Operation risk early warning method and device

Abstract

The invention provides an operation risk early warning method which can be applied to the technical field of big data. The operation risk early warning method comprises the following steps: obtaining an operation association relationship from a data lake, the operation association relationship comprising association relationships between N different first operation objects and second operation objects; based on the operation association relationship, obtaining a first comprehensive index of N different first operation objects and a second comprehensive index of N different second operation objects from a second system in the data lake; on the basis of the first comprehensive indexes of the N different first operation objects and the second comprehensive indexes of the N different second operation objects, N first service concentration degrees corresponding to the N different first operation objects and a second service concentration degree corresponding to the second operation objects are obtained through calculation; and sending an operation risk early warning to the second system based on the N first service concentration degrees and the N second service concentration degrees. The invention further provides an operation risk early warning device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and in particular to a business risk early warning method, apparatus, equipment, medium, and program product. Background Art

[0002] With the rapid development of international trade, document management plays a vital role. Document management protects the rights and interests of both parties to a transaction and promotes the standardization and efficiency of international trade. The information contained in documents can be used as an indicator to assess client business risks, thereby providing early warning of these risks.

[0003] However, due to the inherent characteristics of the document business, there are characteristics such as poor concentration among different enterprises and scattered distribution. This will lead to a lack of linkage means for early warning of business risks of enterprises through documents, which will directly lead to low efficiency in business risk supervision and inaccurate predictions. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a business risk prediction method, device, equipment, medium and program product that improve risk supervision efficiency and risk prediction accuracy.

[0005] According to a first aspect of the present disclosure, a business risk early warning method is provided, comprising: obtaining business association relationships from a data lake, wherein the business association relationships include association relationships between N different first business objects and second business objects, where N is a positive integer; based on the business association relationships, obtaining first comprehensive indicators of the N different first business objects and second comprehensive indicators of the second business objects from a second system in the data lake; based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicators of the second business objects, calculating N first business concentrations corresponding to the N different first business objects and second business concentrations corresponding to the second business objects; and issuing a business risk early warning to the second system based on the N first business concentrations and the second business concentrations.

[0006] According to an embodiment of the present disclosure, obtaining the business association relationship from the data lake includes: for any first business object, obtaining the latest business association sub-relationship stored from the first system in the data lake, wherein the business association sub-relationship is a subordinate correspondence relationship between the first business object and the second business object; and forming or maintaining the business association relationship based on the business association sub-relationship.

[0007] According to an embodiment of the present disclosure, the first comprehensive indicator includes K first comprehensive sub-indicators, K is a positive integer, and the first comprehensive indicator based on the N different first business objects and the second comprehensive indicator of the second business object are used to calculate N first business concentrations corresponding to the N different first business objects and the second business concentration corresponding to the second business object, including: for the first business concentration, obtaining a preset weight coefficient set, the preset weight coefficient set including K weight coefficients corresponding one-to-one to the K first comprehensive sub-indicators; and calculating the first business concentration based on the K weight coefficients and the K first comprehensive sub-indicators.

[0008] According to an embodiment of the present disclosure, the second comprehensive indicator includes K second comprehensive sub-indicators, and the first comprehensive indicator based on the N different first business objects and the second comprehensive indicator of the second business object are used to calculate N first business concentrations corresponding to the N different first business objects and the second business concentration corresponding to the second business object. It also includes: for the second business concentration, the second business concentration is calculated based on the K weight coefficients and the K second comprehensive sub-indicators.

[0009] According to an embodiment of the present disclosure, the issuing of a business risk warning to the second system based on the N first business concentrations and the second business concentrations includes: when the first business concentration is greater than a first preset threshold, issuing a warning for the first business object and the second business object to the second system; and when the second business concentration is greater than a second preset threshold, issuing a warning for the second business object to the second system.

[0010] According to an embodiment of the present disclosure, after issuing a business risk warning to the second system based on the N first business concentrations and the second business concentrations, it also includes: for any first business object or any second business object, when the K first comprehensive sub-indicators are greater than the corresponding preset first preset sub-indicator thresholds, or when the K second comprehensive sub-indicators are greater than the corresponding preset second preset sub-indicator thresholds, issuing a warning for the first business object and the second business object to the second system.

