Business processing method, apparatus and electronic device

Abstract

The present invention discloses a service processing method, apparatus, electronic device, and computer-readable storage medium. The method includes: when receiving an application for processing a service from a user, obtaining the user's information; constructing features based on the user's information; querying multiple candidate service processing systems for processing the service; inputting the user's features into multiple passing rate prediction models corresponding to the multiple candidate service processing systems one by one, and outputting the probabilities of the multiple candidate service processing systems accepting the user's request; inputting the user's features into multiple response rate prediction models corresponding to the multiple candidate service processing systems one by one, and outputting the efficiencies of the multiple candidate service processing systems in processing the service for the user; and selecting a recommended service processing system for recommending to the user to process the service according to the probability of accepting the user's request and the efficiency of processing the service for the user. The present invention can select a service system suitable for recommending to the user, thereby ensuring that the user finally obtains a better service processing result.

Description

Technical Field

[0001] The present invention relates to the field of computer information processing, and more particularly, to a service processing method, apparatus, electronic device, and computer-readable medium. Background Art

[0002] For the services that users need to handle, there are often multiple institutions in the market that can handle the same service. Each institution has its own unique situation, which results in different processing situations for the service applications of the same user by different institutions. For example, if a user needs to handle Internet finance services, due to the differences in interest rates, scales, external risk evaluations, etc. of each institution, for customers who have completed credit application, the credit approval passing rates and transaction passing rates of different institutions vary greatly, and ultimately the service handling results of users vary greatly.

[0003] Currently, when a user submits a service handling application, the application is usually randomly distributed to service handling institutions, and the service handling results obtained by the user are often not satisfactory. Therefore, a new technical solution is needed to perform intelligent distribution processing on the service handling applications of users to ensure that users obtain better service handling results. Summary of the Invention

[0004] The present invention aims to perform intelligent distribution processing on service handling applications of users to ensure that users obtain better service handling results.

[0005] To solve the above technical problems, a first aspect of the present invention provides a service processing method, the method comprising: when receiving an application for processing a service from a user, obtaining information of the user; constructing features according to the information of the user; querying a plurality of candidate service processing systems for processing the service; inputting the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one, and outputting the probabilities of the plurality of candidate service processing systems accepting the user request; inputting the features of the user into a plurality of response rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one, and outputting the efficiencies of the plurality of candidate service processing systems in processing the service for the user; and selecting a recommended service processing system for recommending to the user to process the service from the plurality of candidate service processing systems according to the probabilities of the plurality of candidate service processing systems accepting the user request and the efficiencies of the plurality of candidate service processing systems in processing the service for the user.

[0006] According to a preferred embodiment of the present invention, the information of the user includes: behavior data of the user that generates risks for the service; record data of the user using an application related to the service; and credit data of the user recorded by a third party.

[0007] According to a preferred embodiment of the present invention, before inputting the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one, it further includes: inputting the features of the user into a preset risk prediction model to output the risk generated by the user in processing the service; screening the plurality of candidate service processing systems according to the risk generated by the user in processing the service and the risk-bearing capabilities of the plurality of candidate service processing systems.

[0008] According to a preferred embodiment of the present invention, it further includes: when there are multiple recommended service processing systems, analyzing the user's concerns about the service; querying information matching the concerns of the multiple recommended service processing systems according to the user's concerns and pushing it to the user for the user to select from the multiple recommended service processing systems according to the information.

[0009] According to a preferred embodiment of the present invention, it further includes: when there are multiple recommended service processing systems, querying the service processing systems used by other users associated with the user; if there is a service processing system used by the other user among the multiple recommended service processing systems, selecting the service processing system used by the other user to process the service for the user.

