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein:

[0023] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned credit rating system generation method or credit rating method.

[0024] In a sixth aspect, an embodiment of the present application provides a storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the above-mentioned credit rating system generation method or credit rating method.

[0025] In a seventh aspect, an embodiment of the present application provides a computer program product, which, when called and executed by an electronic device, enables the electronic device to execute the above-mentioned credit rating system generation method or credit rating method.

[0026] In the embodiment of the present application, in response to a data source selection operation, a target data source is determined from a pre-configured data source set, and different data sources in the data source set are used to provide different credit rating reference data; and in response to a rating component selection operation, at least one rating component is determined from a pre-configured rating component set, and different rating components in the rating component set correspond to different credit rating types; in response to a rating parameter configuration request, the credit rating parameters of each pre-configured parameter component are configured, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters; in response to a system generation request, a credit rating system is generated based on the selected rating component and the configured credit rating parameters of each parameter component. In this way, the credit rating business of each subsidiary can be presented in the form of a rating component, and the differences between each subsidiary in each rating component can be presented through the credit rating parameters of the parameter component, and a credit rating system that meets the needs of each subsidiary can be developed in a componentized manner, which can reduce repeated development work and quickly develop a credit rating system that meets the needs of each subsidiary. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0028] Figure 1 A schematic diagram of a software architecture for generating a credit rating system provided in an embodiment of the present application;

[0029] Figure 2 A schematic diagram of a conversion process of external data provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of the structure of a basic component layer provided in an embodiment of the present application;

[0031] Figure 4 A processing flow of an indicator management component provided in an embodiment of the present application;

[0032] Figure 5 A processing flow of a model management component provided in an embodiment of the present application;

[0033] Figure 6 A processing flow of a job scheduling component provided in an embodiment of the present application;

[0034] Figure 7 A schematic diagram of the structure of a core application layer provided in an embodiment of the present application;

[0035] Figure 8 A schematic diagram of the management of an investment pool provided in an embodiment of the present application;

[0036] Fig. 9 A schematic diagram of a quota management provided in an embodiment of the present application;

[0037] Fig.10 A schematic diagram of the architecture of a credit rating system provided in an embodiment of the present application;

[0038] Fig.11 A schematic diagram of a credit rating process provided in an embodiment of the present application;

[0039] Fig.12 A schematic diagram of another credit rating process provided in an embodiment of the present application;

[0040] Fig.13 A flowchart of a method for generating a credit rating system provided in an embodiment of the present application;

[0041] Fig.14A flowchart of a method for generating a credit rating system provided in another embodiment of the present application;

[0042] Fig.15 A flowchart of a method for generating a credit rating system provided in another embodiment of the present application;

[0043] Fig.16 A flow chart of a credit rating method provided in an embodiment of the present application;

[0044] Fig.17 A schematic diagram of the structure of a generating device for a credit rating system provided in an embodiment of the present application;

[0045] Fig.18 A schematic diagram of the structure of a credit rating device provided in an embodiment of the present application;

[0046] Fig.19 A schematic diagram of the hardware structure of an electronic device for implementing a method for generating a credit rating system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to solve the problem in the related art that different subsidiaries have different credit rating requirements and it is difficult to quickly develop a credit rating system that meets the needs of each subsidiary, the embodiments of the present application provide a method for generating a credit rating system and a credit rating method.

[0048] The preferred embodiments of the present application are described below in conjunction with the drawings of the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. In addition, in the embodiments of the present application, the acquisition, storage, use, and processing of data are in compliance with the relevant provisions of national laws and regulations.

[0049] In order to facilitate the understanding of this application, the technical terms involved in this application are:

[0050] Credit bonds are bonds issued by entities other than the government and with a definite principal and interest repayment cash flow. They include enterprise bonds, corporate bonds, short-term financing bills, medium-term notes, separate trading convertible bonds, asset-backed securities, subordinated bonds, etc. Different subsidiaries may have different definitions of the scope of credit bonds.

[0051] Credit rating, also known as credit rating, refers to the rating of entities and debts with credit risks. The rating system includes rating symbols, rating indicators, and rating models. Among them, rating symbols are used to indicate the level of credit rating according to certain rules (such as the credit rating corresponding to AAA is higher than the credit rating corresponding to AA, and the credit rating corresponding to level 1 is higher than the credit rating corresponding to level 9); rating indicators are used to indicate the structured data used for rating, such as the company's operating level, management ability, financial status, etc.; rating models are used to use qualitative or quantitative analysis methods to obtain the final rating results by processing and calculating rating indicators. The types of credit rating objects include bond issuers and debt items.

[0052] External rating refers to the rating results issued by third-party rating agencies in the market on bond issuers and bonds.

[0053] Internal rating refers to the rating results of bond issuers and bonds within different subsidiaries.

[0054] Figure 1 A software architecture diagram for generating a credit rating system provided for an embodiment of the present application includes a unified data layer, a basic component layer, a core application layer and a combined application layer. Among them, the unified data layer is used to establish a unified credit rating data mart to achieve the unification of data standards and data models. Each subsidiary can freely purchase and select data sources, and the data gateway completes the cleaning and processing of heterogeneous data. The basic component layer is used to build a unified basic business component, including rating model management, calculation engine, custom formula indicators, job scheduling, data verification, public parameters, etc. The core application layer is used to implement unified core application components, including subject ratings, debt ratings, credit warnings, tracking reminders, investment pool management, limit management, etc. The combined application layer is used to implement specific credit rating and investment management processes. According to the business needs of each subsidiary, the components of the core application layer can be flexibly selected in the combined application layer, and the parameters of each component in the basic component layer can be configured to develop a credit rating system that meets the needs of each subsidiary in a configured manner.

[0055] The following is an introduction to these layers.

[0056] 1. Unified data layer.

[0057] Data is the core of the credit rating business. In actual business operations, since each subsidiary is an independent legal person, the external information data suppliers purchased by each subsidiary are likely to be different, and the data structure provided by each supplier will also be different. If data access is performed separately for each subsidiary, the development workload will be very huge. Therefore, the first step of this solution is to build a unified data layer through a data gateway, that is, to convert different external data sources according to unified rules to obtain internal standard structured data, that is, the unified data layer stores data structures in a unified format.

[0058] like Figure 2 As shown, you can follow Figure 2 The steps shown in the figure dump different external data into internal standard structured data through unified dumping rules. The processing steps of the unified data layer include:

[0059] 1. Establish a public data model: Through the analysis and modeling of multi-source external data, the public data is classified by structure. For example, the bond category may include bond information table, bond issuance and increase table, bond classification table, bond scale table, bond interest calculation period table, bond default information table, bond guarantee risk table, etc. The table structure field rules are modeled by classification.

