A microfinance loan approval process matching method and device

Abstract

The present application provides a microfinance loan approval process matching method and device, which relates to the field of artificial intelligence and can also be used in the financial field, including: determining a loan approval model combination according to the characteristics of a loan group and its loan business needs; determining a loan approval process variable according to the loan approval model combination; and dynamically configuring a loan approval process according to the loan approval process variables and a preset loan approval process strategy. The present application can dynamically configure a loan approval process according to the characteristics of a loan group and its loan business needs.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and can be used in the financial field, specifically a method and device for matching the approval process of microfinance loans. Background Art

[0002] The main service objects of online loans of microfinance are micro-enterprises. As borrowers, micro-enterprises can apply for loans online and sign relevant contracts and promissory notes. Lending banks can make loans based on their credit, mortgages and pledges. As borrowers, micro-enterprises should repay the loans in accordance with the contracts and promissory notes. In the above process, loan approval is an important part, and the loan can only be made if the loan is approved.

[0003] Existing technologies usually use hard coding to implement loan approval. However, in actual business, the approval process for small and micro financial loans applied for by different groups of small and micro business owners is often different. Therefore, if hard coding is used to complete loan approval, it is necessary to hard code different groups of small and micro business owners separately to achieve it. If you need to add or adjust the approval process, you also need to modify the program again, and configure specific loan approval processes for different groups of small and micro business owners and launch them online. Moreover, if you need to add a certain group of small and micro business owners, you also need to reconfigure the strategy, and you cannot automatically match the approval process for the newly added group of small and micro business owners. The approval process has high maintenance costs and low efficiency. Considering the rapid expansion of the small and micro business owner group and the increasing need to add and change the approval process, the existing loan approval method faces huge challenges. Summary of the invention

[0004] In response to the problems in the prior art, the present application provides a microfinance loan approval process matching method and device, which can dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs.

[0005] In order to solve the above technical problems, this application provides the following technical solutions:

[0006] In a first aspect, the present application provides a microfinance loan approval process matching method, comprising:

[0007] Determine the combination of loan approval models based on the characteristics of the loan group and its loan business needs;

[0008] Determining loan approval process variables according to the loan approval model combination;

[0009] The loan approval process is dynamically configured according to the loan approval process variables and the preset loan approval process strategy.

[0010] Furthermore, the loan approval model combination is determined according to the characteristics of the loan group and its loan business needs, including:

[0011] Obtaining the characteristics of the loan group and its loan business needs;

[0012] Traversing each loan approval model and obtaining model characteristics of each loan approval model;

[0013] The loan approval model combination is determined according to the model characteristics, the characteristics of the loan group and its loan business needs.

[0014] Furthermore, the loan approval model combination includes a decision tree model and a formula editor model, and the step of determining the loan approval process variables according to the loan approval model combination includes:

[0015] Determine a first decision branch according to the decision tree model;

[0016] The first decision branch is input into the formula editor model, and loan approval process variables are determined.

[0017] Furthermore, the loan approval model combination includes a scorecard model and a formula editor model, and the step of determining the loan approval process variables according to the loan approval model combination includes:

[0018] Determining a second decision branch according to the scorecard model;

[0019] The second decision branch is input into the formula editor model, and loan approval process variables are determined.

[0020] Furthermore, the loan approval model combination includes a decision tree model, a scorecard model and a formula editor model, and the step of determining the loan approval process variables according to the loan approval model combination includes:

[0021] Determine a first decision branch according to the decision tree model;

[0022] Inputting the first decision branch into the scorecard model and determining a second decision branch;

[0023] The first decision branch and the second decision branch are input into the formula editor model, and loan approval process variables are determined.

[0024] Furthermore, the microfinance loan approval process matching method further includes:

[0025] The loan approval process is dynamically added or deleted according to the loan approval process variables and the preset loan approval process strategy.

[0026] In a second aspect, the present application provides a small and micro financial loan approval process matching device, comprising:

[0027] A model combination determination unit is used to determine a loan approval model combination according to the characteristics of the loan group and its loan business needs;

[0028] A process variable determination unit, used to determine the loan approval process variables according to the loan approval model combination;

[0029] The dynamic configuration unit is used to dynamically configure the loan approval process according to the loan approval process variables and the preset loan approval process strategy.