[0010] To solve the above technical problems, a second aspect of the present invention proposes a service processing device, the device includes: an information acquisition module, which acquires the information of the user when receiving an application for processing a service from the user; a feature construction module, which constructs features according to the information of the user; a system query module, which queries a plurality of candidate service processing systems for processing the service; a passing rate analysis module, which inputs the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one to output the possibility that the plurality of candidate service processing systems accept the user's request; a response rate analysis module, which inputs the features of the user into a plurality of response rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one to output the efficiency of the plurality of candidate service processing systems in processing the service for the user; a system recommendation module, which selects a recommended service processing system for recommending to the user to process the service from the plurality of candidate service processing systems according to the possibility that the plurality of candidate service processing systems accept the user's request and the efficiency of processing the service for the user.

[0011] According to a preferred embodiment of the present invention, the information of the user includes: behavior data of the user's implementation that generates risks for the service; record data of the user's use of application programs related to the service; credit data of the user recorded by a third party.

[0012] According to a preferred embodiment of the present invention, it further includes: a risk screening module, which, before inputting the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate business processing systems one by one, inputs the features of the user into a preset risk prediction model, outputs the risk generated by the user in processing the business, and screens the plurality of candidate business processing systems according to the risk generated by the user in processing the business and the risk-bearing capabilities of the plurality of candidate business processing systems.

[0013] According to a preferred embodiment of the present invention, it further includes: an information pushing module. When there are multiple recommended business processing systems, it analyzes the user's concerns about the business, and according to the user's concerns, queries the information of the multiple recommended business processing systems that matches the concerns and pushes it to the user, for the user to select from the multiple recommended business processing systems according to the information.

[0014] According to a preferred embodiment of the present invention, it further includes: a system selection module. When there are multiple recommended business processing systems, it queries the business processing systems used by other users associated with the user. If there is a business processing system used by the other users among the multiple recommended business processing systems, it selects the business processing system used by the other users to process the business for the user.

[0015] To solve the above technical problems, a third aspect of the present invention proposes an electronic device, which includes a processor and a memory storing computer-executable instructions. When the computer-executable instructions are executed, the processor executes the above method.

[0016] To solve the above technical problems, a fourth aspect of the present invention proposes a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by a processor, the above method is implemented.

[0017] Different from the prior art solutions, the present invention does not randomly distribute the user's business processing application to the business processing system for processing. Instead, it respectively trains a passing rate prediction model and a response rate prediction model for each business processing system according to the historical business handling data of each business processing system in advance. The passing rate prediction model can analyze the possibility that the user's application is accepted by the system, and the response rate prediction model can analyze the efficiency of the system in handling the business for the user. Then, through the passing rate prediction models and multiple response rate prediction models of each business processing system, it can analyze the possibility that each business processing system accepts the user's business application and the efficiency of handling the business for the user. According to the analysis results, a business processing system suitable for recommending to the user is selected from multiple business processing systems, so as to ensure that the user finally obtains a better business processing result. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the technical problems solved by the present invention, the technical means adopted, and the technical effects achieved more clear, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it should be stated that the accompanying drawings described below are only the drawings of the exemplary embodiments of the present invention. For those skilled in the art, without creative efforts, other embodiments of the drawings can be obtained based on these drawings.

[0019] Figure 1 is a flowchart of a service processing method according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a service processing method according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a service processing method according to an embodiment of the present invention;

[0022] Figure 4 is a block diagram of a service processing device according to an embodiment of the present invention;

[0023] Figure 5 is a block diagram of a service processing device according to an embodiment of the present invention;

[0024] Figure 6 is a block diagram of an electronic device according to an embodiment of the present invention;

[0025] Figure 7 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Specific Embodiments

[0026] Now, the exemplary embodiments of the present invention will be described more comprehensively with reference to the accompanying drawings. Although each exemplary embodiment can be implemented in many specific ways, it should not be understood that the present invention is limited to the embodiments described herein. On the contrary, these exemplary embodiments are provided to make the content of the present invention more complete and more convenient to fully convey the inventive concept to those skilled in the art.

[0027] On the premise of conforming to the technical concept of the present invention, the structures, performances, effects, or other features described in a specific embodiment can be combined with one or more other embodiments in any suitable manner.

[0028] In the process of introducing the specific embodiments, the detailed descriptions of the structures, performances, effects, or other features are for those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can implement the present invention with technical solutions that do not include the above-mentioned structures, performances, effects, or other features in specific situations.