[0060] 2. Write dump rule scripts: Map the table structure fields of different data sources according to the public data model, write dump rules and code scripts for different data sources, and flexibly control the scripts according to the data sources of different subsidiaries.

[0061] 3. Data cleaning: Clean the valid data in the external data source according to the dump rules, and eliminate invalid data and data that is useless according to business rules.

[0062] 4. Dump of all historical data: During the online initialization, the full amount of historical data will be dumped in segments and stored in the database of the unified data layer.

[0063] 5. Scheduled scheduling: Configure the scheduled scheduling task for data refresh according to the update frequency of external data and actual business requirements.

[0064] 6. Incremental data dump: According to the configured update task frequency, the incremental data is regularly dumped to ensure that the unified data layer has the latest and most complete full data.

[0065] 7. Daily monitoring: Regularly monitor the execution of scheduled tasks according to business needs to see if they are successfully executed. If the execution fails, find the problem in time and manually initiate incremental data transfer. If the execution is successful, it is also necessary to check from the business level whether the data-related business functions are normal and available.

[0066] In this way, building a unified data mart and uniformly modeling the information of external data suppliers with different sources and structures can shield a large part of the demand analysis workload caused by company differences. It can not only enhance the group's internal data modeling capabilities in the credit rating business field, but also greatly shorten the time from business demand analysis to implementation and development.

[0067] 2. Basic component layer.

[0068] The functions mainly used to support the core application layer can be configurably developed according to the business needs of different subsidiaries, reducing the amount of code duplication and realizing the principal rating, debt rating and business operation process of different subsidiaries, thereby realizing the group integrated management of credit rating business functions. Therefore, the design of the basic component layer should decouple the underlying application components as much as possible. Figure 3 As shown in the figure, the main basic components involved in this solution include: common parameter component, indicator management component, model management component and job scheduling component. These components are introduced below.

[0069] 1. Public parameter components.

[0070] The purpose of establishing a public parameter component is to flexibly support the personalized needs of different subsidiaries. The business scenarios that can be abstracted into public parameter configurations in actual needs include: data dictionary maintenance and rule control. The following introduces these two business scenarios respectively.

[0071] (1)Data dictionary maintenance class.

[0072] External information data based on the public data model has a unified standard for the public market for various basic attributes including the subject and the debt, such as the administrative region (province, city, district) where the subject is located, the nature of the enterprise (state-owned / private), industry classification (retail / real estate), etc. However, in actual business needs, the credit rating research logic and classification standards of different subsidiaries are different. Taking the main industry classification of Subsidiary A and Subsidiary B as an example, the main industry classification of Subsidiary A is shown in Table 1, and the industry classification of Subsidiary B is shown in Table 2.

[0073] Table 1

[0074]

[0075] Table 2

[0076]

[0077]

[0078] Among them, Subsidiary A divides the subject into 30 categories by industry: urban investment, chemical fiber... gold, shipping, and each category of subject corresponds to a different credit rating analysis model. Subsidiary B divides the subject into 23 primary industries: commerce, steel, non-ferrous metals... non-bank finance, others, and the 23 primary industries are further divided into 65 secondary industry types, and each category or several categories of primary industries correspond to different credit rating analysis models.

[0079] From the above, it can be seen that the public parameter component needs to provide data dictionary maintenance for industry classification and support different subsidiaries to maintain their own personalized industry classification names and code values. In this way, it can not only flexibly support the different business needs of different subsidiaries, but also avoid re-development of code when the business industry classification changes and reduce the amount of repeated development code.

[0080] (2) Rule control category.

[0081] Credit rating results are usually divided into external rating results and internal rating results. External rating results provided by third-party rating agencies can be accessed through a unified data gateway. Different rating agencies can update the external rating symbols of entities or debt items from time to time. Although internal rating results are generated within the credit rating system, researchers are also required to track them regularly at a certain frequency based on changes in symbol levels and investment conditions. There are three types of situations in actual business:

[0082] ① Rating agencies as sources of external rating data: Different subsidiaries have different screening scopes for external rating agencies when viewing external rating data of entities and bonds. For example, among the current external information data, there are 21 rating agencies providing rating result data. Subsidiary A only looks at external rating data from 9 selected agencies, and Subsidiary B only looks at external rating data from 10 selected agencies.

[0083] ② The time range of external rating data display: As the operating conditions of the bond issuer and the macro-industry change, the timeliness of the external rating results will also change. For example, Subsidiary A currently requires the credit rating system to only provide external rating data for the last 18 months, but may change to provide external rating data for the last 24 months in the future.

[0084] ③Frequency of internal rating data tracking: Different internal rating levels correspond to different tracking frequencies. The rating symbol is valid within the tracking period + buffer period. After exceeding the tracking frequency + buffer period, the rating symbol becomes invalid. It is necessary to remind the researcher to track before the rating symbol becomes invalid. The lower the level, the higher the tracking frequency. In addition, the tracking frequency and buffer period may change with the business situation. Table 3 is an example of the relationship between the internal rating level and the tracking frequency.

[0085] Table 3

[0086] Internal rating Tracking frequency (with positions) Tracking frequency (no position) AB file 12 months 24 months CD file 12 months 18 months EF file 12 months 12 months GH 6 months ×

[0087] From the above, it can be seen that the public parameter component needs to provide maintenance for the above rule control parameters so that the credit rating system can respond promptly to changes in business needs by adjusting the parameter configuration.

[0088] Based on actual project experience, this solution proposes two types of common parameters, including data dictionary maintenance class and rule control class, which can basically cover most of the demand changes in current business scenarios caused by differences in business models of different subsidiaries or changes in business logic of the same company. It can minimize the workload of repeated code development and shorten the change time of version iteration.

[0089] 2. Indicator management component.

[0090] Indicators refer to specific data used in credit rating analysis. Indicators are used to analyze and calculate rating models. The processing flow of the indicator management component is as follows: Figure 4 shown.

[0091] (1) Indicator classification

[0092] The public data model classifies external data into structured categories according to business. The first step in managing credit rating model indicators is also to classify the indicators. For example, the indicators of subject data may include: operating indicators, financial indicators, risk indicators, etc.

[0093] (2) Definition of basic indicators.

