CN117952547A - Intelligent examination and approval method and device for express expense based on machine learning - Google Patents
Intelligent examination and approval method and device for express expense based on machine learning Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent approval of express expense and discloses an intelligent approval method and device of express expense based on machine learning; according to the method, through obtaining historical expense application sample data, key characteristic information affecting approval results in the historical expense application sample data is extracted, characteristic processing is carried out on the key characteristic information to obtain a sample data set, and the sample data set is used as model training data; constructing a cost application approval prediction model according to model training data; deploying a fee application approval prediction model as an online service; acquiring new expense application information, extracting new expense application characteristic values, calling an expense application approval prediction model to predict the new expense application characteristic values in real time, and generating application passing probability information; the method has the advantages that the application passing probability information and the preset threshold are obtained, automatic approval or reminding of manual approval is achieved according to the preset threshold and the application passing probability information, and automatic approval is achieved by replacing manual approval.
Description
Technical Field
The invention relates to the technical field of intelligent approval of express expense, in particular to an intelligent approval method and device of express expense based on machine learning.
Background
The current function of the expense application system is not sound, the allocation and the network site can only apply for expense items to the waybill and simply check the history record of the application, the expense application state of the network site can not be checked and tracked, more detailed expense details and processing states can not be checked, and the problems of long expense application period, slow processing, untimely response and the like exist; meanwhile, the current expense approval of the express enterprises mainly depends on manual flow, the efficiency is low, and the automation cannot be realized; the manual approval cannot predict the new application by using the historical data, and the problem of inconsistent decision is caused.
Therefore, the intelligent examination and approval method for the express expense based on machine learning is provided, and the problems are solved.
Disclosure of Invention
The invention mainly aims to solve the problems that in the prior art, the cost approval of the express enterprises mainly depends on manual flow, the efficiency is low, and the automation cannot be realized; the manual approval cannot predict the new application by using the historical data, and the problem of inconsistent decision is caused.
The first aspect of the invention provides an intelligent examination and approval method for express expense based on machine learning, which comprises the following steps:
acquiring historical expense application sample data, extracting key characteristic information influencing an approval result in the historical expense application sample data, performing characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
obtaining model training data, and constructing a cost application approval prediction model according to the model training data;
Acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service;
acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information;
Acquiring the probability information of passing application and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the probability information of passing application.
Optionally, the obtaining the historical expense application sample data, extracting key feature information affecting the approval result in the historical expense application sample data, and performing feature processing on the key feature information to obtain a sample data set includes:
Acquiring historical expense application sample data;
Extracting key characteristic information affecting an approval result from historical expense application sample data, wherein the key characteristic information comprises application amount, application times, applicant credit level, application form number type and receiving/sending area information;
and carrying out box division processing on the key characteristic information, and simultaneously marking target variables in the sample data set, wherein the target variables comprise application passing and application refused, and generating the sample data set according to the target variables and the processed key characteristic information.
Optionally, the obtaining the model training data, and constructing the cost application approval prediction model according to the model training data includes:
step one, calculating the information entropy Ent (D) of the data set D:
Ent(D)＝-Σp(x)log2p(x)
Step two, calculating the information gain g (D, A) of the feature A on the data set D:
g(D,A)＝Ent(D)-Σ(|Dj|/|D|)Ent(Dj)
And thirdly, selecting the characteristic with the maximum information gain as a node, and recursively generating a decision tree.
And repeating the second step and the third step until a preset stopping condition is reached, so as to obtain a trained expense application prediction approval model.
Optionally, the obtaining the model training data, and constructing the cost application approval prediction model according to the model training data further includes:
Acquiring verification set data and a cost application approval prediction model;
Evaluating the effect of a cost application approval prediction model by using verification set data, wherein evaluation indexes comprise accuracy, recall and ROC curves;
and (5) adjusting the model super-parameters to obtain an optimal model.
Optionally, the obtaining the application passing probability information and the preset threshold, and implementing automatic approval or reminding of manual approval according to the preset threshold and the application passing probability information includes:
Acquiring application passing probability information and a preset threshold value;
Judging whether the probability information of passing application is larger than a preset threshold value or not; if yes, automatic approval is carried out; if not, sending the manual approval reminding information.
Optionally, the obtaining a fee application approval prediction model deploys the fee application approval prediction model as an online service as follows:
and acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into the REST API service.
