CN113516551A - Credit and debt investment transaction risk prediction method and device - Google Patents
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Abstract
The invention provides a method and a device for predicting investment transaction risk of credit debts, and relates to the technical field of artificial intelligence. The method comprises the following steps: collecting transaction data during a credit/debt investment transaction, the transaction data comprising: training set data and prediction set data; carrying out supervision training according to the training set data and the support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a kernel parameter in the credit debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and carrying out risk prediction on target transaction data in the target credit debt investment transaction process based on a risk prediction model. The invention can predict the accuracy of the risk prediction model and reduce the learning cost, thereby improving the accuracy of the prediction of the credit debt investment transaction risk.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for predicting investment and transaction risks of credit bonds.
Background
The existing credit and debt investment transaction risk prediction is mainly realized by establishing a risk prediction model through a financial related theoretical system, and a method for training and establishing the risk prediction model based on bond investment transaction historical data by adopting a machine learning algorithm such as a neural network, a Support Vector Machine (SVM) and the like also exists at present.
Along with the gradual accumulation of bond investment transaction data and the continuous enrichment of data warehouses thereof, the prediction of bond investment transaction risks by means of machine learning is expected, but the accuracy rate is low due to the fact that data are over-fitted in the learning efficiency and accuracy rate of the currently adopted machine learning algorithm, machine learning parameters mainly depend on manual setting, and the problem of poor learning efficiency due to the fact that the setting is unreasonable exists.
Therefore, a new method for predicting the risk of investment and transaction of credit debt is urgently needed to improve the accuracy of predicting the risk of investment and transaction of credit debt.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for predicting the investment and transaction risk of credit and debt, which can effectively improve the accuracy of predicting the investment and transaction risk of credit and debt.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for predicting risk of investment transaction of credit debt, comprising:
collecting transaction data during a credit-debt investment transaction, the transaction data comprising: training set data and prediction set data;
carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model.
In one embodiment, after the transaction data in the credit bond investment transaction process, the method further comprises:
and carrying out normalization processing on the training set data and the prediction set data.
Wherein, the collecting of transaction data in the credit/debt investment transaction process comprises:
adding a risk registration tag to each piece of the transaction data;
wherein each risk registration tag corresponds to a risk assessment value.
The step of obtaining a risk prediction model by using the prediction accuracy as an adaptability value of a cuckoo search algorithm and performing iterative optimization processing on a penalty factor and a kernel parameter in the credit and debt risk prediction model through the cuckoo search algorithm comprises the following steps of:
initializing initial parameters of a cuckoo search algorithm, wherein the initial parameters comprise: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor in the credit and debt risk prediction model, and the vertical mark is a nuclear parameter in the credit and debt risk prediction model;
updating the position of a bird nest in a Laiwei flying manner;
taking the prediction accuracy as a fitness value corresponding to each bird nest;
and outputting a penalty factor and a kernel parameter of the iterative optimization processing after the maximum iteration times are reached.
After the prediction accuracy is used as the fitness value corresponding to each bird nest, the method further comprises the following steps:
compared with the previous generation bird nest, replacing the bird nest with low prediction accuracy;
judging whether the generated random number is greater than the host discovery probability; if so, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy; if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
outputting a penalty factor and a kernel parameter of iterative optimization processing after the accuracy of the optimal risk prediction is greater than the preset required accuracy;
wherein the initial parameters further include: host discovery probability.
In a second aspect, the present invention provides a credit-debt investment transaction risk prediction apparatus, comprising:
the data acquisition module is used for acquiring transaction data in the credit/debt investment transaction process, wherein the transaction data comprises: training set data and prediction set data;
the training prediction module is used for carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
the iterative optimization searching module is used for taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and performing iterative optimization searching on a penalty factor and a kernel parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
and the risk prediction module is used for carrying out risk prediction on target transaction data in the target credit/debt investment transaction process based on the risk prediction model.