[0094] Basic indicators include external indicators obtained from the outside through a unified data gateway and system internal indicators obtained according to certain calculation methods. According to the classification of indicators, relevant data can be extracted and abstracted into basic indicators, such as total assets, total liabilities, and calculated debt-to-asset ratio (total liabilities / total assets) can all be defined as basic indicators, thus forming an internal basic indicator library for the credit rating system.

[0095] (3) Derived indicator formula.

[0096] Derivative indicators refer to the use of internal basic indicator libraries and optional formulas provided by components to build user-defined derivative indicators for use in the calculation and analysis of subsequent rating models.

[0097] Common derivative indicator formulas include:

[0098] ① Arithmetic operation formulas: such as +, -, *, / ;

[0099] ②Time series / cross-section statistical formulas: such as sum / min / max / avg / std / quantile / fractile;

[0100] ③Time series / cross-sectional regression formulas: such as beta / correlation / exposure, etc.;

[0101] ④Condition calculation formula: every / any;

[0102] ⑤Logical judgment formula: and / or;

[0103] ⑥Function formula: return / abs / if / .

[0104] Considering that the credit rating business involves a large amount of structured and unstructured data modeling and indicator analysis, different companies or research teams have different analysis logic and focus, so the construction of rating model indicators needs to be very flexible. The indicator management component of this solution can support different subsidiaries to build derivative indicators required by the credit rating business according to custom logic.

[0105] 3. Model management component.

[0106] Based on the indicator management component, the model management component can provide a configurable model building method to support different subsidiaries to manage their own rating models according to different credit rating analysis methods. Figure 5 shown.

[0107] (1) Rating symbol management.

[0108] The first step in determining the rating model is to determine the scale and definition of the rating symbols, that is, to determine whether the symbols for credit ratings from high to low are 1-10 or AH.

[0109] (2)Indicator system management.

[0110] Constructing an indicator system means constructing a complete rating indicator system according to a certain indicator hierarchy by using the basic indicators or derived indicators used in the rating model.

[0111] (3) Indicator weight management.

[0112] The sum of the indicator weights of each level in the rating indicator model is 100%, and different rating models may assign different weights to the same indicator. Indicator weight management supports different subsidiaries to independently set indicator weights.

[0113] (4) Model calculation engine.

[0114] After the model is built, the indicators, calculation rules and public data in the indicator management component can be called to calculate the rating results of the current rating object according to the indicator system and weights of the model.

[0115] The model management component can centrally manage different model indicator systems and weight information. Most importantly, it can achieve logical isolation of functions and physical isolation of data between different subsidiaries.

[0116] 4. Job scheduling component.

[0117] The job scheduling component is used to design the calls to system functions by different business processes. The business processes of different subsidiaries vary greatly. For example, Subsidiary A needs to call the rating model when rating the principal, and the bond rating is directly judged and adjusted manually based on the principal rating results; Subsidiary B's rating object does not exist as a principal, such as asset-backed securitization (ABS), so the rating model is directly called to rate the bond. Therefore, it is necessary to provide a job scheduling component that can adapt to different business processes to meet the business process requirements of different subsidiaries. The processing process of the job scheduling component is as follows: Figure 6 shown.

[0118] (1)Job definition.

[0119] Define business scenarios, such as the debt rating scenario of subsidiary A and the asset-backed securities rating scenario of subsidiary B. The functional components used in different scenarios and the order in which the components are used can all be defined and set.

[0120] (2) Scheduling engine.

[0121] After defining the business scenarios and jobs, the scheduling engine initiates scheduling under certain trigger conditions to implement the calling of different functional modules.

[0122] 3. Core application layer.

[0123] Mainly used to provide some specific functions at the business level, such as Figure 7 As shown, the core application layer includes the subject rating component, the debt rating component, the tracking and early warning component, the investment pool management component and the limit management component.

[0124] These components are introduced below.

[0125] 1. Entity rating components.

[0126] The subject rating component supports initiating subject rating operations, calling rating models to form rating results, and generating rating reports by querying market issuer information in public data.

[0127] 2. Bond rating components.

[0128] The bond rating component supports initiating bond rating operations by querying the outstanding bond information in public data, calling the subject rating results or rating model to form rating results, and generating rating reports.

[0129] 3. Tracking and early warning components.

[0130] Generally, both internal and external rating results need to be updated regularly. When external rating results change or internal rating symbols exceed the tracking validity period, tracking warnings are required to prompt users to update internal rating symbols.

[0131] 4. Investment pool management component.

[0132] The investment pool management component supports automatically forming a pool of subsidiaries that can or cannot bid according to certain rules after the rating of the subject and debt items is completed, which is used for investment risk control management. Figure 8 As shown, for the group-integrated credit rating management system, there is information interaction between the group domain and the subsidiary domain. The content of the interaction mainly includes the branch's recommendation of bonds to subsidiaries, and the subsidiary's investment scale in the group's underwritten bonds or the branch's recommended bonds.

[0133] 5. Quota management component.

[0134] The limit management component mainly supports the integrated credit management of the group, including the limits for the industry, entity, bond, counterparty, etc. Fig. 9 As shown, the group's integrated credit rating management system supports unified management of quotas at the group level. Subsidiaries can obtain real-time information on the remaining quotas allocated to the group as a whole and its subsidiaries to determine whether investment risk control requirements are met. After the investment transaction is completed, the transaction results can also be fed back in real time to update the group's quota information.

[0135] 4. Combined application layer.

[0136] The combined application layer is used to select the corresponding data source based on the external data supplier purchased by the subsidiary, select the business components of the core application layer based on the business needs of the subsidiary, and perform parameter configuration and job scheduling engine settings in the basic application layer. Finally, it is customized and assembled into a credit rating system that realizes credit rating and investment management.

[0137] In summary, according to the business architecture design of this application, the needs of subsidiaries can be met in an assembly manner. Fig.10A schematic diagram of the architecture of a credit rating system provided for an embodiment of the present application, the group has a database of subsidiary A and a database of subsidiary B, through which subject information and debt information are provided to the outside. Any subsidiary conducts subject rating through the corresponding subject information queried from the group, and conducts debt rating through the corresponding debt information queried from the group, and feeds back the rating information formed by the subject rating results and the debt rating results to the group. In addition, any subsidiary can also feed back the investment pool information of its own investment pool management, such as investment quota, to the group. In this way, not only can efficient interaction of data between the group head office and subsidiaries be achieved, but also physical isolation of legal person data can be achieved.

[0138] The scheme of the embodiment of the present application is introduced below in conjunction with specific embodiments.