[0029] The flowcharts in the accompanying drawings are only exemplary flow demonstrations, and do not represent that all the contents, operations, and steps in the flowcharts must be included in the solution of the present invention, nor does it represent that they must be executed in the order shown in the figures. For example, some operations / steps in the flowchart can be decomposed, some operations / steps can be combined or partially combined, etc. Without departing from the gist of the present invention, the execution order shown in the flowchart can be changed according to the actual situation.

[0030] The boxes in the accompanying drawings Figure 1 generally represent functional entities, and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0031] The same reference numerals in the respective drawings represent the same or similar elements, components, or parts. Therefore, the repeated description of the same or similar elements, components, or parts may be omitted hereinafter. It should also be understood that although the first, second, third, etc. attributives indicating numbers may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these attributives. That is to say, these attributives are only used to distinguish one from another. For example, the first device can also be called the second device without departing from the essential technical solution of the present invention. In addition, the terms "and / or", "or / and" mean all combinations including any one or more of the listed items.

[0032] In the prior art solution, when there are multiple systems that can receive an application for a user to process a service, the service processing application of the user is often randomly distributed to any one of the systems. Since the service processing methods and capabilities of the multiple systems are different, randomly allocating user applications will result in the inability to select an appropriate system for the user to process the service, and thus a better processing result cannot be obtained. Different from the prior art solution, when receiving an application for a user to request service processing, the present invention first obtains the user's information, constructs features based on the user's information, queries multiple candidate service processing systems for processing the service, and instead of randomly selecting a system to process the service application, inputs the user's features into multiple passing rate prediction models preset and corresponding to the multiple candidate service processing systems one by one to output the probabilities of the multiple candidate service processing systems accepting the user's request, and inputs the user's features into multiple response rate prediction models preset and corresponding to the multiple candidate service processing systems one by one to output the efficiencies of the multiple candidate service processing systems in processing the service for the user. According to the probabilities of the multiple candidate service processing systems accepting the user's request and the efficiencies of processing the service for the user, a recommended service processing system for recommending to the user to process the service is selected from the multiple candidate service processing systems, so as to select an appropriate service processing system for the user and finally bring the optimal service processing result for the user.

[0033] As Figure 1 shown, in an embodiment of the present invention, a service processing method is provided, and the method includes:

[0034] Step S110, when receiving an application for a user to request service processing, obtain the user's information.

[0035] In this embodiment, there is no restriction on the service type requested by the user to be processed. For example, the user may apply for handling Internet financial services.

[0036] Step S120, construct features based on the user's information.

[0037] In this embodiment, there is no restriction on the type of the user's information. Specifically, the user's information may include: behavioral data of the user that poses risks to the service, and the risk behaviors of the user affect the probability of the service system accepting the user's application; recorded data of the user's use of service-related application programs. When the user submits a service application through the service-related APP, the user's usage records of the APP can reflect some features of the user; credit data of the user recorded by a third party. Similarly, the user's credit status directly affects the probability of the service system accepting the user's application.

[0038] Step S130, query multiple candidate service processing systems for processing the service.

[0039] Step S140: Input the user's features into multiple passing rate prediction models preset for multiple candidate business processing systems one by one, and output the probabilities that the multiple candidate business processing systems accept the user's request.

[0040] Step S150: Input the user's features into multiple response rate prediction models preset for multiple candidate business processing systems one by one, and output the efficiencies of the multiple candidate business processing systems in processing the user's business.

[0041] In this embodiment, it is necessary to train a passing rate prediction model and a response rate prediction model for each business processing system respectively according to the historical business handling data of each business processing system. The passing rate prediction model can analyze the probability that the user's application is accepted by the system, and the response rate prediction model can analyze the efficiency of the system in handling the user's business. Then, through the passing rate prediction model and the response rate prediction model of each business processing system, the probability that each business processing system accepts the user's business application and the efficiency of handling the user's business can be analyzed.

