CN112508723A - Financial risk prediction method and device based on automatic preferential modeling and electronic equipment - Google Patents
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Abstract
The invention provides a financial risk prediction method and device based on automatic preferential modeling and electronic equipment. The method comprises the following steps: presetting a model configuration file, wherein the model configuration file comprises a model generation file for automatically generating each model; receiving user input, wherein the user input comprises a financial product time node and a model generation parameter; performing matching processing, and determining a model generation file matched with the user input; generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model; selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set, and automatically selecting an optimal financial risk model according to evaluation indexes; predicting financial risk of the new user using the optimal financial risk model. The invention can generate the model more automatically, improve the working efficiency of business personnel and optimize the modeling system.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a financial risk prediction method and device based on automatic preferred modeling and electronic equipment.
Background
Risk prediction is the quantification of risk and is a critical technique for risk management. At present, risk prediction is generally carried out in a modeling mode, and in the process of establishing a model, the steps of data extraction, feature generation, feature selection, algorithm model generation, rationality evaluation and the like are mainly carried out.
In the prior art, the main purpose of financial risk prediction is how to distinguish good customers from bad customers, evaluate the risk condition of the user to reduce credit risk and maximize profits. In addition, as the source channels of the data are richer, more and more data can be used as risk characteristic variables. However, many data such as user data and other related data are used without considering the change caused by the time factor, and therefore, when the data are used for model calculation, the model calculation value is not accurate enough, and even the risk assessment for some users is not accurate enough. In addition, related service personnel usually spend a lot of time to implement business analysis and business tasks such as feature engineering, model development, evaluation, online monitoring and the like. Therefore, the method still has great improvement space in the aspects of automatic generation of the model, improvement of model precision or model optimization, data extraction and the like.
Therefore, there is a need to provide a more automated financial risk prediction method.
Disclosure of Invention
In order to generate a model more automatically, further improve the working efficiency of business personnel and further optimize a modeling system, the invention provides a financial risk prediction method based on automatic preferred modeling, which comprises the following steps: presetting a model configuration file, wherein the model configuration file comprises a model generation file for automatically generating each model; receiving user input, wherein the user input comprises a financial product time node and a model generation parameter; matching the received user input with the model configuration file, and determining a model generation file matched with the user input; generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model; selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set, and automatically selecting an optimal financial risk model according to evaluation indexes; predicting financial risk of the new user using the optimal financial risk model.
Preferably, the determining a model generation file matching the user input comprises: identifying identification parameters in the user input, matching the identification parameters with an identification information set, and determining a model generation file matched with the user input according to a first matching rule and a second matching rule, wherein the identification parameters comprise financial product time nodes, selection parameters and model generation parameters; the identification information set comprises time nodes used for representing the life cycle of the financial product, whether a model algorithm is selected, whether model parameter adjustment is carried out or not, and whether a model parameter is selected or not.
Preferably, the method further comprises the following steps: the first matching rule comprises a resource request node, a resource grant node, a resource allocation node, a resource use node, a resource quota increasing node or a resource returning node which are used for judging whether the identification parameters in the user input are resource request nodes, resource grant nodes, resource allocation nodes, resource use nodes or resource quota increasing nodes or resource returning nodes; the second matching rule comprises sample screening, a model algorithm, model parameters and a parameter adjusting and parameter adjusting method which are used for judging whether the page submitting data of the page which can be edited by the user exists.
Preferably, the method further comprises the following steps: in the process of matching processing, a user editable page is provided for a user, the user editable page comprises a plurality of parameter selectable items, and the plurality of parameter selectable items comprise a sample screening method option, a model algorithm option, a model parameter input or increase and decrease option, a model parameter adjustment option and a corresponding adjustment method option.
Preferably, the method further comprises the following steps: and monitoring the page which can be edited by the user, and updating the corresponding model generation file according to the monitored page submission data.
Preferably, the preset model configuration file includes: configuring a plurality of model generation files according to historical business data, wherein each model generation file comprises a sample screening and classifying strategy, and the sample screening and classifying strategy comprises a characteristic extraction rule and a pre-classified data set corresponding to a financial product time node; the characteristic extraction rule comprises a time parameter, an event parameter, a risk parameter and extraction according to the time parameter and/or the event parameter, wherein the time parameter comprises financial performance data in a specific time period from each financial time node, in a specific time period before each financial product time node, in a self-selected specific time period and between two adjacent financial time nodes, and the financial performance data comprises dynamic support data, overdue data, default data and return data; the event parameters comprise whether the user is a new user, whether overdue data exists, whether default data exists, whether prompt data exists and whether the user is a multi-head user.