In one embodiment, the method further comprises:
and the normalization module is used for performing normalization processing on the training set data and the prediction set data.
Wherein, the data acquisition module includes:
the risk registration unit is used for adding a risk registration label to each piece of data in the transaction data;
wherein each risk registration tag corresponds to a risk assessment value.
Wherein the iterative optimization module comprises:
an initialization unit, configured to initialize initial parameters of a cuckoo search algorithm, where the initial parameters include: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
the initial bird nest unit is used for randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor in the credit and debt risk prediction model, and the vertical mark is a nuclear parameter in the credit and debt risk prediction model;
the bird nest updating unit is used for updating the position of a bird nest in a Laiwei flying manner;
the setting unit is used for taking the prediction accuracy as a fitness value corresponding to each bird nest;
and the first output unit is used for outputting a penalty factor and a kernel parameter of the iteration optimizing processing after the maximum iteration times is reached.
Wherein, the iterative optimization module further comprises:
the bird nest replacing unit is used for comparing with the previous generation bird nest and replacing the bird nest with low prediction accuracy;
a judging unit for judging whether the generated random number is greater than the host discovery probability; if so, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy; if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
the second output unit is used for outputting a penalty factor and a kernel parameter of iterative optimization processing after the accuracy of the optimal risk prediction is greater than the preset required accuracy;
wherein the initial parameters further include: host discovery probability.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for predicting risk of investment transactions in credit and debt when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for predicting risk of a credit-debt investment transaction.
According to the technical scheme, the invention provides a method and a device for predicting the investment transaction risk of credit/debt, wherein the method and the device collect transaction data in the investment transaction process of the credit/debt, and the transaction data comprises the following steps: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model. The prediction accuracy of the risk prediction model can be realized, the learning cost can be reduced, and the prediction accuracy of the credit debt investment transaction risk can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a first flowchart of a method for predicting risk of investment transaction of credit debt according to an embodiment of the present invention.
Fig. 2 is a second flow chart of the method for predicting the risk of investment transaction of credit debt in the embodiment of the invention.
Fig. 3 is a schematic diagram of a first process of step S103 in the method for predicting risk of investment transaction of credit debt according to the embodiment of the present invention.
Fig. 4 is a second flowchart of step S103 in the method for predicting risk of investment transaction of credit debt according to the embodiment of the present invention.
Fig. 5 is a schematic view of the whole flow of the method for predicting the risk of investment transaction of credit debt in the embodiment of the present invention.
Fig. 6 is a schematic diagram of a first structure of a credit investment transaction risk prediction apparatus according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a second structure of a credit investment transaction risk prediction apparatus according to an embodiment of the present invention.
Fig. 8 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an embodiment of a method for predicting risk of investment and transaction of credit and debt, which specifically comprises the following contents in reference to fig. 1:
s101: collecting transaction data during a credit-debt investment transaction, the transaction data comprising: training set data and prediction set data;
in the step, transaction data in the credit/debt investment transaction process are collected, and a data warehouse is established. Before the transaction data is brought into a data warehouse, a risk registration tag is added to each piece of transaction data; wherein each risk registration tag corresponds to a risk assessment value, and the risk assessment value is a score of {0,10,20.
It should be noted that the risk assessment value suggestions are scored by an expert system or a trader. Each piece of data in the warranty data repository contains, but is not limited to, the following fields { transaction time, amount, bond type, issue time, issue object.
The data in the data warehouse is divided into two subsets in a random sampling mode, and the two subsets are respectively training set data serving as a support vector machine algorithm (SVM) learner and prediction set data of a model obtained through training.
S102: carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
s103: taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
in this step, the cuckoo search algorithm (CS) effectively solves the optimization problem by simulating parasitic brooding behavior of cuckoos, and improves the local optimization problem by using a related levy flight search mechanism. The cuckoo search algorithm has stronger global search capability and faster iteration speed.