[0042] Step S160: Select a recommended business processing system for recommending to the user to process the business from the multiple candidate business processing systems according to the probabilities that the multiple candidate business processing systems accept the user's request and the efficiencies of processing the user's business.

[0043] According to the technical solution of this embodiment, after analyzing the probability that each business processing system accepts the user's business application and the efficiency of handling the user's business, a business processing system suitable for recommending to the user is selected from the multiple business processing systems according to the analysis results, so as to ensure that the user finally obtains a better business processing result.

[0044] As Figure 2 shown, in an embodiment of the present invention, a business processing method is provided, and the method includes:

[0045] Step S210: When receiving an application for processing a business from a user, obtain the user's information.

[0046] Step S220: Construct features according to the user's information.

[0047] Step S230: Query multiple candidate business processing systems for processing the business.

[0048] Step S240: Input the user's features into a preset risk prediction model, and output the risks generated by the user in processing the business.

[0049] Step S250: Screen the multiple candidate business processing systems according to the risks generated by the user in processing the business and the risk-bearing capabilities of the multiple candidate business processing systems.

[0050] In this embodiment, not only is it considered from the user's perspective to obtain the optimal processing result for the user, but also from the system side to avoid the user bringing too high a risk to the system side.

[0051] Step S260: Input the user's characteristics into multiple passing rate prediction models preset for multiple candidate business processing systems one by one, and output the possibility that the multiple candidate business processing systems accept the user's request.

[0052] Step S270: Input the user's characteristics into multiple response rate prediction models preset for multiple candidate business processing systems one by one, and output the efficiency of the multiple candidate business processing systems in processing the user's business.

[0053] Step S280: Select a recommended business processing system for recommending to the user to process the business from the multiple candidate business processing systems according to the possibility that the multiple candidate business processing systems accept the user's request and the efficiency of processing the user's business.

[0054] Step S290: When there are multiple recommended business processing systems, analyze the user's concerns about the business; according to the user's concerns, query the information of the multiple recommended business processing systems that matches the concerns and push it to the user for the user to select from the multiple recommended business processing systems according to the information.

[0055] In this embodiment, by analyzing the user's concerns about the business, the user's focus points can be found, and the information of each business processing system can be provided to the user according to the focus points, so that the user can obtain the information that he / she wants to know more, and thus select a business processing system that better suits himself / herself for processing.

[0056] Step S2100: When there are multiple recommended business processing systems, query the business processing systems used by other users associated with the user. If there are business processing systems used by other users among the multiple recommended business processing systems, select the business processing systems used by other users to process the user's business.

[0057] In this embodiment, other users associated with the user can be familiar people such as the user's family members and friends. And the user often has more trust in people he / she is familiar with. Therefore, the business processing systems used by the user's relatives and friends can be selected to process the user's business, which is beneficial to improving the user's trust level.

[0058] According to a specific implementation scheme of this embodiment, such as Figure 3As shown in the figure, this solution is applied to the field of Internet finance. It mainly establishes a risk prediction model, a passing rate prediction model, and a response rate prediction model for each Internet financial service institution. Before distributing the user's financial business application to each institution's system, based on data such as the user's various behavioral characteristics, model scores, and external creditworthiness, first use the risk prediction model to screen the institutions that can undertake the user's risk, and then use the passing rate prediction model and the response rate prediction model to seek the optimal "response - passing" effect for the user, and finally determine the institution for targeted distribution. The specific steps are as follows Figure 3 shown:

[0059] Step1: Obtain the latest user characteristics, including risk behavior data, APP (Internet finance application) behavior data, external creditworthiness, central bank data, etc.

[0060] Step2: Establish an XGBoost (XGBoost is an optimized distributed gradient boosting library) risk prediction model to evaluate the risk of the user group; divide the user group into 20 layers according to the evaluation results; and allocate the users in each layer to each institution according to the risk tolerance of each institution.