Preferably, the preset model configuration file includes: configuring a model algorithm and a parameter determining strategy, wherein the determining strategy comprises the model algorithm, parameter adjustment suggestions and parameter adjustment methods corresponding to the number of positive and negative samples of the financial time nodes and the historical data, so that a user can select whether to adjust the model parameters or not, and select the parameter adjustment method from a parameter adjustment method set; and/or configuring a model evaluation strategy, drawing an effect curve for each financial risk model according to evaluation indexes, and comparing model effects to automatically select an optimal financial risk model, wherein the evaluation indexes comprise accuracy, ROC indexes and AUC indexes.
Preferably, the model configuration file further includes an update file for updating each model generation file.
In addition, the invention also provides a financial risk prediction device based on automatic preferred modeling, which comprises: the system comprises a setting module, a model configuration module and a model generation module, wherein the setting module is used for presetting model configuration files, and the model configuration files comprise model generation files used for automatically generating various models; a receiving module for receiving user input, the user input comprising financial product time nodes, model generation parameters; the matching processing module is used for matching the received user input with the model configuration file and determining a model generation file matched with the user input; the generating module generates a financial risk model set according to the matched model generating file, and automatically trains each financial risk model; the evaluation module is used for selecting a corresponding test data set, carrying out effect evaluation on each model in the financial risk model set and automatically selecting an optimal financial risk model according to an evaluation index; a prediction module to predict financial risk of a new user using the optimal financial risk model.
Preferably, the system further comprises an identification module, wherein the identification module is used for identifying an identification parameter in the user input, matching the identification parameter with an identification information set, and determining a model generation file matched with the user input according to a first matching rule and a second matching rule, wherein the identification parameter comprises a financial product time node, a selection parameter and a model generation parameter; the identification information set comprises time nodes used for representing the life cycle of the financial product, whether a model algorithm is selected, whether model parameter adjustment is carried out or not, and whether a model parameter is selected or not.
Preferably, the method further comprises the following steps: the first matching rule comprises a resource request node, a resource grant node, a resource allocation node, a resource use node, a resource quota increasing node or a resource returning node which are used for judging whether the identification parameters in the user input are resource request nodes, resource grant nodes, resource allocation nodes, resource use nodes or resource quota increasing nodes or resource returning nodes; the second matching rule comprises sample screening, a model algorithm, model parameters and a parameter adjusting and parameter adjusting method which are used for judging whether the page submitting data of the page which can be edited by the user exists.
Preferably, the method further comprises the following steps: in the process of matching processing, a user editable page is provided for a user, the user editable page comprises a plurality of parameter selectable items, and the plurality of parameter selectable items comprise a sample screening method option, a model algorithm option, a model parameter input or increase and decrease option, a model parameter adjustment option and a corresponding adjustment method option.
Preferably, the system further comprises a monitoring module, wherein the monitoring module is used for monitoring the page which can be edited by the user, and updating the corresponding model generation file according to the monitored page submission data.
Preferably, the method further comprises the following steps: configuring a plurality of model generation files according to historical business data, wherein each model generation file comprises a sample screening and classifying strategy, and the sample screening and classifying strategy comprises a characteristic extraction rule and a pre-classified data set corresponding to a financial product time node; the characteristic extraction rule comprises a time parameter, an event parameter, a risk parameter and extraction according to the time parameter and/or the event parameter, wherein the time parameter comprises financial performance data in a specific time period from each financial time node, in a specific time period before each financial product time node, in a self-selected specific time period and between two adjacent financial time nodes, and the financial performance data comprises dynamic support data, overdue data, default data and return data; the event parameters comprise whether the user is a new user, whether overdue data exists, whether default data exists, whether prompt data exists and whether the user is a multi-head user.
Preferably, the method further comprises the following steps: configuring a model algorithm and a parameter determining strategy, wherein the determining strategy comprises the model algorithm, parameter adjustment suggestions and parameter adjustment methods corresponding to the number of positive and negative samples of the financial time nodes and the historical data, so that a user can select whether to adjust the model parameters or not, and select the parameter adjustment method from a parameter adjustment method set; and/or configuring a model evaluation strategy, drawing an effect curve for each financial risk model according to evaluation indexes, and comparing model effects to automatically select an optimal financial risk model, wherein the evaluation indexes comprise accuracy, ROC indexes and AUC indexes.
Preferably, the model configuration file further includes an update file for updating each model generation file.
In addition, the present invention also provides an electronic device, wherein the electronic device includes: a processor; and a memory storing computer executable instructions that, when executed, cause the processor to perform the automated preferential modeling based financial risk prediction method of the present invention.
Furthermore, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the automated preferential modeling-based financial risk prediction method of the present invention.