[0030] Furthermore, the model combination determination unit includes:

[0031] A feature demand acquisition module is used to acquire the features of the loan group and its loan business needs;

[0032] A model feature acquisition module, used to traverse each loan approval model and acquire the model features of each loan approval model;

[0033] The model combination determination module is used to determine the loan approval model combination according to the model characteristics, the characteristics of the loan group and its loan business needs.

[0034] Furthermore, the loan approval model combination includes a decision tree model and a formula editor model, and the process variable determination unit includes:

[0035] A first branch determination module, used to determine a first decision branch according to the decision tree model;

[0036] The process variable determination module is used to input the first decision branch into the formula editor model and determine the loan approval process variables.

[0037] Furthermore, the loan approval model combination includes a scorecard model and a formula editor model, and the process variable determination unit includes:

[0038] A second branch determination module, used to determine a second decision branch according to the scorecard model;

[0039] The process variable determination module is used to input the second decision branch into the formula editor model and determine the loan approval process variables.

[0040] Furthermore, the loan approval model combination includes a decision tree model, a scorecard model and a formula editor model, and the process variable determination unit includes:

[0041] A first branch determination module, used to determine a first decision branch according to the decision tree model;

[0042] A second branch determination module, configured to input the first decision branch into the scorecard model and determine a second decision branch;

[0043] The process variable determination module is used to input the first decision branch and the second decision branch into the formula editor model and determine the loan approval process variables.

[0044] Furthermore, the microfinance loan approval process matching device further includes:

[0045] The dynamic adding and deleting unit is used to dynamically add and delete loan approval processes according to the loan approval process variables and preset loan approval process strategies.

[0046] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the microfinance loan approval process matching method when executing the program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the microfinance loan approval process matching method.

[0048] In response to the problems in the prior art, the microfinance loan approval process matching method and device provided in this application can dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, greatly reducing the cost of adding, deleting and modifying the loan approval process, and improving the adaptability of the loan approval process for adding, deleting and modifying the micro-enterprise owner group, effectively helping to promote the microfinance online loan business. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 This is a flow chart of the small and micro financial loan approval process matching method in the embodiment of this application;

[0051] Figure 2 This is one of the structural diagrams of the small and micro financial loan approval process matching method in the embodiment of this application;

[0052] Figure 3 A flowchart for determining a loan approval model combination in an embodiment of the present application;

[0053] Figure 4 This is one of the flow charts for determining loan approval process variables in an embodiment of the present application;

[0054] Figure 5 This is the second flowchart for determining loan approval process variables in the embodiment of the present application;

[0055] Figure 6 This is the third flowchart of determining loan approval process variables in the embodiment of the present application;

[0056] Figure 7 This is a structural diagram of the small and micro financial loan approval process matching device in the embodiment of this application;

[0057] Figure 8 This is a structural diagram of a model combination determination unit in an embodiment of the present application;

[0058] Fig. 9 This is one of the structural diagrams of the process variable determination unit in the embodiment of the present application;

[0059] Fig.10 This is the second structural diagram of the process variable determination unit in the embodiment of the present application;

[0060] Fig.11 This is the third structural diagram of the process variable determination unit in the embodiment of the present application;

[0061] Fig.12 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application;

[0062] Fig.13 This is the second structural diagram of the small and micro financial loan approval process matching method in the embodiment of this application;

[0063] Fig.14 This is the third structural diagram of the small and micro financial loan approval process matching method in the embodiment of this application;

[0064] Fig.15 This is a system diagram of the small and micro financial loan approval process matching method in the embodiment of this application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] It should be noted that the microfinance loan approval process matching method and device provided in this application can be used in the financial field, and can also be used in any field other than the financial field. The application field of the microfinance loan approval process matching method and device provided in this application is not limited.

[0067] In one embodiment, see Figure 1 In order to dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, this application provides a small and micro financial loan approval process matching method, including:

[0068] S101: Determine the loan approval model combination based on the characteristics of the loan group and its loan business needs;

[0069] S102: Determine loan approval process variables according to the loan approval model combination;

[0070] It is understandable that the microfinance loan approval process matching method provided in this application is intended to dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs. In actual business, as a loan group, the loan business needs of small and micro business owners are diverse and personalized. When approving loans, it is often necessary to add, delete or change the loan approval process according to the characteristics of the loan group of small and micro business owners and their loan business needs. Therefore, there is an urgent need for a method that can realize automatic matching of loan approval processes.