[0061] Step3: 1. For each institution, establish a corresponding XGboost passing rate prediction model by combining historical review data and data from cracking the same password review, and obtain the passing probability of each institution for each user's application. 2. For each institution, establish a corresponding LSTM (a type of recurrent neural network) neural network response rate prediction model by combining historical response data, and obtain the response efficiency of each user in each institution. 3. Obtain the response efficiency and passing probability of the user in each institution.

[0062] Step4: Select the two best institutions from all institutions according to the response efficiency and passing probability of the user in each institution, and at this time, the user's application can be submitted to these two institutions.

[0063] It can be seen that the technical solution of this embodiment, in accordance with the principle of "divide and conquer", makes full use of the institution's historical business data, establishes a risk prediction model, a passing rate prediction model, and a response rate prediction model for each institution respectively, and selects a financial institution for the user to handle business according to the output results of the risk prediction model, the passing rate prediction model, and the response rate prediction model, which not only ensures the controllability of the risks of financial service institutions, but also provides the user with the optimal business handling result.

[0064] Those skilled in the art can understand that all or part of the steps of implementing the above embodiments are realized as a program executed by a data processing device (including a computer), that is, a computer program. When this computer program is executed, the above methods provided by the present invention can be realized. Moreover, the computer program can be stored in a computer-readable storage medium, which can be a readable storage medium such as a disk, an optical disc, a ROM, a RAM, etc., or a storage array composed of multiple storage media, such as a disk or tape storage array. The storage medium is not limited to centralized storage, and it can also be distributed storage, such as cloud storage based on cloud computing.

[0065] The device embodiments of the present invention will be described below. The device can be used to execute the method embodiments of the present invention. For the details described in the device embodiments of the present invention, they should be regarded as a supplement to the above method embodiments; for the details not disclosed in the device embodiments of the present invention, they can be implemented with reference to the above method embodiments.

[0066] As Figure 4 shown, in an embodiment of the present invention, a service processing device is provided. The device includes:

[0067] An information acquisition module 410, when receiving an application for processing a service from a user, acquires the user's information.

[0068] In this embodiment, there is no restriction on the type of service requested by the user. For example, the user can apply for an Internet finance service.

[0069] A feature construction module 420 constructs features according to the user's information.

[0070] In this embodiment, there is no restriction on the type of the user's information. Specifically, the user's information may include: behavioral data of the user that poses risks to the service, and the user's risk behaviors affect the possibility of the service system accepting the user's application; record data of the user's use of service-related application programs. When the user submits a service application through the service-related APP, the user's usage records of the APP can reflect some features of the user; credit data of the user recorded by a third party. Similarly, the user's credit situation directly affects the possibility of the service system accepting the user's application.

[0071] A system query module 430 queries multiple candidate service processing systems for processing the service.

[0072] A passing rate analysis module 440 inputs the user's features into multiple passing rate prediction models preset and corresponding to the multiple candidate service processing systems one by one, and outputs the possibility of the multiple candidate service processing systems accepting the user's request.

[0073] The response rate analysis module 450 inputs the user's characteristics into multiple response rate prediction models preset for a plurality of candidate service processing systems one by one, and outputs the efficiency of the plurality of candidate service processing systems in processing services for the user.

[0074] In this embodiment, it is necessary to train a passing rate prediction model and a response rate prediction model for each service processing system respectively according to the historical service handling data of each service processing system. The passing rate prediction model can analyze the possibility that the user's application is accepted by the system, and the response rate prediction model can analyze the efficiency of the system in handling services for the user. Then, through the passing rate prediction model and the response rate prediction model of each service processing system, the possibility of each service processing system accepting the user's service application and the efficiency of handling services for the user can be analyzed.

[0075] The system recommendation module 460 selects a recommended service processing system for recommending to the user to process services from the plurality of candidate service processing systems according to the possibility of the plurality of candidate service processing systems accepting the user's request and the efficiency of processing services for the user.

[0076] According to the technical solution of this embodiment, after analyzing the possibility of each service processing system accepting the user's service application and the efficiency of handling services for the user, a service processing system suitable for recommending to the user is selected from the plurality of service processing systems according to the analysis result, so as to ensure that the user finally obtains a better service processing result.