The second aspect of the invention provides an intelligent examination and approval device for express expense based on machine learning, which comprises the following components:
The model training data acquisition module is used for acquiring historical expense application sample data, extracting key characteristic information affecting approval results in the historical expense application sample data, carrying out characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
The model generation module is used for acquiring model training data and constructing a cost application approval prediction model according to the model training data;
The model deployment module is used for acquiring a cost application approval prediction model and deploying the cost application approval prediction model into an online service;
The probability information generation module is used for acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, calling an expense application approval prediction model to predict the new expense application characteristic values in real time, and generating application passing probability information;
And the approval executing module is used for acquiring the application passing probability information and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the application passing probability information.
A third aspect of the present invention provides an electronic device, comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line; the at least one processor invokes the instructions in the memory to cause the electronic device to perform the machine learning based intelligent approval method for courier costs described above.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform the above-described intelligent approval method for express delivery fees based on machine learning.
In the technical scheme of the invention, the system uses a decision tree machine learning algorithm to realize intelligent approval of the fee application; the decision tree model is trained by utilizing historical application sample data, and the application passing probability is predicted in real time according to the characteristics of the application amount, the times, the credit of the applicant and the like, so that the traditional manual approval is replaced, and the automation is realized.
Drawings
FIG. 1 is a first flowchart of an intelligent approval method for express expense based on machine learning according to an embodiment of the present invention;
FIG. 2 is a second flowchart of an intelligent approval method for express expense based on machine learning according to an embodiment of the present invention;
FIG. 3 is a third flowchart of an intelligent approval method for express expense based on machine learning according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of an intelligent examination and approval device for express expense based on machine learning according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides an intelligent examination and approval method and device for express expense based on machine learning, comprising the steps of obtaining historical expense application sample data, extracting key characteristic information affecting examination and approval results in the historical expense application sample data, carrying out characteristic processing on the key characteristic information to obtain a sample data set, and taking the sample data set as model training data; obtaining model training data, and constructing a cost application approval prediction model according to the model training data; acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service; acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information; the method and the device for automatically approving or reminding the manual approval by the probability information obtain the application passing probability information and the preset threshold, and according to the preset threshold and the application passing probability information, the problems that in the prior art, the cost application period is long, the processing is slow, the response is not timely, the cost approval of an express enterprise mainly depends on the manual flow, the efficiency is low, and the automation cannot be realized are solved. The manual approval cannot predict the new application by using the historical data, and the problem of inconsistent decision exists.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For easy understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and a first embodiment of a method for intelligently approving express delivery fees based on machine learning in the embodiment of the present invention includes:
acquiring historical expense application sample data, extracting key characteristic information influencing an approval result in the historical expense application sample data, performing characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
obtaining model training data, and constructing a cost application approval prediction model according to the model training data;
acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service; the cost application approval prediction model is specifically deployed as REST API service.
Acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information;
Acquiring the probability information of passing application and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the probability information of passing application.
Referring to fig. 2, a second embodiment of the intelligent approval method for express expense based on machine learning according to the embodiment of the present invention includes:
acquiring historical expense application sample data, extracting key characteristic information influencing an approval result in the historical expense application sample data, performing characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
The method specifically comprises the following steps:
Acquiring historical expense application sample data;
Extracting key characteristic information affecting an approval result from historical expense application sample data, wherein the key characteristic information comprises application amount, application times, applicant credit level, application form number type and receiving/sending area information; the passing probability of the application is predicted in real time according to the characteristics of the application amount, the times, the credit of the applicant and the like, the traditional manual approval is replaced, and the automation is realized.
Carrying out box division processing on the key characteristic information, and simultaneously marking target variables in the sample data set, wherein the target variables comprise application passing and application refused, and generating the sample data set according to the target variables and the processed key characteristic information;
obtaining model training data, and constructing a cost application approval prediction model according to the model training data;
acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service; the cost application approval prediction model is specifically deployed as REST API service.
Acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information; the intelligent approval based on machine learning not only improves the approval efficiency, but also ensures that the approval result is more reliable and consistent.
Acquiring the probability information of passing application and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the probability information of passing application.
Referring to fig. 3, a third embodiment of an intelligent approval method for express expense based on machine learning according to the embodiment of the present invention includes:
acquiring historical expense application sample data, extracting key characteristic information influencing an approval result in the historical expense application sample data, performing characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
The method specifically comprises the following steps:
Acquiring historical expense application sample data;
Extracting key characteristic information affecting an approval result from historical expense application sample data, wherein the key characteristic information comprises application amount, application times, applicant credit level, application form number type and receiving/sending area information; the passing probability of the application is predicted in real time according to the characteristics of the application amount, the times, the credit of the applicant and the like, the traditional manual approval is replaced, and the automation is realized.