[0139] The embodiment of the present application provides a business architecture solution that can organize and build system applications according to the actual needs of the credit rating business of different subsidiaries, avoid repeated development due to subtle differences in rules for homogeneous needs as much as possible, and thus improve the adaptation efficiency of the system between different subsidiaries within the group.

[0140] For example, according to the traditional business system development model, when Subsidiary A proposes system development requirements to the technical department, it will propose the following requirements:

[0141] ① The purchased external information data source is data source S1;

[0142] ② The symbol system of credit rating is ABCDEFGH (8 levels), and the industry classification is 30 first-level industries, without subdividing the second-level industries;

[0143] ③ The indicators used in the credit rating model are all annual report financial data (including balance sheet, income statement, and cash flow statement);

[0144] ④ The credit rating model has a two-level indicator system, and the indicator weight is 100% of the total of the secondary indicators;

[0145] ⑤The business process of credit rating is initial evaluation by credit rating analyst - review and confirmation by credit rating supervisor.

[0146] After the technical department conducts customized development based on the above requirements, all data models, rating systems, and business processes in the credit rating application presented to users will be solidified according to the requirements.

[0147] If at this time, subsidiary B puts forward the following business requirements:

[0148] ①The source of external information purchased is S2;

[0149] ② The symbol system of credit rating is 123456789 (9 levels), and the industry classification is 23 primary industries and 65 secondary industries;

[0150] ③In addition to the annual report financial data (including balance sheet, income statement, cash flow statement), the indicators used in the credit rating model also include the operating data obtained from the researcher's survey and the derivative indicators of the financial data;

[0151] ④ The credit rating model has a three-level indicator system, and the indicator weight is 100% of the total of the three-level indicators;

[0152] ⑤The business process of credit rating is initial review by credit rating researchers and confirmation by voting by the credit rating team.

[0153] By comparison, although the user needs of subsidiaries A and B are both the most basic functions of credit rating, due to differences in data models, rating systems, and business processes, similar or even identical business functions must be repeatedly developed, which is a huge waste of development resources.

[0154] To this end, we analyze the differences in the above requirements ①-⑤, abstract the differentiated requirements into a unified data layer and basic component layer that can be configured, and separate some solidified rules from business functions for parameterized configuration. Figure 1 The software architecture shown, where:

[0155] The unified data layer solves the differences in requirements①;

[0156] The public parameter components in the basic component layer solve the differences in requirement ②;

[0157] The indicator management component in the basic component layer solves the difference in requirement ③;

[0158] The model management component in the basic component layer solves the difference in requirement ④;

[0159] The job scheduling component in the basic component layer solves the difference in requirements⑤.

[0160] In this architecture, the unified data layer is the premise and foundation for all functional development. It can shield the differences in data structure between different data sources, so that all functional development can obtain data from a set of common data models, thereby ensuring the unification and stability of the underlying data model. The core application layer is the basic functional module. Generally, the basic functional modules used by different subsidiaries include principal rating and debt rating. The basic component layer is the parameter configuration of the basic functional module, which is the parameter that must be input when different functions are actually operated. For the credit rating system, the public parameter component, indicator management component, model management component, and job scheduling component are all settings for operating parameters. The main purpose of abstracting these operating parameters into configurable components is to meet the differentiated needs of different subsidiaries through parameter adjustment, thereby avoiding repeated development due to different needs. The combined application layer is the actual function that is finally presented to the user under the joint action of functions and parameters. For Subsidiary A, the functions of the core application layer are combined with the configuration of the basic component layer, and the final result is a credit rating system that meets the needs of Subsidiary A.

[0161] Take Subsidiary A as an example. Fig.11 A schematic diagram of a credit rating process provided for an embodiment of the present application includes nine steps: ① query of subject debt information, ② configuration of subject industry type, ③ configuration of rating indicator data, ④ configuration of subject rating model, ⑤ generation of subject rating results, ⑥ review of subject rating results, ⑦ generation of debt rating results, ⑧ review of debt rating results and ⑨ entry of bonds into the investment pool.

[0162] The following uses the credit rating process of subsidiary A as an example to introduce the relationship between components in different layers. Fig.12A schematic diagram of another credit rating process provided in an embodiment of the present application, in ① the subject debt information query link, the subject rating component queries the data required for the subject rating from the company data, and the debt rating component queries the data required for the debt rating from the bond data, in ② the subject industry type configuration link, the subject industry type is configured in the public parameter component based on the industry classification data, in ③ the rating indicator data configuration link, the indicators required for rating are configured in the indicator management component based on the indicator data, in ④ the subject rating model configuration link, the rating model is configured in the model management component based on the indicator management, and in ⑤ the subject rating result is generated In the ⑥ stage of reviewing the subject rating results, the subject rating results are reviewed. Only after the review is passed can the subsequent stages be carried out. In the ⑦ stage of generating bond rating results, the bond rating components are used to perform bond ratings and obtain bond rating results. In the ⑧ stage of reviewing the bond rating results, the bond rating results are reviewed. Only after the review is passed can the subsequent stages be carried out. In the ⑨ stage of bonds entering the investment pool, if it is determined that the bond rating results meet the preset conditions for entering the investment pool, the current bond will be added to the investment pool.

[0163] In the field of credit rating business, this solution can not only meet the application requirements of business scenarios and processes of different subsidiaries, but also meet the scenarios and coordination requirements of information interaction between the banking group and its subsidiaries, and realize the efficient sharing of group credit rating information, investment pool information, quota information and other data, and minimize the repeated development of information systems within the group for homogeneous businesses.

[0164] The following uses bond Q as an example to illustrate how to use the credit rating system generated by the above architecture.

[0165] Step 1: Query the principal debt information.

[0166] The unified data layer obtains company data and bond data from external information data providers. The subject rating component in the core application layer queries the basic information of the subject (i.e. the issuer of bond Q) from the company data, such as the name, number, establishment date, organizational form, organization code, whether it is a branch, whether it is listed, registered capital, number of employees, etc. of the issuing institution; the debt rating component in the core application layer queries the basic information of the bond from the bond data, such as the full name of the bond, issue price, issue scale, current scale, latest rating of the issuer, latest rating of the bond, face value of the bond, type of bond, fundraising method, redemption date, interest calculation method, etc.

[0167] Step 2: Configure the main industry type.

[0168] Based on the industry classification data (such as 30 primary industries) obtained from external information data in the unified data layer, the main industry type of bond Q is set in the public parameter component of the basic component layer according to the credit rating research system of subsidiary A.