[0011] According to an embodiment of the present disclosure, before obtaining the business association relationship from the data lake, it also includes: receiving initial data from the first system of different first platforms, the initial data including the initial first comprehensive sub-indicator and the initial second comprehensive sub-indicator; and performing data preprocessing on the initial data from the first system of different first platforms respectively to obtain the first comprehensive sub-indicator and the second comprehensive sub-indicator in a unified format.

[0012] The second aspect of the present disclosure provides a business risk warning device, which includes: a business association relationship acquisition module, used to obtain business association relationships from a data lake, wherein the business association relationships include association relationships between N different first business objects and second business objects, and N is a positive integer; a comprehensive indicator calculation module, used to obtain the first comprehensive indicators of the N different first business objects and the second comprehensive indicators of the second business objects from the second system in the data lake based on the business association relationships; a business concentration calculation module, used to calculate N first business concentrations corresponding to the N different first business objects and the second business concentrations corresponding to the second business objects based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicators of the second business objects; and a warning module, used to issue a business risk warning to the second system based on the N first business concentrations and the second business concentrations.

[0013] According to an embodiment of the present disclosure, the business association relationship acquisition module is specifically used to obtain, for any first business object, the latest business association sub-relationship stored from the first system in the data lake, wherein the business association sub-relationship is the subordinate correspondence relationship between the first business object and the second business object; and form or maintain the business association relationship based on the business association sub-relationship.

[0014] According to an embodiment of the present disclosure, the first comprehensive indicator includes K first comprehensive sub-indicators, K is a positive integer, and the business concentration calculation module is specifically used to obtain a preset weight coefficient set for the first business concentration, and the preset weight coefficient set includes K weight coefficients corresponding one-to-one to the K first comprehensive sub-indicators; and calculate the first business concentration based on the K weight coefficients and the K first comprehensive sub-indicators.

[0015] According to an embodiment of the present disclosure, the second comprehensive indicator includes K second comprehensive sub-indicators, and the business concentration calculation module is further specifically used to calculate the second business concentration based on the K weight coefficients and the K second comprehensive sub-indicators.

[0016] According to an embodiment of the present disclosure, the early warning module is specifically used to issue an early warning to the second system for the first business object and the second business object when the first business concentration is greater than a first preset threshold; and to issue an early warning to the second system for the second business object when the second business concentration is greater than a second preset threshold.

[0017] According to an embodiment of the present disclosure, the early warning module is further specifically used to issue an early warning for any first business object or any second business object to the second system when the K first comprehensive sub-indicators are greater than the corresponding preset first preset sub-indicator thresholds, or when the K second comprehensive sub-indicators are greater than the corresponding preset second preset sub-indicator thresholds.

[0018] According to an embodiment of the present disclosure, the device also includes: a preprocessing module for receiving initial data from the first system of different first platforms, the initial data including an initial first comprehensive sub-indicator and an initial second comprehensive sub-indicator; and performing data preprocessing on the initial data from the first system of different first platforms respectively to obtain the first comprehensive sub-indicator and the second comprehensive sub-indicator in a unified format.

[0019] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0020] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0021] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0022] In the embodiments of the present disclosure, in order to solve the technical problems of low efficiency in business risk supervision and inaccurate prediction, the embodiments of the present disclosure first obtain the business object association relationship of the first platform and the comprehensive indicators of the second platform uniformly stored in the data lake, ensuring that different business objects can be linked, and then calculate the first business concentration and the second business concentration through the comprehensive indicators and business association relationship, ensuring the unified and efficient data processing, and finally issue an early warning through the first business concentration and the second business concentration, which can improve the business management risk level. The beneficial effects of the embodiments of the present disclosure are: the use of big data technology realizes the maintenance and identification of information through different systems, and at the same time, realizes data operation analysis, which can effectively identify the concentration risks of the first business object and the second business object, and improve the document business risk management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0024] Figure 1 Schematically illustrates an application scenario diagram of the business risk early warning method according to an embodiment of the present disclosure;

[0025] Figure 2 Schematically shows a flow chart of a business risk early warning method according to an embodiment of the present disclosure;

[0026] Figure 3 A schematic diagram illustrating the unified integration of data from the document system and customer information system into the data lake according to an embodiment of the present disclosure is shown;

[0027] Figure 4 A schematic diagram showing a structural block diagram of a business risk early warning device according to an embodiment of the present disclosure is shown; and

[0028] Figure 5 A block diagram of an electronic device suitable for implementing a business risk early warning method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0029] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0030] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0032] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0033] In a typical scenario, centralized international document operations effectively aggregate document processing procedures, personnel, and business data dispersed across bank branches and outlets, establishing standardized processes for centralized review and processing of international documents. This centralized processing and data concentration enables centralized analysis of bank-wide document operations and the development of risk control models. With the rapid growth of international trade, more and more companies are adopting global business models. Among these companies, group clients refer to groups of companies under common control, whose businesses are often highly concentrated and interconnected.