[0071] In order to achieve the above goals, it is first necessary to determine the characteristics of different small and micro business owner loan groups and their loan business needs. For example, small and micro business owner borrowers can be real estate mortgage loan groups. The characteristics of this group may include but are not limited to owning real estate as collateral, and their business needs may be high-value financing with real estate as collateral. According to the characteristics of the small and micro business owner loan group and their loan business needs, a loan approval model combination can be determined. The so-called loan approval model combination refers to a combination of multiple loan approval models including a decision tree model, a scoring card model and a formula editor model. The loan approval model combination does not limit the number of the above three models. In other words, the loan approval model combination can include N decision tree models and M formula editor models; or R scoring card models and T formula editor models; or X decision tree models, Y scoring card models and Z formula editor models, where N, M, R, T, X, Y and Z are all positive integers.

[0072] Specifically, ① the decision tree model is used to make conditional judgments and output branch results. For example, the condition is set to judge whether the loan application amount is greater than 10 million. If it is greater, the output is "branch 1" result; otherwise, the output is "branch 2". ② The scoring card model is used to score multiple judgment rules, and finally judge and output branch results based on the comprehensive score. For example, the scoring card model is set to "customer risk item screening model", and there are two judgment rules. Among them, rule 1 is to judge whether the customer's business term is less than 1 year. If it is less than 1 year, the score is 0, otherwise the score is 1; rule 2 is to judge whether the customer has changed the legal representative within 90 days before applying for a loan. If yes, the score is 1, otherwise the score is 0. Add the score values ​​of the two rules to calculate the total score value of the customer, and then judge the total score value of the customer. If the total score value of the customer is greater than 1, it is judged as "branch 1", otherwise it is judged as "branch 2". The final output result of this model is "branch 1" or "branch 2". ③ The formula editor model is used to output the decision variable value, which can usually output a fixed string. For example: Formula Editor 1 is set to output "Process A". When the model is executed, the output result is "Process A".

[0073] It can be seen from the above description that usually the loan approval model combination must include a formula editor model in order to finally output the loan approval process variables (S102).

[0074] For example, taking the small and micro-enterprise real estate mortgage loan scenario as an example, you can choose to configure a "risk issue screening control model", which is a scoring card model. By setting multiple judgment rules such as whether the customer has dealings with private financing companies containing sensitive fields, the total score is finally obtained and output, and subsequent branch judgments are made based on the total score.

[0075] It should be noted that the decision tree model, scorecard model and formula editor model can all be implemented based on the decision engine. The decision engine is an enterprise-level online risk decision system that can provide functions such as decision models, decision services and decision flows, providing a basis for banks and other financial institutions to implement online risk control.

[0076] S103: Dynamically configuring the loan approval process according to the loan approval process variables and a preset loan approval process strategy.

[0077] It is understandable that after obtaining the loan approval process variables, the loan approval process can be dynamically configured according to the loan approval process variables and the preset loan approval process strategy. Among them, the loan approval process strategy is used to dynamically configure the loan approval process according to the loan approval process variables. The hardware system on which the above method is based can provide automatic matching services for loan approval processes in a distributed service manner.

[0078] For example, the loan approval process strategy may be: when the loan approval process variables are "Process A" and "Process C", the dynamic matching approval process is Approval Process 1, otherwise it is Approval Process 2.

[0079] From the above description, it can be seen that the microfinance loan approval process matching method provided by this application can dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, greatly reducing the cost of adding, deleting and modifying the loan approval process, and improving the adaptability of the loan approval process for adding, deleting and modifying the micro-business owner group, effectively helping to promote the microfinance online loan business.

[0080] In one embodiment, see Figure 3 The loan approval model combination is determined according to the characteristics of the loan group and its loan business needs, including:

[0081] S301: Obtaining the characteristics of the loan group and its loan business needs;

[0082] S302: traversing each loan approval model and obtaining model features of each loan approval model;

[0083] S303: Determine a loan approval model combination according to the model characteristics, the characteristics of the loan group and its loan business needs.