Carrying out box division processing on the key characteristic information, and simultaneously marking target variables in the sample data set, wherein the target variables comprise application passing and application refused, and generating the sample data set according to the target variables and the processed key characteristic information;
Obtaining model training data, and constructing a cost application approval prediction model according to the model training data; the construction process of the expense application approval prediction model comprises the following steps:
step one, calculating the information entropy Ent (D) of the data set D:
Ent(D)＝-Σp(x)log2p(x)；
Step two, calculating the information gain g (D, A) of the feature A on the data set D:
g(D,A)＝Ent(D)-Σ(|Dj|/|D|)Ent(Dj)；
step three, selecting the characteristic with the maximum information gain as a node, and recursively generating a decision tree;
And repeating the second step and the third step until a preset stopping condition is reached, so as to obtain a trained expense application prediction approval model.
Acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service; the cost application approval prediction model is specifically deployed as REST API service.
Acquiring verification set data and a cost application approval prediction model;
The effect of the expense application approval prediction model is evaluated by using verification set data, wherein the main evaluation index is the accuracy rate, namely the proportion of the number of predicted correct samples to the total number of samples;
Recall, namely predicting the proportion of the correct positive number to the total positive number;
ROC curve, real rate and false positive rate under different thresholds;
And (3) adjusting model hyper-parameters such as maximum tree depth, minimum sample number of leaf nodes and the like to obtain an optimal model.
Acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information; the intelligent approval based on machine learning not only improves the approval efficiency, but also ensures that the approval result is more reliable and consistent.
Acquiring application passing probability information and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the application passing probability information, wherein the method specifically comprises the following steps:
Acquiring application passing probability information and a preset threshold value; judging whether the probability information of passing application is larger than a preset threshold value or not; if yes, automatic approval is carried out; if not, sending the manual approval reminding information. The approval cost can be reduced, the enterprise efficiency is improved, the user experience is improved, and the intelligent brand image is established; applying a machine learning technology to acquire rule knowledge of approval decision; searching a new service scene, and realizing the full-flow intellectualization of application;
In this embodiment, the implementation of the system mainly includes: a data layer, a model layer, a business layer and a presentation layer.
The above describes the intelligent examination and approval method for the express expense based on the machine learning in the embodiment of the present invention, and the following describes the intelligent examination and approval device for the express expense based on the machine learning in the embodiment of the present invention, referring to fig. 4, the intelligent examination and approval device for the express expense based on the machine learning in the embodiment of the present invention includes, for the above embodiment:
The model training data obtaining module 401 is configured to obtain historical expense application sample data, extract key feature information affecting an approval result in the historical expense application sample data, perform feature processing on the key feature information to obtain a sample data set, and use the sample data set as model training data;
the model generation module 402 is configured to obtain model training data, and construct a cost application approval prediction model according to the model training data;
The model deployment module 403 is configured to obtain a fee application approval prediction model, and deploy the fee application approval prediction model into an online service;
The probability information generating module 404 is configured to obtain new fee application information, extract a new fee application feature value in the new fee application information, and invoke a fee application approval prediction model to predict the new fee application feature value in real time, so as to generate application passing probability information;
and the approval executing module 405 is configured to obtain the application passing probability information and a preset threshold, and implement automatic approval or remind of manual approval according to the preset threshold and the application passing probability information.
The intelligent examination and approval device for express expense based on machine learning in the embodiment of the invention is described in detail from the angle of modularized functional entity in the above fig. 4, and the electronic equipment in the embodiment of the invention is described in detail from the angle of hardware processing.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 700 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (e.g., one or more processors) and a memory 720, and one or more storage mediums 730 (e.g., one or more mass storage devices) storing application programs 733 or data 732. Wherein memory 720 and storage medium 730 may be transitory or persistent. The program stored in the storage medium 730 may include one or more modules (not shown), each of which may include a series of instruction operations in the electronic device 700. Still further, the processor 710 may be configured to communicate with the storage medium 730 and execute a series of instruction operations in the storage medium 730 on the electronic device 700.