[0034] Traditional document business systems primarily serve individual enterprises and are unable to meet the needs of group clients. Furthermore, existing document business systems have limitations in identifying and analyzing group client information and lack a method for analyzing the concentration risk of business operations for group clients. Therefore, this invention aims to propose a method for analyzing the concentration risk of business operations for group clients in international document business operations. This method addresses the technical issues of existing document business systems' inability to identify group clients, which in turn leads to low risk management efficiency and poor risk prediction efficiency.

[0035] An embodiment of the present disclosure provides a business risk warning method, including: obtaining business association relationships from a data lake, wherein the business association relationships include association relationships between N different first business objects and second business objects, where N is a positive integer; based on the business association relationships, obtaining first comprehensive indicators of the N different first business objects and second comprehensive indicators of the second business objects from a second system in the data lake; based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicators of the second business objects, calculating N first business concentrations corresponding to the N different first business objects and second business concentrations corresponding to the second business objects; and issuing a business risk warning to the second system based on the N first business concentrations and the second business concentrations.

[0036] In the embodiments of the present disclosure, in order to solve the technical problems of low efficiency in business risk supervision and inaccurate prediction, the embodiments of the present disclosure first obtain the business object association relationship of the first platform and the comprehensive indicators of the second platform uniformly stored in the data lake, ensuring that different business objects can be linked, and then calculate the first business concentration and the second business concentration through the comprehensive indicators and business association relationship, ensuring the unified and efficient data processing, and finally issue an early warning through the first business concentration and the second business concentration, which can improve the business management risk level. The beneficial effects of the embodiments of the present disclosure are: the use of big data technology realizes the maintenance and identification of information through different systems, and at the same time, realizes data operation analysis, which can effectively identify the concentration risks of the first business object and the second business object, and improve the document business risk management level.

[0037] Figure 1 The application scenario diagram of the business risk early warning method according to the embodiment of the present disclosure is schematically shown.

[0038] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0039] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0040] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0041] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0042] It should be noted that the business risk early warning method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the business risk early warning device provided in the embodiment of the present disclosure can generally be set in the server 105. The business risk early warning method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the business risk early warning device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0044] The following will be based on Figure 1 The scene described by Figure 2~Figure 3 The business risk early warning method of the disclosed embodiment is described in detail.

[0045] Figure 2 The flowchart of the business risk early warning method according to the embodiment of the present disclosure is schematically shown.

[0046] Figure 3 The diagram schematically shows the unified integration of the document system and customer information system data into the data lake according to an embodiment of the present disclosure.

[0047] like Figure 2 As shown, the business risk early warning method of this embodiment includes operations S210 to S230, and the business risk early warning method can be executed by the server 105.

[0048] According to an embodiment of the present disclosure, before obtaining the business association relationship from the data lake, it also includes: receiving initial data from the first system of different first platforms, the initial data including the initial first comprehensive sub-indicator and the initial second comprehensive sub-indicator; and performing data preprocessing on the initial data from the first system of different first platforms respectively to obtain the first comprehensive sub-indicator and the second comprehensive sub-indicator in a unified format.

[0049] Preprocessing includes data cleaning and data conversion. Specifically, data cleaning is used to remove noise, errors, duplications, and missing data to improve data quality. Data conversion is used to resolve differences in format, structure, and units to achieve data uniformity. The cleaned and converted data is then stored in a database, file system, or other storage system for data management.

[0050] In operation S210 , business association relationships are obtained from a data lake, where the business association relationships include association relationships between N different first business objects and second business objects, where N is a positive integer.

[0051] Among them, the second business object can be regarded as a collective organization of related first business objects. One or more first business objects constitute the second business object, and the first business object is subordinate to the second business object. The association relationship is the business association relationship between one or more first business objects and the second business object.