[0084] It is understandable that each loan approval model is pre-generated and stored in the database based on historical big data, and the number of them may be large. Therefore, it is necessary to traverse these loan approval models and find the model features of each loan approval model. For example, a decision tree model in the loan approval model includes several nodes and branches, and what are the judgment conditions set for each branch, etc. The same is true for other loan approval models, which will not be repeated. Finally, according to the model features, several loan approval models that match or correspond to the characteristics of the loan group and its loan business needs are found as a loan approval model combination.

[0085] From the above description, it can be seen that the microfinance loan approval process matching method provided in this application can determine the loan approval model combination according to the characteristics of the loan group and its loan business needs.

[0086] In one embodiment, see Figure 4 The loan approval model combination includes a decision tree model and a formula editor model, and the loan approval process variables are determined according to the loan approval model combination, including:

[0087] S401: Determine a first decision branch according to the decision tree model;

[0088] S402: Input the first decision branch into the formula editor model and determine the loan approval process variables.

[0089] In one embodiment, see Figure 5 The loan approval model combination includes a scorecard model and a formula editor model, and the step of determining the loan approval process variables according to the loan approval model combination includes:

[0090] S501: Determine a second decision branch according to the scorecard model;

[0091] S502: Input the second decision branch into the formula editor model and determine the loan approval process variables.

[0092] In one embodiment, see Figure 6 The loan approval model combination includes a decision tree model, a score card model and a formula editor model, and the loan approval process variables are determined according to the loan approval model combination, including:

[0093] S601: Determine a first decision branch according to the decision tree model;

[0094] S602: Inputting the first decision branch into the scorecard model, and determining a second decision branch;

[0095] S603: Input the first decision branch and the second decision branch into the formula editor model, and determine the loan approval process variables.

[0096] It can be understood that the above steps S401 to S402, steps S501 to S502 and steps S601 to S603 can be understood with reference to the following embodiments.

[0097] See also Fig.13 , taking the small and micro enterprise real estate mortgage loan scenario as an example:

[0098] (1) Determine the loan approval model combination using the method described in steps S301 to S303: decision tree 1 (determination of customer application amount), score card 1 (customer risk item screening model), formula editor 1 (output "process A"), formula editor 2 (output "process B"), formula editor 3 (output "process C"), and formula editor 4 (output "process D");

[0099] (2) The process of determining the variables of the loan approval process is as follows:

[0100] A. Execute decision tree 1 first, make branch judgments based on the amount of money applied by the customer, and output "branch 1" or "branch 2" based on the judgment results;

[0101] B. If the above model result is "branch 1", execute formula editor 1, and the output decision variable value is: "process A"; if the above model result is "branch 2", execute formula editor 2, and the output decision variable value is: "process B";

[0102] C. Execute the scorecard 1 model again to make a judgment on the customer risk item screening model, and also output the judgment result as "branch 1" or "branch 2";

[0103] D. If the above model result is "branch 1", execute formula editor 3 and output another decision variable value as "process C"; if the above model result is "branch 2", execute formula editor 4 and output another decision variable value as "process D";

[0104] E. After the execution process of all models is completed, there are two final output decision variable values, which may correspond to four situations: "Process A" and "Process C", "Process A" and "Process D", "Process B" and "Process C", "Process B" and "Process D".

[0105] Among them, "Process A", "Process B", "Process C" and "Process D" are loan approval process variables.

[0106] It should be noted that sometimes the loan approval model combination determined by the method described in steps S301 to S303 includes a decision tree model and a formula editor model, as shown in steps S401 to S402; sometimes the loan approval model combination determined by the method described in steps S301 to S303 includes a score card model and a formula editor model, as shown in steps S501 to S502; sometimes the loan approval model combination determined by the method described in steps S301 to S303 includes a decision tree model, a score card model and a formula editor model, as shown in steps S601 to S603. The execution ideas of the above various situations are similar and will not be repeated.

[0107] From the above description, it can be seen that the microfinance loan approval process matching method provided in this application can determine the loan approval process variables according to the loan approval model combination.

[0108] In one embodiment, the microfinance loan approval process matching method further includes: dynamically adding and deleting loan approval processes according to the loan approval process variables and a preset loan approval process strategy.

[0109] It can be understood that the dynamic addition and deletion process can be completed based on the loan approval process strategy. Each loan approval process is all types of approval processes in the corresponding scenario, and the process matching strategy is the mapping relationship between the loan approval process variables and the approval process.