[0169] For example, external data shows that the main industry classification of bond Q is non-bank finance, but according to the research system of subsidiary A, the classification of this industry belongs to the first-level classification: non-bank finance, second-level classification: life insurance. The industry classification of bond Q (i.e., first-level classification: non-bank finance, second-level classification: life insurance) can be set in the public parameter component, which is generally a mapping relationship setting.

[0170] Step ③: Configure rating indicator data.

[0171] Based on the indicators required by the current credit rating system, the indicator values ​​required for the rating are entered in the indicator management component of the basic component layer.

[0172] Step ④: Configure the entity rating model.

[0173] In the model management component of the basic component layer, the indicator level and indicator weight (secondary indicators, weights totaling 100%) of the rating model are set and entered. Table 4 is an example of the relationship between an indicator level and an indicator weight.

[0174] Table 4

[0175]

[0176]

[0177] Among them, the specific values ​​of the secondary indicators are configured and processed in step ③, and the final scoring result (such as 85 points) is obtained by weighted calculation based on the indicators and weights configured in the model.

[0178] Step 5: Generate entity rating results.

[0179] Based on the subject rating component in the core application layer, combined with the parameter configuration of the three-step basic component layer ②③④, the subject rating result of the subject corresponding to bond Q is finally generated.

[0180] Assuming that the main rating result is 85 points and the main rating result corresponding to 85 points is Level 3, the main rating result corresponding to Bond Q is Level 3.

[0181] Step ⑥: Review the entity rating results.

[0182] According to the settings of the job scheduling component in the basic component layer, the subject rating process of Subsidiary A is preliminary evaluation by researchers - review by the credit rating supervisor, and the subject rating is determined only after the review is completed. Therefore, according to the set process, the researcher can submit the subject rating results to the credit rating supervisor for review, and the subject rating results will be saved after the review is passed.

[0183] Step 7: Generate bond rating results.

[0184] Based on the debt rating component in the core application layer, the initial debt rating results are generated by combining the main rating results and the manual adjustments made by the researcher of subsidiary A.

[0185] According to the debt rating rules of subsidiary A, if there is no guarantee clause in the bond, the bond rating will directly inherit the main body rating result. If there is a guarantee clause or other special clause, the researcher will make a judgment based on the main body rating to increase or decrease it by no more than 2 levels. After the researcher checked the terms and conditions of the relevant issuance announcement of bond Q, it was found that bond Q has a guarantor and the guarantor has a high credit rating. Based on this, the researcher believes that the bond rating of bond Q can be increased by 1 level based on the main body rating, that is, the bond rating result is 2 levels.

[0186] Step ⑧: Review the debt rating results.

[0187] According to the settings in the job scheduling component in the basic component layer, the bond rating process of Subsidiary A is preliminary evaluation by researchers - review by the credit rating supervisor, and the bond rating is confirmed only after the review is completed. Therefore, according to the set process, the researcher can submit the bond rating results to the credit rating supervisor for review, and the bond rating results will be saved after the review is passed.

[0188] Step 9: Bonds enter the investment pool.

[0189] Based on the investment pool management component in the core application layer, the bond Q that has completed the credit rating can be maintained in the corresponding investment pool (investable / forbidden to invest) according to the investment product entry standard of subsidiary A, and the basic business process of credit rating is completed.

[0190] Assume that the bond investment pool entry system of subsidiary A requires that bonds with bond ratings in the 1-7 range can enter the investable pool, and bonds with ratings in the 8-10 range can only enter the prohibited pool (this rule can also be configured in the public parameter component). Then, based on the bond rating results generated in the previous step, bond Q will be pushed into the investable pool according to the configured parameters. When investors check the range of the investable pool, they can find bond Q and proceed with the subsequent investment process.

[0191] The solution of the embodiment of the present application is introduced below with reference to a specific flow chart.

[0192] Fig.13 A flowchart of a method for generating a credit rating system provided in an embodiment of the present application includes the following steps.

[0193] In step 1301 , in response to a data source selection operation, a target data source is selected from a preconfigured data source set, wherein different data sources in the data source set are used to provide different credit rating reference data.

[0194] Generally, the data formats of credit rating reference data provided by different data sources in the data source set have been converted into a unified data format in advance. This can shield the differences in data structures among different data sources, which not only allows each subsidiary to freely choose the data source it wants, but also helps to speed up the development of the credit rating system.

[0195] During specific implementation, the target data source is determined by the developer based on the data source requirements of the current user.

[0196] In step 1302, in response to a rating component selection operation, at least one rating component is selected from a pre-configured rating component set, wherein different rating components in the rating component set correspond to different credit rating types.

[0197] In actual applications, the rating component set may include a principal rating component and a bond rating component, wherein the principal rating component is used for principal rating and the bond rating component is used for bond rating. That is, the principal rating component and the bond rating component correspond to different credit rating types.

[0198] Since in some businesses, debt items can be rated directly, while in other businesses, the subject must be rated first and then the debt items, the rating components selected from the rating component set may include only the debt item rating component, or may include both the subject rating component and the debt item rating component. It should be noted that the rating component finally selected may be determined by the technical staff based on the current business needs of the user.

[0199] In step 1303, in response to a rating parameter configuration request, the credit rating parameters of each pre-configured parameter component are configured, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters.

[0200] In specific implementation, each parameter component may include a common parameter component, an indicator management component and a model management component. In addition, since the industry classifications, indicators and indicator levels provided by different data sources will be different, the credit rating parameters of each parameter component need to be determined in combination with the rating reference data that the target data source can provide.

[0201] In step 1304, in response to the system generation request, a credit rating system is generated based on the selected rating components and the credit rating parameters of the configured parameter components.

[0202] In specific implementation, after receiving the system generation request, the credit rating system that meets the current user needs can be built by using the selected rating components and the credit rating parameters of each parameter component configured.

[0203] It should be noted that there is no strict sequence relationship between the above steps 1301 and 1302.

[0204] In order to improve the flexibility of credit rating, the job scheduling component can also be pre-configured. The technical staff can change the rating process corresponding to a rating component by configuring the scheduling parameters of the job scheduling component.

[0205] Fig.14 A flowchart of another method for generating a credit rating system provided in an embodiment of the present application includes the following steps.

[0206] In step 1401 , in response to a data source selection operation, a target data source is selected from a preconfigured data source set, wherein different data sources in the data source set are used to provide different credit rating reference data.

[0207] In step 1402, in response to a rating component selection operation, at least one rating component is selected from a pre-configured rating component set, wherein different rating components in the rating component set correspond to different credit rating types.