[0052] In a typical scenario, the primary business target might be a corporate customer, and the secondary business target might be a group customer. One or more corporate customers constitute a group customer. The customer information system (the first system mentioned above) supports the creation of a corporate customer information table, with the corporate customer ID CUSTID as the primary key. The customer information system also supports the creation of a corporate customer group relationship table, maintaining the correspondence between the corporate customer ID CUSTID and the group ID GROPID. The relationship between CUSTID and GROPID is one-to-many: one corporate customer belongs to only one group customer, and a group customer can contain two or more corporate customers. This is shown in Table 1 below:

[0053] Table 1

[0054]

[0055] Among them, corporate customers A, B, and C all belong to group customer X, and corporate customers D and E both belong to group customer Y.

[0056] According to an embodiment of the present disclosure, obtaining the business association relationship from the data lake includes: for any first business object, obtaining the latest business association sub-relationship stored from the first system in the data lake, wherein the business association sub-relationship is a subordinate correspondence relationship between the first business object and the second business object; and forming or maintaining the business association relationship based on the business association sub-relationship.

[0057] Among them, an operating association sub-relationship is a relationship in which a first operating object triggers a subordinate second operating object. For example, operating association sub-relationship 1 includes first operating object 1 being subordinate to second operating object 1, and operating association sub-relationship 2 includes first operating object 2 being subordinate to second operating object 1. It can be understood that the above operating association relationships are a collection of multiple operating association sub-relationships.

[0058] Specifically, the business association relationship is maintained periodically, and the overall business association relationship for a certain second business object is maintained by associating each first business object with the latest business association sub-relationship between the first business object and the second business object.

[0059] Combine Figure 3 As shown, the customer information system (system 1) will regularly store customer information tables. All changes to the legal person customer information table and the legal person customer group relationship table will be entered into the data lake at the end of the day. The corresponding legal person customer full information table and legal person customer group relationship full table are stored in the data lake, fully storing the latest legal person customer information and legal person customer group relationships. The legal person customer group relationship full table 2 is shown below:

[0060] Table 2

[0061]

[0062] Among them, based on the information in Table 1, the correspondence between legal person customer numbers such as T and R and group number X (i.e., business related sub-relationship) is also updated.

[0063] In operation S220 , based on the business association relationship, first comprehensive indicators of the N different first business objects and second comprehensive indicators of the second business objects from the second system in the data lake are obtained.

[0064] The second system pre-stores first comprehensive indicators for N different first business objects. In the data lake, these related business objects can be associated using the aforementioned business association relationships. That is, the second platform stores all first comprehensive indicators in the data lake, and subsequently retrieves associated first business objects using the business association relationships.

[0065] The first comprehensive indicator for any first business object includes multiple first comprehensive sub-indicators, such as first comprehensive sub-indicator 1, first comprehensive sub-indicator 2, and first comprehensive sub-indicator 3. The first comprehensive sub-indicators are individual indicators of the first business object. For example, the first comprehensive sub-indicators include data such as the number of transactions, amount, year-on-year change, and year-beginning change (ratio) for a single corporate client. These data can be divided into three time dimensions: monthly, quarterly, and annual.

[0066] The second comprehensive indicator for any second operating target includes multiple second comprehensive sub-indicators, such as second comprehensive sub-indicator 1, second comprehensive sub-indicator 2, and second comprehensive sub-indicator 3. The second comprehensive sub-indicators are individual indicators of the second operating target. For example, the second comprehensive sub-indicators include data such as the number of transactions, amount, year-on-year change, and year-beginning change (ratio) for group customers. These data can be divided into three time dimensions: monthly, quarterly, and annual.

[0067] Combine Figure 3 As shown, the document system stores the business data information table in the lake, wherein the business data information table includes the full amount of the first comprehensive sub-indicator and the second comprehensive sub-indicator.

[0068] In operation S230, based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicator of the second business object, N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object are calculated.

[0069] Specifically, the first business concentration is calculated using the first comprehensive indicator, and the second business concentration is calculated using the second comprehensive indicator.