[0077] As Figure 5 shown, in an embodiment of the present invention, a service processing device is provided, and the method includes:

[0078] The information acquisition module 510 acquires the user's information when receiving an application for processing a service from the user.

[0079] The feature construction module 520 constructs features according to the user's information.

[0080] The system query module 530 queries a plurality of candidate service processing systems for processing services.

[0081] The risk screening module 540 inputs the user's characteristics into a preset risk prediction model, outputs the risks generated by the user in processing services, and screens the plurality of candidate service processing systems according to the risks generated by the user in processing services and the risk-bearing capabilities of the plurality of candidate service processing systems.

[0082] In this embodiment, not only is it considered from the user's perspective to obtain the optimal processing result for the user, but also it is considered from the system side to avoid the user bringing too high risks to the system side.

[0083] The passing rate analysis module 550 inputs the user's characteristics into multiple preset passing rate prediction models corresponding to multiple candidate business processing systems one by one, and outputs the possibility that the multiple candidate business processing systems accept the user's request.

[0084] The response rate analysis module 560 inputs the user's characteristics into multiple preset response rate prediction models corresponding to multiple candidate business processing systems one by one, and outputs the efficiency of the multiple candidate business processing systems in processing the user's business.

[0085] The system recommendation module 570 selects a recommended business processing system for recommending to the user to process the business from multiple candidate business processing systems according to the possibility that the multiple candidate business processing systems accept the user's request and the efficiency of processing the user's business.

[0086] The information push module 580 analyzes the user's attention items for the business when there are multiple recommended business processing systems; according to the user's attention items, queries the information of the multiple recommended business processing systems that matches the attention items and pushes it to the user for the user to select from the multiple recommended business processing systems according to the information.

[0087] In this embodiment, by analyzing the user's attention items for the business, the user's attention points can be found, and the information of each business processing system is provided to the user according to the attention points, so that the user can obtain the information that he / she wants to know more, and thus select a business processing system that better suits himself / herself for processing.

[0088] The system selection module 590 queries the business processing systems used by other users associated with the user when there are multiple recommended business processing systems, and if there are business processing systems used by other users among the multiple recommended business processing systems, selects the business processing systems used by other users to process the user's business.

[0089] In this embodiment, other users associated with the user can be familiar people such as the user's family members and friends, and the user often has more trust in people he / she is familiar with. Therefore, the business processing systems used by the user's relatives and friends can be selected to process the user's business, which is beneficial to improving the user's trust level.

[0090] According to a specific implementation scheme of this embodiment, such as Figure 3As shown in the figure, this solution is applied to the field of Internet finance. It mainly establishes a risk prediction model, a passing rate prediction model, and a response rate prediction model for each Internet financial service institution. Before distributing the user's financial business application to each institution's system, based on data such as the user's various behavioral characteristics, model scores, and external creditworthiness, first use the risk prediction model to screen the institutions that can undertake the user's risk, and then use the passing rate prediction model and the response rate prediction model to seek the optimal "response - passing" effect for the user, and finally determine the institution for targeted distribution. The specific steps are as Figure 3 shown below:

[0091] Step1: Obtain the user's latest characteristics, including risk behavior data, APP (Internet finance application) behavior data, external creditworthiness, central bank data, etc.

[0092] Step2: Establish an XGBoost (XGBoost is an optimized distributed gradient boosting library) risk prediction model to evaluate the risk of the user group; divide the user group into 20 layers according to the evaluation results; and allocate the users in each layer to each institution according to the risk tolerance of each institution.

[0093] Step3: 1. For each institution, establish a corresponding XGboost passing rate prediction model by combining historical review data and data for checking against duplicate accounts, and obtain the passing probability of each institution for each user's application. 2. For each institution, establish a corresponding LSTM (a type of recurrent neural network) neural network response rate prediction model by combining historical response data, and obtain the response efficiency of each user in each institution. 3. Obtain the response efficiency and passing probability of the user in each institution.

[0094] Step4: According to the response efficiency and passing probability of the user in each institution, select the two best institutions from all institutions, and at this time, the user's application can be submitted to these two institutions.