Advantageous effects
Compared with the prior art, the modeling method has the advantages that the modeling process is more automatic and standardized, the optimal model can be efficiently produced, the modeling process can be further simplified, and the modeling system can be further optimized; the corresponding data set is automatically selected, and an automatic training model is realized; through the user input of related business personnel, the optimal financial risk model is automatically selected, so that the time for the related business personnel to complete related business tasks is greatly reduced, and the working efficiency of the business personnel is improved; the optimal financial risk model can be provided for different users, and the risk condition of the user can be predicted more accurately and automatically.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
Fig. 1 is a flowchart of an example of a financial risk prediction method based on automated preferential modeling according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of another example of the financial risk prediction method based on the automated preferential modeling according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of another example of the financial risk prediction method based on the automated preferential modeling according to embodiment 1 of the present invention.
Fig. 4 is a schematic diagram of an example of the financial risk prediction apparatus based on the automated preferential modeling according to embodiment 2 of the present invention.
Fig. 5 is a schematic diagram of another example of the financial risk prediction apparatus based on the automated preferential modeling according to embodiment 2 of the present invention.
Fig. 6 is a schematic diagram of still another example of the financial risk prediction apparatus based on the automated preferential modeling according to embodiment 2 of the present invention.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention.
Fig. 8 is a block diagram of an exemplary embodiment of a computer-readable medium according to the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
In view of the above problems, the present invention provides a financial risk prediction method based on automatic preferential modeling. The method can ensure that the modeling process is more automatic and standardized, can efficiently produce the optimal model, and can further optimize the modeling system, thereby further improving the working efficiency of business personnel. The specific procedures of the method of the present invention will be described in detail below.
It should be noted that, in the present invention, a resource refers to any available substance, information, and time, and an information resource includes a computing resource and various types of data resources. The data resources include various private data in various domains. The innovation of the invention is how to use the information interaction technology between the server and the client to make the resource allocation process more automatic, efficient and reduce the labor cost. Therefore, the method can be applied to risk prediction in the distribution and return of various resources, not only in financial resources, but also in physical goods, water, electricity, meaningful data and the like. However, for convenience, the resource allocation is described as being implemented by taking financial data resources as an example, but those skilled in the art will understand that the invention can also be used for risk prediction of other resources.
Example 1
Hereinafter, an embodiment of the financial risk prediction method based on the automated preferential modeling according to the present invention will be described with reference to fig. 1 to 3.
FIG. 1 is a flow chart of an example of a financial risk prediction method based on automated preferential modeling of the present invention. As shown in fig. 1, the method includes the following steps.
Step S101, presetting model configuration files, wherein the model configuration files comprise model generation files used for automatically generating each model.
Step S102, receiving user input, wherein the user input comprises financial product time nodes and model generation parameters.
Step S103, matching the received user input with the model configuration file, and determining a model generation file matched with the user input.
And step S104, generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model.
And S105, selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set, and automatically selecting an optimal financial risk model according to evaluation indexes.
And step S106, predicting the financial risk of the new user by using the optimal financial risk model.
In this example, in an application scenario where a user makes resource usage of a financial services product or a financial management product, a model profile is preset according to business personnel input, for example, by an automated wind-controlled modeling system.
Specifically, the automatic wind control modeling system is connected with a plurality of clients, and different types of business personnel use the automatic wind control modeling system to perform file configuration, updating and other management or data processing and the like according to authority or business tasks.
For example, the system comprises a plurality of types of service personnel, wherein the first type of service personnel are users who perform file configuration and management according to service requirements and the like; the second class of business personnel are users who perform model generation applications according to preset files. However, the present invention is not limited thereto, and the above description is only given as a preferred example, and is not to be construed as limiting the present invention. The following will be described in more detail with reference to specific steps.
First, in step S101, a model profile including a model generation file for automatically generating each model is preset.
Specifically, the first class of business personnel sets a model configuration file according to historical business data, wherein the model configuration file comprises a plurality of model generation files. For example, a plurality of model generation files are configured on a visual interface provided by an automatic wind control modeling system based on a first class of business personnel, so as to be used for automatically generating an optimal model.
Further, each model generation file includes determining a sample screening and classification policy that includes feature extraction rules, pre-classified data sets corresponding to financial product time nodes.
Preferably, the feature extraction rule includes time parameter, event parameter, risk parameter, and parameter extraction according to the time parameter, event parameter, risk parameter, and the like.
For example, the time parameters include financial performance data between two adjacent financial time nodes, including move payment data, overdue data, default data, and return data, within a certain time period from each financial time node, within a certain time period before each financial product time node, within a self-selected certain time period; the event parameters comprise whether the user is a new user, whether overdue data exists, whether default data exists, whether prompt data exists and whether the user is a multi-head user.
Specifically, in performing the pre-classification of the data set, for example, time parameters of three, five, seven, fifteen, thirty, etc. days after the past are defined, and resource return times of three, six, nine, etc. are defined.
In this example, for historical service data of different service lines, sample screening is performed according to the feature extraction rule, sample tags are defined, and sample pre-classification is performed. From this, sample screening and classification strategies can be determined.