[0070] According to an embodiment of the present disclosure, the first comprehensive indicator includes K first comprehensive sub-indicators, K is a positive integer, and the first comprehensive indicator based on the N different first business objects and the second comprehensive indicator of the second business object are used to calculate N first business concentrations corresponding to the N different first business objects and the second business concentration corresponding to the second business object, including: for the first business concentration, obtaining a preset weight coefficient set, the preset weight coefficient set including K weight coefficients corresponding one-to-one to the K first comprehensive sub-indicators; and calculating the first business concentration based on the K weight coefficients and the K first comprehensive sub-indicators.

[0071] According to an embodiment of the present disclosure, the second comprehensive indicator includes K second comprehensive sub-indicators, and the first comprehensive indicator based on the N different first business objects and the second comprehensive indicator of the second business object are used to calculate N first business concentrations corresponding to the N different first business objects and the second business concentration corresponding to the second business object. It also includes: for the second business concentration, the second business concentration is calculated based on the K weight coefficients and the K second comprehensive sub-indicators.

[0072] Specifically, the comprehensive indicators describing the data lake big data technical indicators are shown in Table 3 below:

[0073] Table 3

[0074]

[0075] As shown in Table 3, the weights for different business concentration warnings are determined: u, v, w, x, y, z... These values ​​are set by the business management department based on an analysis of the varying sensitivities of different document business products to transaction volume, amount, and year-on-year changes. The larger the weight, the greater the impact on risk concentration calculations. The business concentration for corporate and group customer A = the warning ratio for transaction volume concentration ratio * Weight 1 + the warning ratio for amount concentration ratio * Weight 2 + ... + the warning ratio for year-on-year amount (ratio) change * Weight 3 + ....

[0076] In operation S240 , a business risk warning is issued to the second system based on the N first business concentrations and the second business concentration.

[0077] Specifically, risk warnings can be diverse. When indicators fall within different ranges, different risk warnings can be issued, such as: high (60%), medium (80%), low (100%) business risk coefficient; active (120%), appropriate (100%), and average (80%) business promotion efforts. These warnings are then sent back to the second platform for early warning management.

[0078] According to an embodiment of the present disclosure, the issuing of a business risk warning to the second system based on the N first business concentrations and the second business concentrations includes: when the first business concentration is greater than a first preset threshold, issuing a warning for the first business object and the second business object to the second system; and when the second business concentration is greater than a second preset threshold, issuing a warning for the second business object to the second system.

[0079] Specifically, business concentration refers to the degree of concentration of an enterprise's resource allocation within a specific business field. It reflects the different trends of specialization and diversification in the enterprise's business model. Enterprises with higher business concentration usually focus most of their resources and efforts on limited businesses or markets, while enterprises with lower concentration may be involved in multiple business fields and have more dispersed resource allocation. The level of business concentration can affect the performance and market competitiveness of an enterprise because it is directly related to whether the enterprise can form competitive advantages and economies of scale in specific fields. In short, business concentration is an indicator to measure the degree of focus of an enterprise's business. It has important guiding significance for the enterprise's resource allocation decisions and business operation improvements. The first preset threshold and the second preset threshold can be defined manually and will not be repeated here.

[0080] According to an embodiment of the present disclosure, after issuing a business risk warning to the second system based on the N first business concentrations and the second business concentrations, it also includes: for any first business object or any second business object, when the K first comprehensive sub-indicators are greater than the corresponding preset first preset sub-indicator thresholds, or when the K second comprehensive sub-indicators are greater than the corresponding preset second preset sub-indicator thresholds, issuing a warning for the first business object and the second business object to the second system.

[0081] As shown in Table 3, a, b, c, and d are the indicator thresholds corresponding to the indicators.

[0082] In the embodiments of the present disclosure, in order to solve the technical problems of low efficiency in business risk supervision and inaccurate prediction, the embodiments of the present disclosure first obtain the business object association relationship of the first platform and the comprehensive indicators of the second platform uniformly stored in the data lake, ensuring that different business objects can be linked, and then calculate the first business concentration and the second business concentration through the comprehensive indicators and business association relationship, ensuring the unified and efficient data processing, and finally issue an early warning through the first business concentration and the second business concentration, which can improve the business management risk level. The beneficial effects of the embodiments of the present disclosure are: the use of big data technology realizes the maintenance and identification of information through different systems, and at the same time, realizes data operation analysis, which can effectively identify the concentration risks of the first business object and the second business object, and improve the document business risk management level.

[0083] Based on the above-mentioned business risk early warning method, the present disclosure also provides a business risk early warning device. Figure 4 The device is described in detail.