[0095] It can be seen that the technical solution of this embodiment, according to the principle of "divide and conquer", makes full use of the institution's historical business data, establishes a risk prediction model, a passing rate prediction model, and a response rate prediction model for each institution respectively, and selects a financial institution for the user to handle the business according to the output results of the risk prediction model, the passing rate prediction model, and the response rate prediction model, which not only ensures the risk controllability of the financial service institution, but also provides the user with the best business handling result.

[0096] Those skilled in the art can understand that the various modules in the above device embodiments can be distributed in the device as described, or can be changed accordingly and distributed in one or more devices different from the above embodiments. The modules in the above embodiments can be combined into one module, or further split into multiple sub - modules.

[0097] Embodiments of the electronic device of the present invention are described below. The electronic device can be regarded as an implementation in physical form of the above-described method and apparatus embodiments of the present invention. Details described in the embodiments of the electronic device of the present invention should be regarded as a supplement to the above-described method or apparatus embodiments; for details not disclosed in the embodiments of the electronic device of the present invention, reference can be made to the above-described method or apparatus embodiments for implementation.

[0098] Figure 6 It is a block diagram of the structure of an exemplary embodiment of an electronic device according to the present invention. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

[0099] As Figure 6 shown, the electronic device 200 of this exemplary embodiment is presented in the form of a general-purpose data processing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including the storage unit 220 and the processing unit 210), a display unit 240, etc.

[0100] Among them, the storage unit 220 stores a computer-readable program, which can be the source program or the code of a read-only program. The program can be executed by the processing unit 210, so that the processing unit 210 executes the steps of various embodiments of the present invention. For example, the processing unit 210 can execute as Figure 1 or Figure 2 shown steps.

[0101] The storage unit 220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 2201 and / or a cache storage unit 2202, and may further include a read-only storage unit (ROM) 2203. The storage unit 220 may also include a program / utility 2204 having a set (at least one) of program modules 2205. Such program modules 2205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0102] The bus 230 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0103] The electronic device 200 can also communicate with one or more external devices 300 (such as a keyboard, a display, a network device, a Bluetooth device, etc.), enabling a user to interact with the electronic device 200 via these external devices 300, and / or enabling the electronic device 200 to communicate with one or more other data processing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 250, and can also be through the network adapter 260 with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet). The network adapter 260 can communicate with other modules of the electronic device 200 through the bus 230. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0104] Figure 7 is a schematic diagram of an embodiment of a computer-readable medium of the present invention. As Figure 7 shown, the computer program can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. When the computer program is executed by one or more data processing devices, the computer-readable medium can implement the above method of the present invention, that is: when receiving an application for processing a service from a user, obtaining the information of the user; constructing features according to the information of the user; querying a plurality of candidate service processing systems for processing the service; inputting the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one, and outputting the possibility that the plurality of candidate service processing systems accept the user request; inputting the features of the user into a plurality of response rate prediction models preset and corresponding to the plurality of candidate service processing systems one by one, and outputting the efficiency of the plurality of candidate service processing systems in processing the service for the user; selecting a recommended service processing system for recommending to the user to process the service from the plurality of candidate service processing systems according to the possibility that the plurality of candidate service processing systems accept the user request and the efficiency of processing the service for the user.

[0105] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described in the present invention can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a data processing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention.

[0106] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0107] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, executed as an independent 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 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, by using an Internet service provider to connect through the Internet).

[0108] In summary, the present invention can be implemented by a method, apparatus, electronic device, or computer-readable medium for executing a computer program. Some or all of the functions of the present invention can be implemented by using a general-purpose data processing device such as a microprocessor or a digital signal processor (DSP) in practice.