[0110] From the above description, it can be seen that the microfinance loan approval process matching method provided in this application can dynamically add or delete loan approval processes according to the loan approval process variables and preset loan approval process strategies.

[0111] It should be noted that see Figure 2 The system implementing the method described in the present application may include a model configuration device and a model data storage device. After a specific model is configured in the model configuration device, it will be stored in the model data storage device in real time. The model data storage device provides data query services to the outside, specifically:

[0112] ① The model configuration device is used to add, delete and modify the front-end model, which is realized through the vue front-end framework and related component libraries. System personnel can access the visualization page through the browser and enter the model data through selection and input;

[0113] ② The model data storage device is used for adding, deleting and modifying model data storage at the front end. It is a database server and can use the Oracle database table structure to implement data storage. It provides data query services to the outside world in a distributed service manner based on the dubbo framework.

[0114] An example is given below to illustrate the method provided by the present application in more detail. Fig.14 .

[0115] ① Model process configuration device: used to drag and drop configuration and modify the required model, and perform model execution process. The example is as follows: Taking the above-mentioned small and micro enterprise real estate mortgage loan scenario as an example, you can drag 1 decision tree block, 1 score card block and 4 formula editor blocks into the canvas, 1 start block and 1 end block each; click on a specific graphic block and select the corresponding decision tree 1, score card 1, formula editor 1, formula editor 2, formula editor 3, formula editor 4; adjust the position and connect each graphic block to realize process configuration.

[0116] ②Model process storage device: used to store the required models and model execution process data. It is a database server that uses the Oracle database table structure to implement data storage and provides model process data query services based on dubbo distributed services.

[0117] ③Decision variable output service: used to execute the configured model process, output one or more decision variable values, implemented based on the decision engine, and provide services based on dubbo distributed services.

[0118] Taking small and micro enterprise mortgage loans as an example, the method described in steps S301 to S303 can be used to configure two approval processes in this scenario: Approval process 1 is investigation, review, and approval; Approval process 2 is investigation and verification. The configuration process matching strategy can be: when the input loan approval process variables are "Process A" and "Process C", the corresponding approval process is Approval process 1, otherwise it is Approval process 2.

[0119] In one embodiment, see Figure 2 As described above, the approval configuration and automatic matching system may include an approval process configuration device, an approval process data storage device, and a process matching service. After configuring the corresponding approval process and process matching strategy in the approval process configuration device, the data is stored in the approval process data storage device in real time, and the system provides process matching services to the outside in the form of distributed services.

[0120] (1) Approval process configuration device: used to configure the loan product approval process and process matching strategy. It is implemented through the vue front-end framework and related component libraries. System personnel can access the visualization page through the browser in the device and configure the approval process and process matching strategy by selecting and filling in information.

[0121] (2) Approval process data storage device: used to store loan product approval process and process matching strategy data, implemented using a database table structure, and providing model process data query services.

[0122] (3) Process matching service, which is used to output the final matching approval process. The execution process is as follows: Fig.15 :A. After configuring the model, model process, approval process and process matching strategy, call the process matching service;

[0123] B. The approval configuration and automatic matching system calls the decision variable output service provided by the model process configuration system to obtain the decision variable value;

[0124] C. The model process configuration system queries the model process information required for process matching and parses out the specific model used; calls the model information query service provided by the model configuration system to obtain specific model information; executes the model process to output loan approval process variables;

[0125] D. Approval configuration and automatic matching The system queries the approval process and process matching strategy, automatically matches the process according to the input loan approval process variables, and finally outputs the matching process.

[0126] Based on the same inventive concept, the embodiments of the present application also provide a microfinance loan approval process matching device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of solving the problem by the microfinance loan approval process matching device is similar to that of the microfinance loan approval process matching method, the implementation of the microfinance loan approval process matching device can refer to the implementation of the method based on software performance benchmark determination, and the repetitions will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0127] In one embodiment, see Figure 7 In order to dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, the present application provides a microfinance loan approval process matching device, including: a model combination determination unit 701, a process variable determination unit 702 and a dynamic configuration unit 703.

[0128] The model combination determination unit 701 is used to determine the loan approval model combination according to the characteristics of the loan group and its loan business needs;

[0129] A process variable determination unit 702, used to determine the loan approval process variables according to the loan approval model combination;

[0130] The dynamic configuration unit 703 is used to dynamically configure the loan approval process according to the loan approval process variables and the preset loan approval process strategy.

