CN110599335A - User financial risk assessment method and device based on multiple models - Google Patents
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Abstract
The invention discloses a user financial risk assessment method and device based on multiple models, electronic equipment and a computer readable medium. The method comprises the following steps: the method comprises the steps of obtaining user data, inputting the user data into a plurality of trained evaluation models, determining risk indexes of the user respectively through each evaluation model, calculating KS values of each evaluation model, and determining final risk indexes of the user for financial services based on the risk indexes and the KS values respectively corresponding to the evaluation models. The final risk index of the user can be comprehensively evaluated according to the evaluation results of the plurality of evaluation models and the KS value corresponding to each evaluation model, the plurality of evaluation models can evaluate the qualification of the user from a plurality of different angles, and the accuracy of the financial risk evaluation of the user is improved.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a user financial risk assessment method and device based on multiple models, electronic equipment and a computer readable storage medium.
Background
With the rapid development of network information technology, internet finance is also more and more widely applied to a plurality of scenes such as work, life and the like. Many banks or financial institutions for credit service emerge in the financial market, and credit products set under various flags are full of line and full of eyes so as to meet the requirements of different customers.
Currently, each credit agency typically needs a risk assessment for a user before providing credit services to the user to determine whether the user qualifies for credit. For example, credit agencies typically use corresponding assessment models to determine a risk index for a user.
However, in the process of implementing the inventive concept, the inventor finds that in the prior art, a single model is usually used to evaluate the risk index of the user, the accuracy is low, and a misjudgment phenomenon often occurs, so that many users with application qualification are rejected, the customer loss of a credit agency is caused, and the experience of the application user is not good.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, a single model is used for evaluating the risk index of a user, the accuracy is low, the phenomenon of misjudgment often occurs, so that a plurality of users with application qualification are rejected, the customer loss of a credit agency is caused, and the experience of the application user is poor.
In order to solve the above technical problem, a first aspect of the present invention provides a method for evaluating a financial risk of a user based on multiple models, including: the method comprises the steps of obtaining user data, inputting the user data into a plurality of trained evaluation models, determining risk indexes of users respectively through the evaluation models, calculating KS values of the evaluation models, and determining final risk indexes of the users for financial services based on the risk indexes and the KS values respectively corresponding to the evaluation models.
According to a preferred embodiment of the present invention, the determining the final risk index of the user for the financial transaction based on the risk index and the KS value respectively corresponding to each evaluation model includes: and calculating the weight of each evaluation model according to the KS value of each evaluation model, wherein the weight is in direct proportion to the KS value, and determining the final risk index of the user for the financial business based on the risk index and the weight respectively corresponding to each evaluation model.
According to a preferred embodiment of the present invention, the plurality of evaluation models are the same or different, and the training data of the plurality of evaluation models are different.
According to a preferred embodiment of the present invention, the plurality of evaluation models are different, and the training data of the plurality of evaluation models are the same or different, wherein the plurality of evaluation models are different, and the plurality of evaluation models include different model algorithms of the plurality of evaluation models or different input features of the plurality of evaluation models.
According to a preferred embodiment of the present invention, the final risk index refers to a default probability of the user, where the default probability is:
wherein p isiIs the probability of violation, ω, predicted by the ith modeliIs the weight of the ith model.
According to a preferred embodiment of the present invention, the weights are calculated as follows:
wherein, KSiIs the KS value for the ith model.
In order to solve the technical problem, the second aspect of the present invention provides a user financial risk assessment apparatus based on multiple models, which includes an obtaining module, an input module, a calculating module, and a determining module. The acquisition module is used for acquiring user data. The input module is used for inputting the user data into a plurality of trained evaluation models, and each evaluation model respectively determines the risk index of the user. The calculation module is used for calculating the KS value of each evaluation model. And the determining module is used for determining the final risk index of the user for the financial business based on the risk index and the KS value respectively corresponding to each evaluation model.
According to a preferred embodiment of the present invention, the determining the final risk index of the user for the financial transaction based on the risk index and the KS value respectively corresponding to each evaluation model includes: and calculating the weight of each evaluation model according to the KS value of each evaluation model, wherein the weight is in direct proportion to the KS value, and determining the final risk index of the user for the financial business based on the risk index and the weight respectively corresponding to each evaluation model.
According to a preferred embodiment of the present invention, the plurality of evaluation models are the same or different, and the training data of the plurality of evaluation models are different.