Further, according to the sample data after the sample screening and the sample data after the classification, a training data set, a testing data set and the like corresponding to each model are established.
For the training data set, for example, based on time nodes such as a resource quota granting node, a resource using node, and a resource returning node, sample data is segmented, for example, sample data between the resource quota granting nodes, sample data between the resource quota granting node and the resource using node, and the number of times of resource use is greater than a certain number of times and reaches the sample data of a certain number of times of resource returning.
For example, according to the segmented sample data in each segment, positive samples and negative samples are respectively defined, and the labels are 0 and 1, wherein 1 represents a sample of the user, which is more than or equal to Y, and 0 represents a sample of the user, which has a resource return probability of less than Y, and the values of Y in the segments are different. Generally, the higher the resource return probability of the user, the better the loan-recovery principal, the better the efficiency of the use of the funds, the lower the risk level of the property, and vice versa.
It should be noted that the above description is only used as an example, and is not to be construed as limiting the present invention, and in other examples, overdue probability (or default probability) may be used to define positive and negative samples.
For another example, taking the resource usage node as an example, the training data set includes time characteristic data of historical users related to the resource usage node, event characteristic data, user resource usage behavior data within a certain time period, a probability of overdue (breach probability or resource return probability), wherein the certain time period includes within a certain time period from the resource quota granting node, within a time period from the resource quota granting node to the occurrence time of the first resource usage behavior, within a certain time period from the occurrence time of the first resource usage behavior, and the like.
Preferably, the method further comprises a determination strategy for configuring the model algorithm and the parameters.
Specifically, the determination strategy comprises a model algorithm, a parameter adjustment suggestion and a parameter adjustment method corresponding to the number of positive and negative samples of the financial time node and the historical data, so that a user can select whether to perform model parameter adjustment or not, and the parameter adjustment method is selected from a parameter adjustment method set.
In another example, the corresponding model algorithm is selected according to the number of positive and negative samples of the sample data, the number of labeled sample data, the sample data density, and the like. For example, for sample data having a number of labeled sample data smaller than a minimum specific number, modeling calculation is performed using a feature learning algorithm such as CNN, RNN, or the like. As another example, for sample data without tags, an unsupervised self-learning algorithm is used. As another example, for the case where the number of data for positive or negative samples is greater than the minimum specified number and less than the specified number, oversampling is performed to create a data set for model training. In other examples, one algorithm or a combination of two or more algorithms of logistic regression, random forest, GBDT, XGBoost may also be used.
In another example, the method further comprises configuring a model evaluation strategy, drawing an effect curve for each financial risk model according to evaluation indexes, wherein the evaluation indexes comprise accuracy, ROC indexes and AUC indexes, and comparing the model effects to automatically select the optimal financial risk model.
Preferably, multidimensional model parameters are input, for example using Cartesian product calculations, to generate a plurality of models. Thus, a large variety of profiles are obtained.
In yet another example, the model configuration file further includes an update file for updating each model generation file. For example, each model generation file is periodically updated according to the parameter change or the data amount change in the specific application and the evaluation result.
Therefore, through presetting the configuration files, a plurality of automatic processes can be realized, an optimized financial risk model can be automatically selected, the modeling process can be simplified, the time for business personnel to model can be reduced, and the working efficiency of the business personnel can be improved.
The above description is only given as a preferred example, and the present invention is not limited thereto.
Next, in step S102, user input is received, the user input including financial product time nodes, model generation parameters.
In this example, user input is received for a user, wherein the user is a second type of business person.
For example, during the process of using the automatic wind-controlled modeling system by the user a (i.e. the second type of business personnel), user input can be carried out on the use interface to complete the modeling business task.
In this example, the user input of user a includes financial product time nodes, model generation parameters, and the like.
Specifically, the financial product time node includes a resource request node, a resource grant node, a resource allocation node, a resource use node, a resource limit increase and decrease node, a resource return node, and the like, which are related to a financial service product or a financial product.
In this example, a financial time node is used as an identification parameter for the matching process of the model profile.
Further, the model generation parameters include necessary influencing factors or index parameters for generating each model, such as minimum variance loss, sample data distribution parameters, and the like.
The above description is only given as a preferred example, and the present invention is not limited thereto.
Next, in step S103, a matching process is performed using the received user input and the model configuration file, and a model generation file matching the user input is determined.
As shown in fig. 2, a step S201 of identifying the identification parameter in the user input is further included.
In step S201, an identification parameter in the user input is identified.
Specifically, identification parameters in user input of a current user are identified, and the identification parameters are matched with an identification information set, wherein the identification information set comprises time nodes used for representing the life cycle of a financial product, whether a model algorithm is selected, whether model parameter adjustment is performed, and whether model parameters are selected.
Preferably, the method further comprises determining a matching rule for the matching process.