[0208] In step 1403, in response to the scheduling parameter configuration request, the scheduling parameters of the pre-configured job scheduling component are configured, and the scheduling parameters of the job scheduling component are used to determine the rating process of the selected rating component.

[0209] Among them, the rating process may include an initial review by a credit rating researcher and then a vote confirmation by the credit rating team. Another example is an initial review by a credit rating researcher and then a review and confirmation by a credit rating supervisor.

[0210] In step 1404, in response to a rating parameter configuration request, the credit rating parameters of each pre-configured parameter component are configured, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters.

[0211] In step 1405, in response to the system generation request, a credit rating system is generated based on the selected rating component, the scheduling parameters of the configured job scheduling component, and the credit rating parameters of the configured parameter components.

[0212] In specific implementation, after receiving the system generation request, the selected rating component, the scheduling parameters of the configured job scheduling component, and the credit rating parameters of each parameter component can be used to build a credit rating system that meets the current user needs.

[0213] In addition, in order to enrich the functionality of the generated credit rating system, a management component set may be configured to manage the credit rating objects with the help of the management components in the management component set.

[0214] Fig.15 A flowchart of another method for generating a credit rating system provided in an embodiment of the present application includes the following steps.

[0215] In step 1501 , in response to a data source selection operation, a target data source is selected from a preconfigured data source set, wherein different data sources in the data source set are used to provide different credit rating reference data.

[0216] In step 1502, in response to a rating component selection operation, at least one rating component is selected from a pre-configured rating component set, wherein different rating components in the rating component set correspond to different credit rating types.

[0217] In step 1503, in response to the scheduling parameter configuration request, the scheduling parameters of the pre-configured job scheduling component are configured, and the scheduling parameters of the job scheduling component are used to determine the rating process of the selected rating component.

[0218] Among them, the rating process may include an initial review by a credit rating researcher and then a vote confirmation by the credit rating team. Another example is an initial review by a credit rating researcher and then a review and confirmation by a credit rating supervisor.

[0219] In step 1504, in response to the management component selection operation, at least one management component is selected from a pre-configured management component set, wherein different management components in the management component set correspond to different rating management links.

[0220] Generally, the management component set may include an investment pool management component, a tracking and warning component, and a limit management component, wherein the investment pool management component corresponds to the investment pool management link, and is used to manage the credit rating objects in the investment pool; the tracking and warning component corresponds to the tracking and warning link, and is used to perform tracking and warning processing on the credit rating objects; the limit management component corresponds to the limit management link, and is used to limit the investment amount of the credit rating objects.

[0221] In other words, the credit rating system developed can combine investment pool management, tracking and early warning, and limit management capabilities, with richer functions.

[0222] In step 1505, in response to a rating parameter configuration request, the credit rating parameters of each pre-configured parameter component are configured, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters.

[0223] In specific implementation, each parameter component may include a common parameter component, an indicator management component and a model management component. In addition, since the industry classifications, indicators and indicator levels provided by different data sources will be different, the credit rating parameters of each parameter component need to be determined in combination with the rating reference data that the target data source can provide.

[0224] In step 1506, in response to the system generation request, the credit rating system is generated based on the selected rating component, the selected management component, the configured scheduling parameters of the job scheduling component, and the configured credit rating parameters of each parameter component.

[0225] In specific implementation, after receiving the system generation request, the selected rating component, the selected management component, the configured scheduling parameters of the job scheduling component, and the configured credit rating parameters of each parameter component can be used to build a credit rating system that meets the current user needs.

[0226] Fig.16 A flowchart of a credit rating method provided in an embodiment of the present application, the credit rating method is applied to a credit rating system, the credit rating system includes at least one rating component and parameter components with configured credit rating parameters, different rating components correspond to different credit rating types, different parameter components correspond to different types of credit rating parameters, the method includes the following steps.

[0227] In step 1601, in response to a parameter value configuration request, the parameter values ​​of the credit rating parameters of each parameter component are configured, wherein the configured parameter values ​​are determined based on the credit rating data of the object to be rated.

[0228] During the specific implementation, credit rating data such as the industry to which the credit rating object belongs, the issuer of the credit rating object, the issuance scale, etc., are different for different credit rating objects, so it is necessary to configure the parameter values ​​of the credit rating parameters of each parameter component according to the credit rating data of the current object to be rated.

[0229] In step 1602, a credit rating is performed on the object to be rated based on the credit characterization data of the object to be rated, the rating components and the parameter values ​​of the configured parameter components, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

[0230] Among them, the credit characterization data of the subject to be rated include the industry situation of the industry to which the subject to be rated belongs, the debt situation of the issuing entity of the subject to be rated, etc.

[0231] In some embodiments, the credit rating system may also include a job scheduling component with configured scheduling parameters. The scheduling parameters of the job scheduling component are used to determine the rating process of the rating component. At this time, the credit rating of the rated object can be performed based on the credit characterization data, the rating component, the parameter values ​​of each configured parameter component, and the scheduling parameters of the job scheduling component to ensure that the credit rating process meets the requirements.

[0232] In some embodiments, the credit rating system may also include at least one management component, and different management components correspond to different rating management links. In this case, after the credit rating of the rated object is performed, the rated object can also be managed based on the credit rating results and the management component.

[0233] The management component may be any combination of an investment pool management component, a tracking and early warning component, and a limit management component.

[0234] For example, after conducting a credit rating on the object to be rated, a decision is made whether to add the object to be rated to the investment pool based on the credit rating result and the entry conditions of the investment pool. If the object to be rated is added to the investment pool, a tracking period and early warning conditions can also be set for the object to be rated, and the investment amount of the object to be rated can be set to manage the investment risk of the object to be rated.

[0235] When the method provided in the embodiments of the present application is implemented by software or hardware or a combination of software and hardware, the electronic device may include multiple functional modules, and each functional module may include software, hardware or a combination thereof.

[0236] Based on the same technical concept, the embodiment of the present application also provides a device for generating a credit rating system. The principle of solving the problem by the device for generating a credit rating system is similar to that of the above-mentioned method for generating a credit rating system. Therefore, the implementation of the device for generating a credit rating system can refer to the implementation of the method for generating a credit rating system, and the repeated parts will not be repeated. Fig.17 A schematic diagram of the structure of a generation device of a credit rating system provided in an embodiment of the present application includes a data source selection module 1701, a rating component selection module 1702, a business parameter configuration module 1703, and a generation module 1704, wherein:

[0237] A data source selection module 1701 is used to select a target data source from a pre-configured data source set in response to a data source selection operation, where different data sources in the data source set are used to provide different credit rating reference data;

[0238] A rating component selection module 1702, configured to select at least one rating component from a pre-configured rating component set in response to a rating component selection operation, wherein different rating components in the rating component set correspond to different credit rating types;

[0239] The business parameter configuration module 1703 is used to configure the credit rating parameters of each pre-configured parameter component in response to the rating parameter configuration request, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters;

[0240] The generation module 1704 is used to generate a credit rating system in response to a system generation request based on the selected rating components and the credit rating parameters of the configured parameter components.