According to a preferred embodiment of the present invention, the plurality of evaluation models are different, and the training data of the plurality of evaluation models are the same or different, wherein the plurality of evaluation models are different, and the plurality of evaluation models include different model algorithms of the plurality of evaluation models or different input features of the plurality of evaluation models.
According to a preferred embodiment of the present invention, the final risk index refers to a default probability of the user, where the default probability is:
wherein p isiIs the probability of violation, ω, predicted by the ith modeliIs the weight of the ith model.
According to a preferred embodiment of the present invention, the weights are calculated as follows:
wherein, KSiIs the KS value for the ith model.
In order to solve the above technical problem, a third aspect of the present invention proposes an electronic device comprising a processor and a memory storing computer-executable instructions that, when executed, cause the processor to perform the above method.
In order to solve the above technical problem, a fourth aspect of the present invention proposes a computer-readable storage medium storing one or more programs which, when executed by a processor, implement the above-mentioned method.
According to the evaluation method and the evaluation system, the final risk index of the user can be comprehensively evaluated according to the evaluation results of the plurality of evaluation models and the KS value corresponding to each evaluation model, the plurality of evaluation models can evaluate the qualification of the user from a plurality of different angles, the accuracy of the financial risk evaluation of the user is improved, and the inconvenience caused by misjudgment on the user and the problem of customer loss caused by credit institutions are at least partially avoided.
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 step.
Fig. 1A and 1B schematically illustrate application scenarios of the multi-model-based user financial risk assessment method and apparatus according to an embodiment of the present invention.
FIG. 2 schematically shows a flow diagram of a multi-model based user financial risk assessment method according to an embodiment of the invention.
FIG. 3 schematically shows a block diagram of a multi-model based user financial risk assessment apparatus according to an embodiment of the present invention.
Fig. 4 schematically shows a block diagram of an electronic device according to an embodiment of the invention.
FIG. 5 schematically shows a schematic diagram of a computer-readable medium according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention may be embodied in many specific 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 structures, properties, effects or other characteristics described in a certain embodiment may be combined in any suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features.
The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities 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.
The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only 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. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.
The embodiment of the invention provides a user financial risk assessment method based on multiple models, which comprises the following steps: the method comprises the steps of obtaining user data, inputting the user data into a plurality of trained evaluation models, determining the risk index of the user by each evaluation model, calculating the KS value of each evaluation model, and determining the final risk index of the user for financial services based on the risk index and the KS value corresponding to each evaluation model.
Fig. 1A and 1B schematically illustrate application scenarios of the multi-model-based user financial risk assessment method and apparatus according to an embodiment of the present invention.
It should be noted that fig. 1A and 1B are only examples of application scenarios in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
It will be appreciated that each credit agency requires the user to provide corresponding user data, such as credit records, payroll status, academic status, household income, etc., before providing credit services to the user, in order to prove that the user has certain repayment capabilities. Credit agencies typically perform risk assessments on users based on user data provided by the users to determine whether the users qualify for the credit service.
As shown in FIG. 1A, in the prior art, each credit agency typically only uses a single model to evaluate a user's risk index from the user data when evaluating the user's risk. It can be understood that, the determination of whether to approve the application of the user or reject the application of the user is only determined according to the evaluation result of one model, and a misjudgment phenomenon often occurs, so that a plurality of users with application qualification are rejected, the customer loss of a credit agency is caused, and the experience of the application user is not good.
In view of this, the embodiment of the present invention provides a user financial risk assessment method based on multiple models. As shown in fig. 1B, the present invention inputs the user data into a plurality of evaluation models, and comprehensively evaluates the final risk index of the user according to the evaluation results of the plurality of evaluation models and the KS values corresponding to the evaluation models. It can be understood that the plurality of evaluation models can evaluate the qualification of the user from a plurality of different angles, so that the accuracy of the financial risk evaluation of the user is improved, and the inconvenience caused by misjudgment to the customer and the problem of customer loss caused by credit institutions are at least partially avoided.
FIG. 2 schematically shows a flow diagram of a multi-model based user financial risk assessment method according to an embodiment of the invention.
As shown in fig. 2, the method includes operations S201 to S204.
In operation S201, user data is acquired.
According to the embodiment of the invention, the acquiring of the user data can be acquiring data of multiple dimensions of the user. Such as the user's payroll status, academic status, property status, age, credit records, and the like.