For example, the first matching rule and the second matching rule are automatically determined based on the identification parameters and the model-generated optional parameters. However, the present invention is not limited thereto, and the above description is only given as a preferred example, and is not to be construed as limiting the present invention.
Specifically, the first matching rule includes determining whether the identification parameter in the user input includes a resource request node, a resource grant node, a resource allocation node, a resource usage node, a resource quota increase node, or a resource return node in the financial time nodes. And the second matching rule comprises sample screening, a model algorithm, model parameters and a parameter adjusting and parameter adjusting method which are used for judging whether the page submitting data of the page which can be edited by the user exists.
Further, according to the first matching rule and the second matching rule, determining a model generation file matched with the user input, wherein the identification parameters comprise a financial product time node, a selection parameter and a model generation parameter.
Furthermore, the identified identification parameters are automatically matched with the model configuration file, so that a corresponding model generation file can be obtained.
Therefore, according to the input of the user, the corresponding model generation file can be automatically matched, so that the automation of the modeling process can be realized, and the modeling flow can be further simplified.
The above description is only given as a preferred example, and the present invention is not limited thereto.
Next, in step S104, a financial risk model set is generated from the matched model generation file, and each financial risk model is automatically trained.
For example, a model generation document 1 is matched, and a plurality of corresponding models (i.e., a financial risk model set) are generated according to the model generation document 1.
Specifically, the training data set in step S101 is selected, and each financial risk model is automatically trained.
As shown in fig. 3, a step S301 of providing a user editable page to the user in the process of the matching process is further included.
In step S301, during the matching process (i.e. during the matching process between the identification parameter of the user and the identification information set), a user-editable page is provided to the user (i.e. the second-class service person).
Specifically, the user editable page includes a plurality of parameter selectable items, which include a sample screening method option, a model algorithm option, a model parameter input or increase/decrease option, a model parameter adjustment option, and a corresponding adjustment method option.
Further, the user a may select one or more items of data from the above-described plurality of parameter selectable items on the user-editable page, and submit the page data through the ok button.
Preferably, the user editable page is monitored, and data is submitted according to the monitored page to update the corresponding model generation file.
In an example, when the page data selected by the user with the model parameter adjustment option is monitored, the model parameter adjustment is performed by using the model evaluation policy configured in step S101.
Therefore, automatic selection of corresponding data sets is achieved, and automatic training of the model is achieved.
The above description is only given as a preferred example, and the present invention is not limited thereto. In other examples, the user editable page may also be provided to the user (i.e., the second class of business personnel) at the time of the matching process or after the model generation file is determined.
Next, in step S105, a corresponding test data set is selected, effect evaluation is performed on each model in the financial risk model set, and an optimal financial risk model is automatically selected according to an evaluation index.
In this example, automatically selecting the optimal model from the plurality of models is also included.
Specifically, a plurality of models are generated based on the matched model generation file, and a corresponding test data set is automatically selected according to each model.
Further, according to the model evaluation strategy configured in step S101, at least two evaluation indexes of the accuracy, the ROC index, and the AUC index are selected, an effect curve is drawn for each model, and the model effects are compared to automatically select an optimal financial risk model.
In another example, the method further comprises recording the user input, the optimal financial risk model corresponding to the user input, and storing the user input, the optimal financial risk model corresponding to the user input, to a model database, wherein the model database uses financial product time nodes as indexes and the model database is used for optimal model matching according to model input features.
Therefore, the optimal financial risk model is automatically selected through the user input of the related business personnel, the time for the related business personnel to complete the related business tasks is greatly reduced, and the working efficiency is improved.
It should be noted that the above description is only given by way of example, and the present invention is not limited thereto.
Next, in step S106, the financial risk of the new user is predicted using the optimal financial risk model.
Specifically, user data of the current user is acquired, and whether the user is a new user is judged.
Further, for example, the second type of service personnel extracts valid feature data from the user data of the new user.
In this example, the valid feature data is the time feature data and the risk feature data of the new user, and the valid feature data is used as an input feature for the model.
Note that the extraction method of the valid feature data is the same as that of step S101, and therefore, the description thereof is omitted.
In another example, the second business person enters the user data of the new user into the corresponding page, for example, and returns the optimal financial risk model and its calculated values corresponding to the user characteristics directly.
In another example, for example, the first-class service personnel inputs the user data of the new user into an effective feature extraction page, automatically extracts effective features, uses the effective features as model input features, and performs optimal model matching to automatically select an optimal financial risk model corresponding to the effective features.
Further, the selected optimal financial risk model is used for calculation to obtain a financial predicted value, in this example, the financial risk predicted value is a predicted value representing a quantitative user risk condition, and the predicted value is a value between 0 and 1. For example, the financial risk prediction value is a default probability, an overdue probability, or a resource return probability.