[0084] Figure 4 The structural block diagram of the business risk early warning device according to an embodiment of the present disclosure is schematically shown.

[0085] like Figure 4 As shown, the business risk early warning device 400 of this embodiment includes a business association relationship acquisition module 410, a comprehensive index calculation module 420, a business concentration calculation module 430 and an early warning module 440.

[0086] Business relationship acquisition module 41 is used to acquire business relationship relationships from the data lake, where the business relationship relationships include relationships between N different first business objects and second business objects, where N is a positive integer. In one embodiment, business relationship acquisition module 410 can be used to perform operation S210 described above and will not be further described here.

[0087] Comprehensive indicator calculation module 420 is configured to obtain, based on the business association relationships, first comprehensive indicators for the N different first business objects and second comprehensive indicators for the second business objects from the second system in the data lake. In one embodiment, comprehensive indicator calculation module 420 can be configured to perform operation S220 described above and will not be further described here.

[0088] Business concentration calculation module 430 is configured to calculate, based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicators of the second business objects, N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object. In one embodiment, business concentration calculation module 430 may be configured to perform operation S230 described above and will not be further described here.

[0089] The warning module 440 is configured to issue an operational risk warning to the second system based on the N first business concentrations and the second business concentration. In one embodiment, the warning module 440 may be configured to execute the operation S240 described above, which will not be described in detail here.

[0090] In the embodiments of the present disclosure, in order to solve the technical problems of low efficiency in business risk supervision and inaccurate prediction, the embodiments of the present disclosure first obtain the business object association relationship of the first platform and the comprehensive indicators of the second platform uniformly stored in the data lake, ensuring that different business objects can be linked, and then calculate the first business concentration and the second business concentration through the comprehensive indicators and business association relationship, ensuring the unified and efficient data processing, and finally issue an early warning through the first business concentration and the second business concentration, which can improve the business management risk level. The beneficial effects of the embodiments of the present disclosure are: the use of big data technology realizes the maintenance and identification of information through different systems, and at the same time, realizes data operation analysis, which can effectively identify the concentration risks of the first business object and the second business object, and improve the document business risk management level.

[0091] According to an embodiment of the present disclosure, the business association relationship acquisition module is specifically used to obtain, for any first business object, the latest business association sub-relationship stored from the first system in the data lake, wherein the business association sub-relationship is the subordinate correspondence relationship between the first business object and the second business object; and form or maintain the business association relationship based on the business association sub-relationship.

[0092] According to an embodiment of the present disclosure, the first comprehensive indicator includes K first comprehensive sub-indicators, K is a positive integer, and the business concentration calculation module is specifically used to obtain a preset weight coefficient set for the first business concentration, and the preset weight coefficient set includes K weight coefficients corresponding one-to-one to the K first comprehensive sub-indicators; and calculate the first business concentration based on the K weight coefficients and the K first comprehensive sub-indicators.

[0093] According to an embodiment of the present disclosure, the second comprehensive indicator includes K second comprehensive sub-indicators, and the business concentration calculation module is further specifically used to calculate the second business concentration based on the K weight coefficients and the K second comprehensive sub-indicators.

[0094] According to an embodiment of the present disclosure, the early warning module is specifically used to issue an early warning to the second system for the first business object and the second business object when the first business concentration is greater than a first preset threshold; and to issue an early warning to the second system for the second business object when the second business concentration is greater than a second preset threshold.

[0095] According to an embodiment of the present disclosure, the early warning module is further specifically used to issue an early warning for any first business object or any second business object to the second system when the K first comprehensive sub-indicators are greater than the corresponding preset first preset sub-indicator thresholds, or when the K second comprehensive sub-indicators are greater than the corresponding preset second preset sub-indicator thresholds.

[0096] According to an embodiment of the present disclosure, the device also includes: a preprocessing module for receiving initial data from the first system of different first platforms, the initial data including an initial first comprehensive sub-indicator and an initial second comprehensive sub-indicator; and performing data preprocessing on the initial data from the first system of different first platforms respectively to obtain the first comprehensive sub-indicator and the second comprehensive sub-indicator in a unified format.