[0109] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A business processing method, characterized in that, it includes: When receiving an application for processing a business from a user, obtain the information of the user; Construct features according to the information of the user; Query multiple candidate business processing systems for processing the business; Train a passing rate prediction model and a response rate prediction model for each business processing system respectively according to the historical business handling data of each business processing system; Input the features of the user into multiple passing rate prediction models preset and corresponding to the multiple candidate business processing systems one by one, and output the possibility that the multiple candidate business processing systems accept the user request; Input the features of the user into multiple response rate prediction models preset and corresponding to the multiple candidate business processing systems one by one, and output the efficiency of the multiple candidate business processing systems in processing the business for the user; According to the possibility that the multiple candidate business processing systems accept the user request and the efficiency of processing the business for the user, select a recommended business processing system from the multiple candidate business processing systems for recommending to the user to process the business.

2. The business processing method according to claim 1, characterized in that, The information of the user includes: behavior data of the user that poses risks to the business; record data of the user using the application related to the business; credit data of the user recorded by a third party.

3. The business processing method according to claim 1, characterized in that, Before inputting the features of the user into multiple passing rate prediction models preset and corresponding to the multiple candidate business processing systems one by one, it further includes: Input the features of the user into a preset risk prediction model, and output the risks generated by the user in processing the business; Screen the multiple candidate business processing systems according to the risks generated by the user in processing the business and the risk-bearing capabilities of the multiple candidate business processing systems.

4. The business processing method according to claim 1, characterized in that, It further includes: When there are multiple recommended business processing systems, analyze the user's concerns about the business; According to the user's concerns, query information of the multiple recommended business processing systems that matches the concerns and push it to the user for the user to select from the multiple recommended business processing systems according to the information.

5. The business processing method according to claim 1, characterized in that, It further includes: When there are multiple recommended business processing systems, query the business processing systems used by other users associated with the user; If there is a business processing system used by the other user among the multiple recommended business processing systems, select the business processing system used by the other user to process the business for the user.

6. A business processing device, characterized in that, it includes: An information acquisition module, which obtains the information of the user when receiving an application for processing a business from the user; A feature construction module, which constructs features according to the information of the user; A system query module, which queries multiple candidate business processing systems for processing the business; A training module, configured to train a passing rate prediction model and a response rate prediction model for each business processing system respectively according to the historical business handling data of each business processing system. A passing rate analysis module, configured to input the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate business processing systems one by one, and output the possibility that the plurality of candidate business processing systems accept the user request. A response rate analysis module, configured to input the features of the user into a plurality of response rate prediction models preset and corresponding to the plurality of candidate business processing systems one by one, and output the efficiency of the plurality of candidate business processing systems in processing the business for the user. A system recommendation module, configured to select a recommended business processing system for recommending to the user to process the business from the plurality of candidate business processing systems according to the possibility that the plurality of candidate business processing systems accept the user request and the efficiency of processing the business for the user.

7. The business processing device according to claim 6, wherein, the information of the user includes: behavior data of the user that poses risks to the business; record data of the user using an application related to the business; credit data of the user recorded by a third party.

8. The business processing device according to claim 6, wherein, further comprising: A risk screening module, before inputting the features of the user into a plurality of passing rate prediction models preset and corresponding to the plurality of candidate business processing systems one by one, inputs the features of the user into a preset risk prediction model, outputs the risks generated by the user in processing the business, and screens the plurality of candidate business processing systems according to the risks generated by the user in processing the business and the risk-bearing capabilities of the plurality of candidate business processing systems.

9. The business processing device according to claim 6, wherein, further comprising: An information push module, when there are multiple recommended business processing systems, analyzes the user's concerns about the business, and queries and pushes information of the multiple recommended business processing systems that matches the concerns to the user for the user to select from the multiple recommended business processing systems according to the information.

10. The business processing device according to claim 6, wherein, further comprising: A system selection module, when there are multiple recommended business processing systems, queries the business processing systems used by other users associated with the user, and if there are business processing systems used by the other users among the multiple recommended business processing systems, selects the business processing systems used by the other users to process the business for the user.

11. An electronic device, comprising: a processor; and a memory storing computer-executable instructions, which when executed cause the processor to execute the method according to any one of claims 1-5.

12. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs, which, when executed by a processor, implement the method according to any one of claims 1-5.

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