Therefore, the optimal financial risk model can be provided for different users, and the risk condition of the user can be predicted more accurately and automatically.
The above description is only given as a preferred example, and the present invention is not limited thereto.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Compared with the prior art, the modeling method has the advantages that the modeling process is more automatic and standardized, the optimal model can be efficiently produced, the modeling process can be further simplified, and the modeling system can be further optimized; the corresponding data set is automatically selected, and an automatic training model is realized; through the user input of related business personnel, the optimal financial risk model is automatically selected, so that the time for the related business personnel to complete related business tasks is greatly reduced, and the working efficiency of the business personnel is improved; the optimal financial risk model can be provided for different users, and the risk condition of the user can be predicted more accurately and automatically.
Example 2
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Referring to fig. 4, 5 and 6, the present invention further provides a financial risk prediction apparatus 400 based on automated preferential modeling, the financial risk prediction apparatus 400 including: a setting module 401, configured to preset model configuration files, where the model configuration files include model generation files for automatically generating models; a receiving module 402 for receiving user input, the user input comprising a financial product time node, model generation parameters; a matching processing module 403, configured to perform matching processing using the received user input and the model configuration file, and determine a model generation file matching the user input; a generating module 404, which generates a financial risk model set according to the matched model generating file, and automatically trains each financial risk model; an evaluation module 405, configured to select a corresponding test data set, perform effect evaluation on each model in the financial risk model set, and automatically select an optimal financial risk model according to an evaluation index; a prediction module 406 for predicting financial risk of the new user using the optimal financial risk model.
As shown in fig. 5, the system further includes an identifying module 501, where the identifying module 501 is configured to identify an identification parameter in the user input, perform matching processing on the identification parameter and an identification information set, and determine a model generation file matched with the user input according to a first matching rule and a second matching rule, where the identification parameter includes a financial product time node, a selection parameter, and a model generation parameter; the identification information set comprises time nodes used for representing the life cycle of the financial product, whether a model algorithm is selected, whether model parameter adjustment is carried out or not, and whether a model parameter is selected or not.
Preferably, the method further comprises the following steps: the first matching rule comprises a resource request node, a resource grant node, a resource allocation node, a resource use node, a resource quota increasing node or a resource returning node which are used for judging whether the identification parameters in the user input are resource request nodes, resource grant nodes, resource allocation nodes, resource use nodes or resource quota increasing nodes or resource returning nodes; the second matching rule comprises sample screening, a model algorithm, model parameters and a parameter adjusting and parameter adjusting method which are used for judging whether the page submitting data of the page which can be edited by the user exists.
Preferably, the method further comprises the following steps: in the process of matching processing, a user editable page is provided for a user, the user editable page comprises a plurality of parameter selectable items, and the plurality of parameter selectable items comprise a sample screening method option, a model algorithm option, a model parameter input or increase and decrease option, a model parameter adjustment option and a corresponding adjustment method option.
As shown in fig. 6, the system further includes a monitoring module 601, where the monitoring module 601 is configured to monitor the user-editable page, and update the corresponding model generation file according to the monitored page submission data.
Preferably, the method further comprises the following steps: configuring a plurality of model generation files according to historical business data, wherein each model generation file comprises a sample screening and classifying strategy, and the sample screening and classifying strategy comprises a characteristic extraction rule and a pre-classified data set corresponding to a financial product time node; the characteristic extraction rule comprises a time parameter, an event parameter, a risk parameter and extraction according to the time parameter and/or the event parameter, wherein the time parameter comprises financial performance data in a specific time period from each financial time node, in a specific time period before each financial product time node, in a self-selected specific time period and between two adjacent financial time nodes, and the financial performance data comprises dynamic support data, overdue data, default data and return data; the event parameters comprise whether the user is a new user, whether overdue data exists, whether default data exists, whether prompt data exists and whether the user is a multi-head user.
Preferably, the method further comprises the following steps: configuring a model algorithm and a parameter determining strategy, wherein the determining strategy comprises the model algorithm, parameter adjustment suggestions and parameter adjustment methods corresponding to the number of positive and negative samples of the financial time nodes and the historical data, so that a user can select whether to adjust the model parameters or not, and select the parameter adjustment method from a parameter adjustment method set; and/or configuring a model evaluation strategy, drawing an effect curve for each financial risk model according to evaluation indexes, and comparing model effects to automatically select an optimal financial risk model, wherein the evaluation indexes comprise accuracy, ROC indexes and AUC indexes.
Preferably, the model configuration file further includes an update file for updating each model generation file.
In embodiment 2, the same portions as those in embodiment 1 are not described.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Compared with the prior art, the modeling method has the advantages that the modeling process is more automatic and standardized, the optimal model can be efficiently produced, the modeling process can be further simplified, and the modeling system can be further optimized; the corresponding data set is automatically selected, and an automatic training model is realized; through the user input of related business personnel, the optimal financial risk model is automatically selected, so that the time for the related business personnel to complete related business tasks is greatly reduced, and the working efficiency of the business personnel is improved; the optimal financial risk model can be provided for different users, and the risk condition of the user can be predicted more accurately and automatically.