[0241] In some embodiments, the data formats of the credit rating reference data provided by different data sources in the data source set have been pre-converted into a unified data format.

[0242] In some embodiments, it also includes:

[0243] The scheduling parameter configuration module 1705 is used to configure the scheduling parameters of the pre-configured job scheduling component in response to the scheduling parameter configuration request, wherein the scheduling parameters of the job scheduling component are used to determine the rating process of the selected rating component;

[0244] The generating module 1704 is further used to generate the credit rating system based on the selected rating component, the configured scheduling parameters of the job scheduling component and the configured credit rating parameters of each parameter component.

[0245] In some embodiments, it also includes:

[0246] A management component selection module 1706, configured to select at least one management component from a pre-configured management component set in response to a management component selection operation, wherein different management components in the management component set correspond to different rating management links;

[0247] The generation module 1704 is also used to generate the credit rating system in response to the system generation request based on the selected rating component, the selected management component, the configured scheduling parameters of the job scheduling component, and the configured credit rating parameters of each parameter component.

[0248] In some embodiments, the rating component set includes a subject rating component and a bond rating component; the parameter components include a common parameter component, an indicator management component and a model management component; the management component set includes an investment pool management component, a tracking and early warning component and a limit management component.

[0249] Based on the same technical concept, the embodiment of the present application also provides a credit rating device. The principle of solving the problem by the credit rating device is similar to that of the above-mentioned credit rating method. Therefore, the implementation of the credit rating device can refer to the implementation of the credit rating method, and the repeated parts will not be repeated. Fig.18 A schematic diagram of the structure of a credit rating device provided in an embodiment of the present application includes a parameter value configuration module 1801 and a rating module 1802, wherein:

[0250] The parameter value configuration module 1801 is used to configure the parameter value of the credit rating parameter of each parameter component in response to the parameter value configuration request, wherein the configured parameter value is determined based on the credit rating data of the object to be rated;

[0251] Rating module 1802 is used to perform credit rating on the object to be rated based on the credit characterization data of the object to be rated, the rating components and the parameter values ​​of the configured parameter components, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

[0252] In some embodiments, the credit rating system further comprises a job scheduling component configured with scheduling parameters, the scheduling parameters of the job scheduling component are used to determine the rating process of the rating component, and the rating module 1802 is further used to:

[0253] Based on the credit characterization data, the rating component, the parameter values ​​of each configured parameter component, and the scheduling parameters of the job scheduling component, the object to be rated is credit rated.

[0254] Rating module 1802, the credit rating system also includes at least one management component, different management components correspond to different rating management links, and also includes a management module 1803, which is used to:

[0255] After credit rating the object to be rated based on the credit representation data of the object to be rated, the rating component and the parameter values ​​of each configured parameter component, the object to be rated is managed based on the credit rating result and the management component.

[0256] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, the functional modules in the embodiments of the present application may be integrated in a processor, or may exist physically separately, or two or more modules may be integrated in one module. The coupling between the modules may be achieved through some interfaces, which are usually electrical communication interfaces, but it is not excluded that they may be mechanical interfaces or other forms of interfaces. Therefore, the modules described as separate components may or may not be physically separated, and may be located in one place or distributed to different locations of the same or different devices. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0257] After introducing the method and apparatus for generating a credit rating system according to an exemplary embodiment of the present application, next, an electronic device according to another exemplary embodiment of the present application is introduced.

[0258] In some possible implementations, the electronic device of the present application may include at least one processor and at least one memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the method according to various exemplary implementations of the present application described above in this specification.

[0259] Refer to the following Fig.19 To describe the electronic device 190 implemented according to this embodiment of the present application. Fig.19 The electronic device 190 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0260] like Fig.19 As shown, the electronic device 190 is in the form of a general electronic device. The components of the electronic device 190 may include but are not limited to: at least one processor 191, at least one memory 192, and a bus 193 connecting different system components (including the memory 192 and the processor 191).

[0261] Bus 193 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor, or a local bus using any of a variety of bus architectures.

[0262] The memory 192 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 1921 and / or a cache memory 1922 , and may further include a read-only memory (ROM) 1923 .

[0263] Memory 192 may also include a program / utility 1925 having a set (at least one) of program modules 1924, such program modules 1924 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0264] The electronic device 190 may also communicate with one or more external devices 194 (e.g., keyboards, pointing devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 190, and / or communicate with any device that enables the electronic device 190 to communicate with one or more other electronic devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 195. Furthermore, the electronic device 190 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 196. As shown, the network adapter 196 communicates with other modules for the electronic device 190 via a bus 193. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 190, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0265] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 192 including instructions, and the instructions can be executed by the processor 191 to complete the above-mentioned credit rating system generation method or credit rating method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0266] In an exemplary embodiment, a computer program product is also provided. When the computer program product is called and executed by an electronic device, the electronic device executes any exemplary method provided in the present application.

[0267] Furthermore, the computer program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a compact disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0268] The program product for generating a credit rating system or a program product for credit rating in the embodiments of the present application may adopt a CD-ROM and include program code, and may be run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0269] The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0270] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0271] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device via any type of network such as a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0272] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0273] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0274] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0275] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0276] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0277] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0278] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0279] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for generating a credit rating system, characterized in that: include: In response to a data source selection operation, a target data source is selected from a pre-configured data source set, where different data sources in the data source set are used to provide different credit rating reference data; as well as In response to a rating component selection operation, at least one rating component is selected from a pre-configured rating component set, wherein different rating components in the rating component set correspond to different credit rating types, and the rating component set includes an entity rating component and an item rating component; In response to a rating parameter configuration request, configure the credit rating parameters of each pre-configured parameter component, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters, and each parameter component includes a common parameter component, an indicator management component, and a model management component; In response to the system generation request, a credit rating system is generated based on the selected rating components and the credit rating parameters of the configured parameter components.