[0097] According to embodiments of the present disclosure, any multiple modules among the business relationship acquisition module 410, comprehensive index calculation module 420, business concentration calculation module 430, and early warning module 440 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the business relationship acquisition module 410, comprehensive index calculation module 420, business concentration calculation module 430, and early warning module 440 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the business association relationship acquisition module 410, the comprehensive index calculation module 420, the business concentration calculation module 430 and the early warning module 440 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0098] Figure 5 A block diagram of an electronic device suitable for implementing a business risk early warning method according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 5 As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0100] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 503. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and RAM 503. The processor 501 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0101] According to an embodiment of the present disclosure, electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to bus 504. Electronic device 500 may also include one or more of the following components connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or modem. Communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 510 as needed, so that computer programs read from the removable media can be installed into storage section 508 as needed.

[0102] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0103] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 502 and / or RAM 503 described above, and / or one or more memories other than ROM 502 and RAM 503.

[0104] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiments of the present disclosure.

[0105] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0106] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0107] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the processor 501, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0108] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0111] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A business risk early warning method, characterized in that: The method comprises: Obtaining business association relationships from the data lake, wherein the business association relationships include association relationships between N different first business objects and second business objects, where N is a positive integer; Based on the business association relationship, obtain the first comprehensive index of the N different first business objects and the second comprehensive index of the second business object from the second system in the data lake; Based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicator of the second business object, calculate N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object; and An operating risk warning is issued to the second system based on the N first business concentrations and the second business concentration.

2. The method according to claim 1, characterized in that The acquisition of business relationships from the data lake includes: For any first business object, obtain the latest business-related sub-relationship stored in the data lake from the first system, wherein the business-related sub-relationship is the subordinate correspondence relationship between the first business object and the second business object; and Based on the business association sub-relationship, the business association relationship is formed or maintained.

3. The method according to claim 2, characterized in that in, The first comprehensive index includes K first comprehensive sub-indicators, where K is a positive integer. The calculating, based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicator of the second business object, N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object includes: For the first business concentration, obtaining a preset weight coefficient set, wherein the preset weight coefficient set includes K weight coefficients corresponding one-to-one to the K first comprehensive sub-indicators; and The first business concentration is calculated based on the K weight coefficients and the K first comprehensive sub-indicators.

4. The method according to claim 3, characterized in that in, The second comprehensive indicator includes K second comprehensive sub-indicators. The calculating, based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicator of the second business object, N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object further includes: For the second business concentration, the second business concentration is calculated based on the K weight coefficients and the K second comprehensive sub-indicators.

5. The method according to claim 3 or 4, characterized in that The issuing of an operational risk warning to the second system based on the N first business concentrations and the second business concentrations includes: When the first business concentration is greater than a first preset threshold, issuing an early warning for the first business object and the second business object to the second system; and When the second business concentration is greater than a second preset threshold, an early warning for the second business object is issued to the second system.

6. The method according to claim 5, characterized in that After issuing a business risk warning to the second system based on the N first business concentrations and the second business concentrations, the method further includes: For any first business object or any second business object, when the K first comprehensive sub-indicators are greater than the corresponding preset first preset sub-indicator thresholds, or when the K second comprehensive sub-indicators are greater than the corresponding preset second preset sub-indicator thresholds, an early warning for the first business object and the second business object is issued to the second system.

7. The method according to any one of claims 3 to 6, characterized in that: Before obtaining the business relationship from the data lake, the following steps are also included: Receiving initial data from a first system on a different first platform, the initial data including an initial first comprehensive sub-indicator and an initial second comprehensive sub-indicator; and Data preprocessing is performed on the initial data of the first systems from different first platforms respectively to obtain the first comprehensive sub-indicators and the second comprehensive sub-indicators in a unified format.

8. A business risk early warning device, characterized in that: The device comprises: A business association relationship acquisition module is used to acquire business association relationships from the data lake, wherein the business association relationships include association relationships between N different first business objects and second business objects, where N is a positive integer; a comprehensive indicator calculation module, configured to obtain, based on the business association relationship, first comprehensive indicators of the N different first business objects and second comprehensive indicators of the second business objects from the second system in the data lake; a business concentration calculation module, configured to calculate, based on the first comprehensive indicators of the N different first business objects and the second comprehensive indicator of the second business object, N first business concentrations corresponding to the N different first business objects and a second business concentration corresponding to the second business object; and An early warning module is used to issue an operating risk early warning to the second system based on the N first business concentrations and the second business concentrations.

9. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images