The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input/output interfaces 750, and/or one or more operating systems 731, such as WindowsServe, mac OS X, unix, linux, freeBSD, etc. It will be appreciated by those skilled in the art that the electronic device structure shown in fig. 5 is not limiting on the electronic device-based architecture and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
The invention also provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium, and can also be a volatile computer readable storage medium, wherein instructions are stored in the computer readable storage medium, and when the instructions run on a computer, the instructions cause the computer to execute the steps of the intelligent examination and approval method for express expense based on machine learning.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the system or apparatus and unit described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. The intelligent examination and approval method for the express expense based on the machine learning is characterized by comprising the following steps of:
acquiring historical expense application sample data, extracting key characteristic information influencing an approval result in the historical expense application sample data, performing characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
obtaining model training data, and constructing a cost application approval prediction model according to the model training data;
Acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into an online service;
acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, and calling an expense application approval prediction model to predict the new expense application characteristic values in real time to generate application passing probability information;
Acquiring the probability information of passing application and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the probability information of passing application.
2. The intelligent examination and approval method for express delivery expense based on machine learning according to claim 1, wherein the obtaining of the historical expense application sample data, extracting key feature information affecting examination and approval results in the historical expense application sample data, and performing feature processing on the key feature information to obtain a sample data set comprises:
Acquiring historical expense application sample data;
Extracting key characteristic information affecting an approval result from historical expense application sample data, wherein the key characteristic information comprises application amount, application times, applicant credit level, application form number type and receiving/sending area information;
and carrying out box division processing on the key characteristic information, and simultaneously marking target variables in the sample data set, wherein the target variables comprise application passing and application refused, and generating the sample data set according to the target variables and the processed key characteristic information.
3. The intelligent examination and approval method for express delivery expense based on machine learning according to claim 2, wherein the obtaining model training data, and constructing an expense application examination and approval prediction model according to the model training data comprises:
step one, calculating the information entropy Ent (D) of the data set D:
Ent(D)＝-Σp(x)log2p(x)
Step two, calculating the information gain g (D, A) of the feature A on the data set D:
g(D,A)＝Ent(D)-Σ(|Dj|/|D|)Ent(Dj)
And thirdly, selecting the characteristic with the maximum information gain as a node, and recursively generating a decision tree.
And repeating the second step and the third step until a preset stopping condition is reached, so as to obtain a trained expense application prediction approval model.
4. The intelligent examination and approval method for express delivery expense based on machine learning according to claim 3, wherein the obtaining model training data, and constructing an expense application examination and approval prediction model according to the model training data further comprises:
Acquiring verification set data and a cost application approval prediction model;
Evaluating the effect of a cost application approval prediction model by using verification set data, wherein evaluation indexes comprise accuracy, recall and ROC curves;
and (5) adjusting the model super-parameters to obtain an optimal model.
5. The intelligent examination and approval method for express delivery expense based on machine learning according to claim 4, wherein the obtaining the application passing probability information and the preset threshold, and realizing automatic examination and approval or reminding manual examination and approval according to the preset threshold and the application passing probability information comprises:
Acquiring application passing probability information and a preset threshold value;
Judging whether the probability information of passing application is larger than a preset threshold value or not; if yes, automatic approval is carried out; if not, sending the manual approval reminding information.
6. The intelligent approval method for express delivery expense based on machine learning according to claim 5, wherein the obtaining of the expense application approval prediction model deploys the expense application approval prediction model as an online service as follows:
and acquiring a cost application approval prediction model, and deploying the cost application approval prediction model into the REST API service.
7. Express expense intelligence approval device based on machine learning, its characterized in that includes:
The model training data acquisition module is used for acquiring historical expense application sample data, extracting key characteristic information affecting approval results in the historical expense application sample data, carrying out characteristic processing on the key characteristic information to acquire a sample data set, and taking the sample data set as model training data;
The model generation module is used for acquiring model training data and constructing a cost application approval prediction model according to the model training data;
The model deployment module is used for acquiring a cost application approval prediction model and deploying the cost application approval prediction model into an online service;
The probability information generation module is used for acquiring new expense application information, extracting new expense application characteristic values in the new expense application information, calling an expense application approval prediction model to predict the new expense application characteristic values in real time, and generating application passing probability information;
And the approval executing module is used for acquiring the application passing probability information and a preset threshold value, and realizing automatic approval or reminding manual approval according to the preset threshold value and the application passing probability information.
8. An electronic device comprising a memory and at least one processor, the memory having instructions stored therein;
The at least one processor invokes the instructions in the memory to cause the electronic device to perform the steps of the machine learning based express delivery cost intelligent approval method of any one of claims 1-7.
9. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the steps of the machine learning based intelligent approval method for courier costs of any one of claims 1-7.
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