It is to be understood that the present invention is not limited to the type of the acquired user data, and those skilled in the art can set the type according to the actual situation.
In operation S202, user data is input into a plurality of trained evaluation models, and each evaluation model determines a risk index of the user.
According to the embodiment of the invention, each evaluation model can obtain the risk index of the user according to the input user data. It is understood that each evaluation model in the embodiments of the present invention may work independently, and each evaluates the risk index of the user according to the input user data.
The plurality of evaluation models in the embodiment of the invention can be different or dissimilar or different in evaluation angle, so that the risk index of the user can be evaluated more comprehensively.
In an embodiment of the present invention, the plurality of evaluation models are the same or different, and the training data of the plurality of evaluation models are different.
It can be understood that, in order to more comprehensively evaluate the risk index of the user, the invention can adopt a method of isolating the data sources of a plurality of evaluation models and training the plurality of evaluation models in a non-crossed manner.
For example, the training data sources for the multiple assessment models are completely different and do not intersect. For example, the evaluation model 1 uses user data of users 1 to 100 as training data, and the evaluation model 2 uses user data of users 101 to 200 as training data. It will be appreciated that the emphasis on the model trained differs from training data to training data.
In another embodiment of the present invention, the plurality of evaluation models are different, and the training data of the plurality of evaluation models are the same or different. Wherein the plurality of evaluation models are different, and the plurality of evaluation models comprise different model algorithms or different input characteristics.
It is to be understood that the multiple assessment models of the present invention may be assessment models of different model algorithms in order to more fully assess the risk index of the user. For example, the evaluation model 1 may employ a KNN algorithm, the evaluation model 2 may employ a SVM algorithm, the evaluation model 3 may employ a Logistic Regression (LR) algorithm, and so on.
Alternatively, in order to ensure that the evaluation angles of the models are different, the present invention may also adopt a method in which the input characteristics of a plurality of evaluation models are different. For example, the input features of the assessment model 1 include user age, user scholarship, and user family member status, and the input features of the assessment model 2 include user wage status, property status, and credit records. The input features of the multiple evaluation models of the present invention may not be completely the same, and there may be cross or no cross between the input features, and those skilled in the art can set the input features according to actual situations.
In operation S203, a KS value of each evaluation model is calculated.
KS (Kolmogorov-Smirnov) values can be used for evaluating the risk differentiation capability of the models, and the KS values corresponding to the models can be determined through the actual performances of the models. The specific method for calculating the KS value can be set by those skilled in the art according to actual circumstances.
In operation S204, a final risk index of the user for the financial transaction is determined based on the risk index and the KS value respectively corresponding to each evaluation model.
According to the embodiment of the invention, the weight of each evaluation model can be calculated according to the KS value of each evaluation model, the weight is in direct proportion to the KS value, and then the final risk index of the user for the financial business is determined based on the risk index and the weight which respectively correspond to each evaluation model.
The calculation method for calculating the weight of each evaluation model based on the KS value is as follows:
wherein, KSiIs the KS value for the ith model.
The final risk index may refer to default probability of the user, and the default probability of the user is determined to be, based on the risk index and the weight respectively corresponding to each evaluation model:
wherein p isiIs the probability of violation, ω, predicted by the ith modeliIs the weight of the ith model.
For example, the user data of the user a is input into 3 evaluation models, the default probability obtained by the evaluation model 1 is 0.5, the default probability obtained by the evaluation model 2 is 0.7, the default probability obtained by the evaluation model 3 is 0.3, and the weights of the models are 0.2, and 0.6, respectively, so that the final default probability of the user a is 0.42. And if the default probability exceeds 0.5, the user is considered as a risk user, the user A can be considered as a qualified user, and the corresponding credit service can be provided for the user through the application of the user.
It can be understood that the embodiment of the invention adopts a plurality of evaluation models to independently evaluate the risk index of the user, and then comprehensively considers the evaluation results of the plurality of models, so that the comprehensive evaluation is more comprehensively carried out on the user, and the evaluation accuracy is improved. In addition, the weight of each model is determined according to the actual performance of each model, so that the model which is better in performance and closer to the real situation obtains larger weight, and the evaluation accuracy is further optimized.
In another embodiment of the present invention, instead of matching weights for each model, a multi-model voting method may be used to determine the final risk index of the user.