Example 3
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. An electronic apparatus 200 according to this embodiment of the present invention is described below with reference to fig. 7. The electronic device 200 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform steps according to various exemplary embodiments of the present invention described in the processing method section of the electronic device described above in this specification. For example, the processing unit 210 may perform the steps as shown in fig. 1.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to carry out the above-described methods of the invention.
As shown in fig. 8, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. A financial risk prediction method based on automatic preferential modeling is characterized by comprising the following steps:
presetting a model configuration file, wherein the model configuration file comprises a model generation file for automatically generating each model;
receiving user input, wherein the user input comprises a financial product time node and a model generation parameter;
matching the received user input with the model configuration file, and determining a model generation file matched with the user input;
generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model;
selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set, and automatically selecting an optimal financial risk model according to evaluation indexes;
predicting financial risk of the new user using the optimal financial risk model.
2. The automated preferential modeling-based financial risk prediction method of claim 1, wherein the determining a model generation profile that matches the user input comprises:
identifying identification parameters in the user input, matching the identification parameters with an identification information set, and determining a model generation file matched with the user input according to a first matching rule and a second matching rule, wherein,
the identification parameters comprise financial product time nodes, selection parameters and model generation parameters;
the identification information set comprises time nodes used for representing the life cycle of the financial product, whether a model algorithm is selected, whether model parameter adjustment is carried out or not, and whether a model parameter is selected or not.
3. The automated preferential modeling-based financial risk prediction method of claim 2 further comprising:
the first matching rule comprises a resource request node, a resource grant node, a resource allocation node, a resource use node, a resource quota increasing node or a resource returning node which are used for judging whether the identification parameters in the user input are resource request nodes, resource grant nodes, resource allocation nodes, resource use nodes or resource quota increasing nodes or resource returning nodes;
the second matching rule comprises sample screening, a model algorithm, model parameters and a parameter adjusting and parameter adjusting method which are used for judging whether the page submitting data of the page which can be edited by the user exists.
4. The automated preferential modeling-based financial risk prediction method according to claim 1 or 3, further comprising:
in the process of matching processing, a user editable page is provided for a user, the user editable page comprises a plurality of parameter selectable items, and the plurality of parameter selectable items comprise a sample screening method option, a model algorithm option, a model parameter input or increase and decrease option, a model parameter adjustment option and a corresponding adjustment method option.
5. The automated preferential modeling-based financial risk prediction method of claim 4 further comprising:
and monitoring the page which can be edited by the user, and updating the corresponding model generation file according to the monitored page submission data.
6. The automated preferential modeling-based financial risk prediction method of claim 1, wherein the pre-set model profile includes:
configuring a plurality of model generation files according to historical business data, wherein each model generation file comprises a sample screening and classifying strategy, and the sample screening and classifying strategy comprises a characteristic extraction rule and a pre-classified data set corresponding to a financial product time node;
the characteristic extraction rule comprises a time parameter, an event parameter, a risk parameter and extraction according to the time parameter and/or the event parameter, wherein the time parameter comprises financial performance data in a specific time period from each financial time node, in a specific time period before each financial product time node, in a self-selected specific time period and between two adjacent financial time nodes, and the financial performance data comprises dynamic support data, overdue data, default data and return data; the event parameters comprise whether the user is a new user, whether overdue data exists, whether default data exists, whether prompt data exists and whether the user is a multi-head user.
7. The automated preferential modeling-based financial risk prediction method according to claim 1 or 6, wherein the preset model profile includes:
configuring a model algorithm and a parameter determining strategy, wherein the determining strategy comprises the model algorithm, parameter adjustment suggestions and parameter adjustment methods corresponding to the number of positive and negative samples of the financial time nodes and the historical data, so that a user can select whether to adjust the model parameters or not, and select the parameter adjustment method from a parameter adjustment method set; and/or
And configuring a model evaluation strategy, drawing an effect curve for each financial risk model according to evaluation indexes, and comparing model effects to automatically select an optimal financial risk model, wherein the evaluation indexes comprise accuracy, ROC indexes and AUC indexes.