[0131] In one embodiment, see Figure 8 The model combination determination unit 701 includes: a feature requirement acquisition module 801, a model feature acquisition module 802 and a model combination determination module 803.

[0132] The feature demand acquisition module 801 is used to acquire the features of the loan group and its loan business demand;

[0133] A model feature acquisition module 802 is used to traverse each loan approval model and acquire the model features of each loan approval model;

[0134] The model combination determination module 803 is used to determine the loan approval model combination according to the model characteristics, the characteristics of the loan group and its loan business needs.

[0135] In one embodiment, see Fig. 9The loan approval model combination includes a decision tree model and a formula editor model. The process variable determination unit 702 includes: a first branch determination module 901 and a process variable determination module 902.

[0136] A first branch determination module 901, configured to determine a first decision branch according to the decision tree model;

[0137] The process variable determination module 902 is used to input the first decision branch into the formula editor model and determine the loan approval process variables.

[0138] In one embodiment, see Fig.10 The loan approval model combination includes a scoring card model and a formula editor model. The process variable determination unit 702 includes: a second branch determination module 1001 and a process variable determination module 1002.

[0139] A second branch determination module 1001, configured to determine a second decision branch according to the scorecard model;

[0140] The process variable determination module 1002 is used to input the second decision branch into the formula editor model and determine the loan approval process variables.

[0141] In one embodiment, see Fig.11 The loan approval model combination includes a decision tree model, a scoring card model and a formula editor model. The process variable determination unit 702 includes: a first branch determination module 1101, a second branch determination module 1102 and a process variable determination module 1103.

[0142] A first branch determination module 1101, configured to determine a first decision branch according to the decision tree model;

[0143] A second branch determination module 1102, configured to input the first decision branch into the scorecard model and determine a second decision branch;

[0144] The process variable determination module 1103 is used to input the first decision branch and the second decision branch into the formula editor model and determine the loan approval process variables.

[0145] In one embodiment, the microfinance loan approval process matching device further includes:

[0146] The dynamic adding and deleting unit is used to dynamically add and delete loan approval processes according to the loan approval process variables and preset loan approval process strategies.

[0147] From the hardware level, in order to dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, the present application provides an embodiment of an electronic device for implementing all or part of the content of the microfinance loan approval process matching method, and the electronic device specifically includes the following content:

[0148] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the microfinance loan approval process matching device and related devices such as core business systems, user terminals and related databases; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., and this embodiment is not limited to this. In this embodiment, the logic controller can be implemented with reference to the embodiment of the microfinance loan approval process matching method in the embodiment, and the embodiment of the microfinance loan approval process matching device, and the contents are merged here, and the repeated parts are not repeated.

[0149] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0150] In practical applications, part of the microfinance loan approval process matching method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0151] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0152] Fig.12 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig.12As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig.12 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0153] In one embodiment, the microfinance loan approval process matching method function may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:

[0154] S101: Determine the loan approval model combination based on the characteristics of the loan group and its loan business needs;

[0155] S102: Determine loan approval process variables according to the loan approval model combination;

[0156] S103: Dynamically configuring the loan approval process according to the loan approval process variables and a preset loan approval process strategy.

[0157] From the above description, it can be seen that the microfinance loan approval process matching method provided by the present application can dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, greatly reducing the cost of adding, deleting and modifying the loan approval process, and improving the adaptability of the loan approval process for adding, deleting and modifying the micro-enterprise owner group, which effectively helps promote the microfinance online loan business. In another embodiment, the microfinance loan approval process matching device can be configured separately from the central processor 9100. For example, the data composite transmission device microfinance loan approval process matching device can be configured as a chip connected to the central processor 9100, and the function of the microfinance loan approval process matching method is realized through the control of the central processor.

[0158] like Fig.12 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig.12 In addition, the electronic device 9600 may also include Fig.12 For components not shown, reference may be made to the prior art.

[0159] like Fig.12 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device 9600.