The cuckoo search algorithm (CS) has better performance in convergence rate, search stability and search accuracy, parameter optimization is carried out by a learner supporting the vector machine algorithm, learning efficiency is effectively improved, and the optimal solution in credit and debt transaction risk prediction accuracy and learning cost is realized as far as possible, so that the accuracy of the bond investment transaction risk is improved.
S104: and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model.
As can be seen from the above description, the method for predicting risk of investment transaction of credit/debt according to the embodiment of the present invention collects transaction data during investment transaction of credit/debt, where the transaction data includes: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model. The prediction accuracy of the risk prediction model can be realized, the learning cost can be reduced, and the prediction accuracy of the credit debt investment transaction risk can be improved.
In an embodiment of the present invention, referring to fig. 2, after step S101 of the method for predicting risk of investment transaction of credit/debt specifically includes the following steps:
s105: and carrying out normalization processing on the training set data and the prediction set data.
It should be noted that data normalization is a basic work of data mining, different evaluation indexes often have different dimensions and dimension units, and in order to eliminate dimension influences among the indexes, data normalization processing needs to be performed to improve convergence speed and accuracy of a model in a subsequent step.
In the step, a min-max standardization method is adopted to process the training set data and the prediction set data, so as to realize the normalization processing of the data.
In an embodiment of the present invention, referring to fig. 3, step S103 of the method for predicting risk of investment transaction of credit/debt specifically includes the following steps:
s1031: initializing initial parameters of a cuckoo search algorithm, wherein the initial parameters comprise: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
s1032: randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor c in the credit and debt risk prediction model, and the vertical mark of the initial bird nest position is a nuclear parameter g in the credit and debt risk prediction model;
s1033: updating the position of a bird nest in a Laiwei flying manner;
in this step, the levy flight can be regarded as a non-gaussian random walk behavior, the step length of which satisfies a heavy-tailed levy stable distribution. In the flying process, the short-distance walking with small stride at high frequency and the long-distance walking with large stride which occurs in time alternate with each other, and the phenomenon that aggregation caused by multiple short-distance walks is divided by occasional large-distance jumping walking is formed.
The Laiwei flight is applied to an intelligent algorithm, so that the search range can be enlarged, the population diversity is enriched, and the local optimal problem is not easy to fall into.
S1034: taking the prediction accuracy as a fitness value corresponding to each bird nest;
s1035: and outputting a penalty factor and a kernel parameter of the iterative optimization processing after the maximum iteration times are reached.
In an embodiment of the present invention, referring to fig. 4, step S103 of the method for predicting risk of investment transaction of credit/debt specifically includes the following steps:
s1036: compared with the previous generation bird nest, replacing the bird nest with low prediction accuracy;
s1037: judging whether the generated random number is greater than the host discovery probability;
s1038: if yes, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
s1039: if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
s1035: outputting a penalty factor and a kernel parameter of iterative optimization processing after the accuracy of the optimal risk prediction is greater than the preset required accuracy;
wherein the initial parameters further include: host discovery probability.
It can be understood that parameter optimization is performed on the SVM based on the CS algorithm, and an optimal combination of two parameter penalty factors c and a kernel parameter g of a Support Vector Machine (SVM) is used as an optimal quality solution of the CS algorithm. The parameter pa in the CS algorithm is the host discovery probability, the recommendation is set to 0.25, the maximum iteration number in the CS algorithm is recommended to be set to 200, and the number of bird nests is 15.
In particular implementation, fig. 5 is a schematic view of the whole flow of the credit investment transaction risk prediction method. In fig. 5, 1-1 is a part for establishing a credit and debt risk prediction model and determining prediction accuracy based on training set data and test set data, and 1-2 is a part for performing parameter optimization on an SVM based on a CS algorithm, and is also a specific flow diagram actually adopted in step S103 in the credit and debt investment transaction risk prediction method of the embodiment.