For example, the voting may be performed based on risk index user identities respectively corresponding to the plurality of evaluation models, the user identities including risk users and qualified users, and the final risk index of the users for the financial transaction is determined based on the voting result.
For example, if the risk index output by the plurality of evaluation models can be default probability, the evaluation model is characterized to judge that the user is a risk user when the default probability output by the evaluation model is greater than or equal to a probability threshold, and the evaluation model is characterized to judge that the user is a qualified user when the default probability output by the evaluation model is less than the probability threshold. Or, if the risk index output by the plurality of evaluation models is 0 or 1, when the risk index output by the evaluation model is 1, the evaluation model is characterized to judge that the user is a risk user, and when the risk index output by the evaluation model is 0, the evaluation model is characterized to judge that the user is a qualified user.
Determining a final risk index of the user for the financial transaction based on the voting results may include: and if the number of the risk users in the voting result is greater than that of the qualified users, determining that the user is a risk user of the financial service, if the number of the risk users in the voting result is equal to that of the qualified users, determining that the user is a risk user of the financial service, and if the number of the risk users in the voting result is less than that of the qualified users, determining that the user is a qualified user of the financial service.
For example, the user data of the user a is input into 3 evaluation models, the default probability obtained by the evaluation model 1 is 0.5, the default probability obtained by the evaluation model 2 is 0.7, the default probability obtained by the evaluation model 3 is 0.3, and the probability threshold is 0.5, then the evaluation model 1 casts a risk user ticket for the user a, the evaluation model 2 casts a risk user ticket for the user a, the evaluation model 3 casts a qualified user ticket for the user, and then the user is finally a risk user.
It can be understood that the invention adopts a multi-model voting method, which can reduce the calculation amount brought by the weight method on the basis of comprehensively evaluating the user, and the voting method can also be regarded as matching equal weights of all models.
According to the method, the user data are input into the plurality of evaluation models, and the risk evaluation is carried out on the user by integrating the evaluation results of the plurality of evaluation models. It can be understood that the plurality of evaluation models can evaluate the qualification of the user from a plurality of different angles, so that the accuracy of the financial risk evaluation of the user is improved, and the inconvenience caused by misjudgment to the customer and the problem of customer loss caused by credit institutions are at least partially avoided.
Those skilled in the art will appreciate that all or part of the steps for implementing the above-described embodiments are implemented as programs executed by data processing apparatuses (including computers), i.e., computer programs. 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.
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.
FIG. 3 schematically shows a block diagram of a multi-model based user financial risk assessment apparatus according to an embodiment of the present invention.
As shown in fig. 3, the evaluation device 300 includes an acquisition module 310, an input module 320, a calculation module 330, and a determination module 340.
The obtaining module 310 is used for obtaining user data. According to the embodiment of the present invention, the obtaining module 310 may, for example, perform the operation S201 described above with reference to fig. 2, which is not described herein again.
The input module 320 is configured to input the user data into a plurality of trained evaluation models, and each evaluation model determines a risk index of the user. According to the embodiment of the present invention, the input module 320 may, for example, perform the operation S202 described above with reference to fig. 2, which is not described herein again.
The calculation module 330 is used to calculate the KS value for each evaluation model. According to the embodiment of the present invention, the calculating module 330 may, for example, perform operation S203 described above with reference to fig. 2, which is not described herein again.
The determining module 340 is configured to determine a final risk index of the user for the financial transaction based on the risk index and the KS value respectively corresponding to each evaluation model. According to the embodiment of the present invention, the determining module 340 may, for example, perform the operation S204 described above with reference to fig. 2, which is not described herein again.
According to the embodiment of the present invention, determining the final risk index of the user for the financial transaction based on the risk index and the KS value respectively corresponding to each evaluation model may include: and calculating the weight of each evaluation model according to the KS value of each evaluation model, wherein the weight is in direct proportion to the KS value, and determining the final risk index of the user for the financial business based on the risk index and the weight respectively corresponding to each evaluation model.
According to the embodiment of the invention, the plurality of evaluation models are the same or different, and the training data of the plurality of evaluation models are different.
According to the embodiment of the invention, the plurality of evaluation models are different, and the training data of the plurality of evaluation models are the same or different, wherein the plurality of evaluation models are different and comprise different model algorithms of the plurality of evaluation models or different input features of the plurality of evaluation models.