[0160] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0161] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0162] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0163] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0164] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0165] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0166] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the microfinance loan approval process matching method in the above embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the microfinance loan approval process matching method in the above embodiment are implemented by the execution subject being a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0167] S101: Determine the loan approval model combination based on the characteristics of the loan group and its loan business needs;

[0168] S102: Determine loan approval process variables according to the loan approval model combination;

[0169] S103: Dynamically configuring the loan approval process according to the loan approval process variables and a preset loan approval process strategy.

[0170] From the above description, it can be seen that the microfinance loan approval process matching method provided by this application can dynamically configure the loan approval process according to the characteristics of the loan group and its loan business needs, greatly reducing the cost of adding, deleting and modifying the loan approval process, and improving the adaptability of the loan approval process for adding, deleting and modifying the micro-business owner group, effectively helping to promote the microfinance online loan business.

[0171] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0175] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A microfinance loan approval process matching method, characterized in that: include: Determine the combination of loan approval models based on the characteristics of the loan group and its loan business needs; Determining loan approval process variables according to the loan approval model combination; Dynamically configure the loan approval process according to the loan approval process variables and the preset loan approval process strategy; The step of determining the loan approval model combination according to the characteristics of the loan group and its loan business needs includes: Obtaining the characteristics of the loan group and its loan business needs; Traversing each loan approval model and obtaining model characteristics of each loan approval model; Determine a loan approval model combination according to the model characteristics, the characteristics of the loan group and its loan business needs; Wherein, when the loan approval model combination includes a decision tree model and a formula editor model, determining the loan approval process variables according to the loan approval model combination includes: Determine a first decision branch according to the decision tree model; Inputting the first decision branch into the formula editor model and determining loan approval process variables; Wherein, when the loan approval model combination includes a scorecard model and a formula editor model, determining the loan approval process variables according to the loan approval model combination includes: Determining a second decision branch according to the scorecard model; Inputting the second decision branch into the formula editor model and determining loan approval process variables; Wherein, when the loan approval model combination includes a decision tree model, a scorecard model and a formula editor model, determining the loan approval process variables according to the loan approval model combination includes: Determine a first decision branch according to the decision tree model; Inputting the first decision branch into the scorecard model and determining a second decision branch; The first decision branch and the second decision branch are input into the formula editor model, and loan approval process variables are determined.

2. The microfinance loan approval process matching method according to claim 1 is characterized in that: Also includes: The loan approval process is dynamically added or deleted according to the loan approval process variables and the preset loan approval process strategy.

3. A small and micro financial loan approval process matching device, characterized in that: include: A model combination determination unit is used to determine a loan approval model combination according to the characteristics of the loan group and its loan business needs; A process variable determination unit, used to determine the loan approval process variables according to the loan approval model combination; A dynamic configuration unit, used to dynamically configure the loan approval process according to the loan approval process variables and a preset loan approval process strategy; Wherein, the model combination determination unit includes: A feature demand acquisition module is used to acquire the features of the loan group and its loan business needs; A model feature acquisition module, used to traverse each loan approval model and acquire the model features of each loan approval model; A model combination determination module, used to determine a loan approval model combination according to the model characteristics, the characteristics of the loan group and its loan business needs; Wherein, when the loan approval model combination includes a decision tree model and a formula editor model, the process variable determination unit includes: A first branch determination module, used to determine a first decision branch according to the decision tree model; A process variable determination module, used for inputting the first decision branch into the formula editor model and determining loan approval process variables; Wherein, when the loan approval model combination includes a scorecard model and a formula editor model, the process variable determination unit includes: A second branch determination module, used to determine a second decision branch according to the scorecard model; A process variable determination module, used for inputting the second decision branch into the formula editor model and determining the loan approval process variables; Wherein, when the loan approval model combination includes a decision tree model, a scorecard model and a formula editor model, the process variable determination unit includes: A first branch determination module, used to determine a first decision branch according to the decision tree model; A second branch determination module, configured to input the first decision branch into the scorecard model and determine a second decision branch; The process variable determination module is used to input the first decision branch and the second decision branch into the formula editor model and determine the loan approval process variables.

4. The microfinance loan approval process matching device according to claim 3 is characterized in that: Also includes: The dynamic adding and deleting unit is used to dynamically add and delete loan approval processes according to the loan approval process variables and preset loan approval process strategies.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the microfinance loan approval process matching method described in any one of claims 1 to 2 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the microfinance loan approval process matching method described in any one of claims 1 to 2 are implemented.

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