The credit and debt investment transaction risk prediction method based on parameter optimization of the SVM by the CS algorithm improves the original SVM model, and achieves the optimal solution of improving the prediction accuracy of the credit and debt investment transaction risk and reducing the learning cost as far as possible, thereby improving the prediction performance of the credit and debt investment transaction risk method.
An embodiment of the present invention provides a specific implementation of a credit/debt investment transaction risk prediction apparatus capable of implementing all contents in the method for predicting a credit/debt investment transaction risk, and referring to fig. 6, the credit/debt investment transaction risk prediction apparatus specifically includes the following contents:
a data collection module 10, configured to collect transaction data in a credit/debt investment transaction process, where the transaction data includes: training set data and prediction set data;
the training prediction module 20 is used for performing supervision training according to the training set data and the support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
the iterative optimization searching module 30 is configured to use the prediction accuracy as a fitness value of a cuckoo search algorithm, and perform iterative optimization searching on a penalty factor and a kernel parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
and the risk prediction module 40 is used for carrying out risk prediction on target transaction data in the target credit/debt investment transaction process based on the risk prediction model.
Wherein, the data acquisition module 10 includes:
the risk registration unit is used for adding a risk registration label to each piece of data in the transaction data;
wherein each risk registration tag corresponds to a risk assessment value.
Wherein the iterative optimization module 30 comprises:
an initialization unit, configured to initialize initial parameters of a cuckoo search algorithm, where the initial parameters include: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
the initial bird nest unit is used for randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor in the credit and debt risk prediction model, and the vertical mark is a nuclear parameter in the credit and debt risk prediction model;
the bird nest updating unit is used for updating the position of a bird nest in a Laiwei flying manner;
the setting unit is used for taking the prediction accuracy as a fitness value corresponding to each bird nest;
and the first output unit is used for outputting a penalty factor and a kernel parameter of the iteration optimizing processing after the maximum iteration times is reached.
Wherein, the iterative optimization module 30 further includes:
the bird nest replacing unit is used for comparing with the previous generation bird nest and replacing the bird nest with low prediction accuracy;
a judging unit for judging whether the generated random number is greater than the host discovery probability; if so, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy; if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
in an embodiment of the present invention, referring to fig. 7, the credit-debt investment transaction risk prediction apparatus further includes:
and a normalization module 50, configured to perform normalization processing on the training set data and the prediction set data.
The embodiment of the credit/debt investment transaction risk prediction apparatus provided by the present invention may be specifically configured to execute the processing procedure of the embodiment of the method for predicting credit/debt investment transaction risk in the foregoing embodiment, and the functions thereof are not described herein again, and reference may be made to the detailed description of the method embodiment.
As can be seen from the above description, the credit-debt investment transaction risk prediction apparatus provided in the embodiment of the present invention collects transaction data during a credit-debt investment transaction process, where the transaction data includes: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model. The prediction accuracy of the risk prediction model can be realized, the learning cost can be reduced, and the prediction accuracy of the credit debt investment transaction risk can be improved.
The application provides an embodiment of an electronic device for implementing all or part of contents in the method for predicting the risk of investment transaction of credit/debt, wherein the electronic device specifically comprises the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between related devices; the electronic device may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the electronic device may be implemented with reference to the embodiment for implementing the method for predicting risk of investment transaction of credit debt and the embodiment for implementing the device for predicting risk of investment transaction of credit debt in the embodiments, and the contents thereof are incorporated herein, and repeated details are not repeated herein.
Fig. 8 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 8, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 8 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the credit-debt investment transaction risk prediction function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
collecting transaction data during a credit-debt investment transaction, the transaction data comprising: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model.
As can be seen from the above description, the electronic device provided in the embodiments of the present application collects transaction data during a credit investment transaction, where the transaction data includes: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model. The prediction accuracy of the risk prediction model can be realized, the learning cost can be reduced, and the prediction accuracy of the credit debt investment transaction risk can be improved.