8. An automated preferential modeling-based financial risk prediction apparatus, comprising:
the system comprises a setting module, a model configuration module and a model generation module, wherein the setting module is used for presetting model configuration files, and the model configuration files comprise model generation files used for automatically generating various models;
a receiving module for receiving user input, the user input comprising financial product time nodes, model generation parameters;
the matching processing module is used for matching the received user input with the model configuration file and determining a model generation file matched with the user input;
the generating module generates a financial risk model set according to the matched model generating file, and automatically trains each financial risk model;
the evaluation module is used for selecting a corresponding test data set, carrying out effect evaluation on each model in the financial risk model set and automatically selecting an optimal financial risk model according to an evaluation index;
a prediction module to predict financial risk of a new user using the optimal financial risk model.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the automated preferential modeling based financial risk prediction method of any one of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the automated preferential modeling-based financial risk prediction method of any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112884092A (en) * | 2021-04-28 | 2021-06-01 | 深圳索信达数据技术有限公司 | AI model generation method, electronic device, and storage medium |
| CN113297480A (en) * | 2021-04-29 | 2021-08-24 | 上海淇玥信息技术有限公司 | Customized information sending method and device and electronic equipment |
| CN113487427A (en) * | 2021-04-20 | 2021-10-08 | 微梦创科网络科技（中国）有限公司 | Transaction risk identification method, device and system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060190310A1 (en) * | 2005-02-24 | 2006-08-24 | Yasu Technologies Pvt. Ltd. | System and method for designing effective business policies via business rules analysis |
| CN106663224A (en) * | 2014-06-30 | 2017-05-10 | 亚马逊科技公司 | Interactive interfaces for machine learning model evaluations |
| CN110046706A (en) * | 2019-04-18 | 2019-07-23 | 腾讯科技（深圳）有限公司 | Model generating method, device and server |
| CN110210624A (en) * | 2018-07-05 | 2019-09-06 | 第四范式（北京）技术有限公司 | Execute method, apparatus, equipment and the storage medium of machine-learning process |
| US20190295011A1 (en) * | 2018-03-20 | 2019-09-26 | Xingjian Duan | Distributed computer framework for data analysis, risk management, and automated compliance |
| CN111639798A (en) * | 2020-05-26 | 2020-09-08 | 华青融天(北京)软件股份有限公司 | Intelligent prediction model selection method and device |
| WO2020249125A1 (en) * | 2019-06-14 | 2020-12-17 | 第四范式（北京）技术有限公司 | Method and system for automatically training machine learning model |
| CN112270545A (en) * | 2020-10-27 | 2021-01-26 | 上海淇馥信息技术有限公司 | Financial risk prediction method and device based on migration sample screening and electronic equipment |
| CN112270547A (en) * | 2020-10-27 | 2021-01-26 | 上海淇馥信息技术有限公司 | Financial risk assessment method and device based on feature construction and electronic equipment |
-
2021
- 2021-02-05 CN CN202110166439.1A patent/CN112508723B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060190310A1 (en) * | 2005-02-24 | 2006-08-24 | Yasu Technologies Pvt. Ltd. | System and method for designing effective business policies via business rules analysis |
| CN106663224A (en) * | 2014-06-30 | 2017-05-10 | 亚马逊科技公司 | Interactive interfaces for machine learning model evaluations |
| US20190295011A1 (en) * | 2018-03-20 | 2019-09-26 | Xingjian Duan | Distributed computer framework for data analysis, risk management, and automated compliance |
| CN110210624A (en) * | 2018-07-05 | 2019-09-06 | 第四范式（北京）技术有限公司 | Execute method, apparatus, equipment and the storage medium of machine-learning process |
| CN110046706A (en) * | 2019-04-18 | 2019-07-23 | 腾讯科技（深圳）有限公司 | Model generating method, device and server |
| WO2020249125A1 (en) * | 2019-06-14 | 2020-12-17 | 第四范式（北京）技术有限公司 | Method and system for automatically training machine learning model |
| CN111639798A (en) * | 2020-05-26 | 2020-09-08 | 华青融天(北京)软件股份有限公司 | Intelligent prediction model selection method and device |
| CN112270545A (en) * | 2020-10-27 | 2021-01-26 | 上海淇馥信息技术有限公司 | Financial risk prediction method and device based on migration sample screening and electronic equipment |
| CN112270547A (en) * | 2020-10-27 | 2021-01-26 | 上海淇馥信息技术有限公司 | Financial risk assessment method and device based on feature construction and electronic equipment |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487427A (en) * | 2021-04-20 | 2021-10-08 | 微梦创科网络科技（中国）有限公司 | Transaction risk identification method, device and system |
| CN112884092A (en) * | 2021-04-28 | 2021-06-01 | 深圳索信达数据技术有限公司 | AI model generation method, electronic device, and storage medium |
| CN112884092B (en) * | 2021-04-28 | 2021-11-02 | 深圳索信达数据技术有限公司 | AI model generation method, electronic device, and storage medium |
| CN113297480A (en) * | 2021-04-29 | 2021-08-24 | 上海淇玥信息技术有限公司 | Customized information sending method and device and electronic equipment |
| CN113297480B (en) * | 2021-04-29 | 2023-03-14 | 上海淇玥信息技术有限公司 | Customized information sending method and device and electronic equipment |
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