According to the embodiment of the invention, the final risk index refers to the default probability of the user, and the default probability is as follows:
wherein p isiIs the probability of violation, ω, predicted by the ith modeliIs the weight of the ith model.
According to the embodiment of the invention, the weight is calculated as follows:
wherein, KSiIs the KS value for the ith model.
According to an embodiment of the present invention, the evaluation apparatus 300 may, for example, perform the method described above with reference to fig. 2, which is not described herein again.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the invention may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the obtaining module 310, the inputting module 320, the calculating module 330, and the determining module 340 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 310, the inputting module 320, the calculating module 330, and the determining module 340 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 310, the inputting module 320, the calculating module 330 and the determining module 340 may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as an implementation in physical form 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. 4 schematically shows a block diagram of an electronic device 400 according to an embodiment of the invention. The electronic device shown in fig. 4 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. 4, the electronic device 400 of the exemplary embodiment is represented in the form of a general-purpose data processing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 that connects the various system components (including the memory unit 420 and the processing unit 410), a display unit 440, and the like.
The storage unit 420 stores a computer-readable program, which may be a code of a source program or a read-only program. The program may be executed by the processing unit 410 such that the processing unit 410 performs the steps of various embodiments of the present invention. For example, the processing unit 410 may perform the steps as shown in fig. 2.
The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203. The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 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.
Bus 430 may be any bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., a keyboard, a display, a network device, a bluetooth device, etc.), enable a user to interact with the electronic device 400 via the external devices 500, and/or enable the electronic device 400 to communicate with one or more other data processing devices (e.g., a router, a modem, etc.). Such communication may occur via input/output (I/O) interfaces 450, and may also occur via a network adapter 460 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). The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in the electronic device 400, 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.
FIG. 5 schematically shows a schematic diagram of a computer-readable medium according to an embodiment of the invention. As shown in fig. 5, 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 program, when executed by one or more data processing devices, enables the computer-readable medium to implement the above-described method of the invention, namely: receiving an environment switching instruction, wherein the environment switching instruction can indicate a target server to be switched, acquiring a configuration file of the target server to be switched, judging whether the target server meets a preset condition or not based on the configuration file, and if the target server meets the preset condition, switching to the target server.
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 data processing 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 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 present invention can be implemented as a method, an apparatus, an electronic device, or a computer-readable medium executing a computer program. Some or all of the functions of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP).
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 (9)
1. A user financial risk assessment method based on multiple models is characterized by comprising the following steps:
acquiring user data;
inputting the user data into a plurality of trained evaluation models, wherein each evaluation model respectively determines the risk index of the user;
calculating a KS value of each evaluation model;
and determining the final risk index of the user for the financial business based on the risk index and the KS value respectively corresponding to each evaluation model.
2. The method of claim 1, wherein determining the final risk index for the financial transaction for the user based on the risk index and the KS value corresponding to each assessment model comprises:
calculating the weight of each evaluation model according to the KS value of each evaluation model, wherein the weight is in direct proportion to the KS value;
and determining the final risk index of the user for the financial business based on the risk index and the weight respectively corresponding to each evaluation model.
3. The method according to any one of claims 1-2, wherein the plurality of evaluation models are the same or different, and wherein the training data of the plurality of evaluation models are different.
4. The method according to any one of claims 1-3, wherein the plurality of evaluation models are different, and the training data of the plurality of evaluation models are the same or different, wherein the plurality of evaluation model differences comprise different model algorithms of the plurality of evaluation models or different input features of the plurality of evaluation models.
5. The method according to any one of claims 1-4, wherein the final risk index is a default probability for the user, the default probability being:
wherein p isiIs the probability of violation, ω, predicted by the ith modeliIs the weight of the ith model.
6. The method according to any of claims 1-5, wherein the weights are calculated as follows:
wherein, KSiIs the KS value for the ith model.
7. A user financial risk assessment device based on multiple models, comprising:
the acquisition module is used for acquiring user data;
the input module is used for inputting the user data into a plurality of trained evaluation models, and each evaluation model respectively determines the risk index of the user;
the calculation module is used for calculating KS values of the evaluation models;
and the determining module is used for determining the final risk index of the user for the financial business based on the risk index and the KS value respectively corresponding to each evaluation model.
8. 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 method of any of claims 1-6.
9. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-6.
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