2. The method according to claim 1, characterized in that The data formats of the credit rating reference data provided by different data sources in the data source set have been converted in advance into a unified data format.

3. The method according to claim 1 or 2, characterized in that Also includes: In response to the scheduling parameter configuration request, configuring the scheduling parameters of the pre-configured job scheduling component, wherein the scheduling parameters of the job scheduling component are used to determine the rating process of the selected rating component; In response to the system generation request, a credit rating system is generated based on the selected rating component and the configured credit rating parameters of each parameter component, including: The credit rating system is generated based on the selected rating component, the configured scheduling parameters of the job scheduling component and the configured credit rating parameters of each parameter component.

4. The method according to claim 3, characterized in that Also includes: In response to the management component selection operation, at least one management component is selected from a pre-configured management component set, wherein different management components in the management component set correspond to different rating management links; In response to the system generation request, the credit rating system is generated based on the selected rating component, the configured scheduling parameters of the job scheduling component and the configured credit rating parameters of each parameter component, including: In response to the system generation request, the credit rating system is generated based on the selected rating component, the selected management component, the configured scheduling parameters of the job scheduling component, and the configured credit rating parameters of each parameter component.

5. The method according to claim 4, characterized in that The management component set includes an investment pool management component, a tracking and early warning component, and a limit management component.

6. A credit rating method, characterized in that: Applied to a credit rating system, the credit rating system includes at least one rating component and parameter components configured with credit rating parameters, the at least one rating component is selected from a subject rating component and a debt rating component, the parameter components include a common parameter component, an indicator management component and a model management component, different rating components correspond to different credit rating types, and different parameter components correspond to different types of credit rating parameters, the method includes: In response to the parameter value configuration request, configuring the parameter value of the credit rating parameter of each parameter component, wherein the configured parameter value is determined based on the credit rating data of the object to be rated; Based on the credit characterization data of the object to be rated, the rating components and the parameter values ​​of the configured parameter components, the object to be rated is credit rated, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

7. The method according to claim 6, characterized in that The credit rating system further includes a job scheduling component configured with scheduling parameters, wherein the scheduling parameters of the job scheduling component are used to determine the rating process of the rating component, and Based on the credit characterization data of the to-be-rated object, the rating component and the parameter values ​​of each configured parameter component, credit rating is performed on the to-be-rated object, including: Based on the credit characterization data, the rating component, the parameter values ​​of each configured parameter component, and the scheduling parameters of the job scheduling component, the object to be rated is credit rated.

8. The method according to claim 6 or 7, characterized in that The credit rating system further includes at least one management component, different management components correspond to different rating management links, and After performing credit rating on the object to be rated based on the credit representation data of the object to be rated, the rating component and the parameter values ​​of each configured parameter component, the method further includes: Based on the credit rating result and the management component, the object to be rated is managed.

9. A device for generating a credit rating system, characterized in that: include: A data source selection module, configured to select a target data source from a pre-configured data source set in response to a data source selection operation, wherein different data sources in the data source set are used to provide different credit rating reference data; A rating component selection module, configured to select at least one rating component from a pre-configured rating component set in response to a rating component selection operation, wherein different rating components in the rating component set correspond to different credit rating types, and the rating component set includes an entity rating component and a debt rating component; A business parameter configuration module, configured to configure the credit rating parameters of each pre-configured parameter component in response to a rating parameter configuration request, wherein the credit rating parameters of each parameter component are determined based on the credit rating reference data provided by the target data source, and different parameter components correspond to different types of credit rating parameters, and each parameter component includes a common parameter component, an indicator management component, and a model management component; The generation module is used to generate a credit rating system in response to a system generation request based on the selected rating components and the credit rating parameters of the configured parameter components.

10. The device according to claim 9, characterized in that The data formats of the credit rating reference data provided by different data sources in the data source set have been converted in advance into a unified data format.

11. The device according to claim 9 or 10, characterized in that Also includes: A scheduling parameter configuration module, configured to configure the scheduling parameters of the pre-configured job scheduling component in response to the scheduling parameter configuration request, wherein the scheduling parameters of the job scheduling component are used to determine the rating process of the selected rating component; The generation module is also used to generate the credit rating system based on the selected rating component, the configured scheduling parameters of the job scheduling component and the configured credit rating parameters of each parameter component.

12. The device according to claim 11, characterized in that Also includes: A management component selection module, configured to select at least one management component from a pre-configured management component set in response to a management component selection operation, wherein different management components in the management component set correspond to different rating management links; The generation module is also used to generate the credit rating system in response to the system generation request based on the selected rating component, the selected management component, the configured scheduling parameters of the job scheduling component, and the configured credit rating parameters of each parameter component.

13. The device according to claim 12, characterized in that The management component set includes an investment pool management component, a tracking and early warning component, and a limit management component.

14. A credit rating device, characterized in that: Applied to a credit rating system, the credit rating system includes at least one rating component and parameter components configured with credit rating parameters, the at least one rating component is selected from a subject rating component and a debt rating component, the parameter components include a common parameter component, an indicator management component and a model management component, different rating components correspond to different credit rating types, and different parameter components correspond to different types of credit rating parameters, the device includes: A parameter value configuration module, configured to configure the parameter value of the credit rating parameter of each parameter component in response to a parameter value configuration request, wherein the configured parameter value is determined based on the credit rating data of the object to be rated; The rating module is used to perform credit rating on the object to be rated based on the credit characterization data of the object to be rated, the rating component and the parameter values ​​of each configured parameter component, wherein the credit characterization data is obtained from the credit rating reference data provided by the target data source.

15. The device according to claim 14, characterized in that The credit rating system further includes a job scheduling component configured with scheduling parameters, the scheduling parameters of the job scheduling component are used to determine the rating process of the rating component, and the rating module is further used to: Based on the credit characterization data, the rating component, the parameter values ​​of each configured parameter component, and the scheduling parameters of the job scheduling component, the object to be rated is credit rated.

16. The device according to claim 14 or 15, characterized in that The credit rating system further includes at least one management component, different management components correspond to different rating management links, and also includes a management module for: After credit rating the object to be rated based on the credit representation data of the object to be rated, the rating component and the parameter values ​​of each configured parameter component, the object to be rated is managed based on the credit rating result and the management component.

17. An electronic device, characterized in that: include: at least one processor, and a memory communicatively connected to the at least one processor, wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 8.

18. A storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the method as claimed in any one of claims 1 to 8.

19. A computer program product, characterized in that When the computer program product is called and executed by an electronic device, the electronic device executes the method according to any one of claims 1 to 8.

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