In another embodiment, the credit-debt investment transaction risk prediction apparatus may be configured separately from the central processor 9100, for example, the credit-debt investment transaction risk prediction apparatus may be configured as a chip connected to the central processor 9100, and the function of predicting the credit-debt investment transaction risk is realized by the control of the central processor.
As shown in fig. 8, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 8; further, the electronic device 9600 may further include components not shown in fig. 8, which may be referred to in the art.
As shown in fig. 8, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the method for predicting risk of investment transaction of credit bond in the above embodiment, wherein the computer-readable storage medium stores thereon a computer program, which when executed by a processor implements all the steps in the method for predicting risk of investment transaction of credit bond in the above embodiment, for example, the processor implements the following steps when executing the computer program:
collecting transaction data during a credit-debt investment transaction, the transaction data comprising: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model.
As can be seen from the above description, the computer-readable storage medium provided by the embodiment of the present invention collects transaction data in a credit investment transaction process, where the transaction data includes: training set data and prediction set data; carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction; taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model; and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model. The prediction accuracy of the risk prediction model can be realized, the learning cost can be reduced, and the prediction accuracy of the credit debt investment transaction risk can be improved.
Although the present invention provides method steps as described in the examples or flowcharts, more or fewer steps may be included based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, apparatus (system) or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (12)
1. A method for predicting risk of investment transactions for credit/debt, comprising:
collecting transaction data during a credit-debt investment transaction, the transaction data comprising: training set data and prediction set data;
carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and carrying out iterative optimization processing on a penalty factor and a nuclear parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
and performing risk prediction on target transaction data in the target credit debt investment transaction process based on the risk prediction model.
2. The method of predicting risk of credit-debt investment transactions according to claim 1, further comprising, after the transaction data during the credit-debt investment transaction process:
and carrying out normalization processing on the training set data and the prediction set data.
3. The method for predicting risk of credit-debt investment transactions according to claim 1 or 2, wherein the collecting transaction data during the credit-debt investment transactions comprises:
adding a risk registration tag to each piece of the transaction data;
wherein each risk registration tag corresponds to a risk assessment value.
4. The method for predicting the risk of investment transaction of credit debt according to claim 1 or 2, wherein the step of obtaining the risk prediction model by using the prediction accuracy as the fitness value of the cuckoo search algorithm and performing iterative optimization processing on the penalty factor and the kernel parameter in the credit debt risk prediction model through the cuckoo search algorithm comprises:
initializing initial parameters of a cuckoo search algorithm, wherein the initial parameters comprise: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor in the credit and debt risk prediction model, and the vertical mark is a nuclear parameter in the credit and debt risk prediction model;
updating the position of a bird nest in a Laiwei flying manner;
taking the prediction accuracy as a fitness value corresponding to each bird nest;
and outputting a penalty factor and a kernel parameter of the iterative optimization processing after the maximum iteration times are reached.
5. The method of predicting the risk of investment transaction of credit debt according to claim 4, further comprising, after taking the prediction accuracy as the fitness value corresponding to each bird nest:
compared with the previous generation bird nest, replacing the bird nest with low prediction accuracy;
judging whether the generated random number is greater than the host discovery probability; if so, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy; if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
outputting a penalty factor and a kernel parameter of iterative optimization processing after the accuracy of the optimal risk prediction is greater than the preset required accuracy;
wherein the initial parameters further include: host discovery probability.
6. A credit-debt investment transaction risk prediction apparatus, comprising:
the data acquisition module is used for acquiring transaction data in the credit/debt investment transaction process, wherein the transaction data comprises: training set data and prediction set data;
the training prediction module is used for carrying out supervision training according to the training set data and a support vector machine algorithm to obtain a credit and debt risk prediction model; performing risk prediction on the prediction set data based on the credit and debt risk prediction model and determining the prediction accuracy of the risk prediction;
the iterative optimization searching module is used for taking the prediction accuracy as a fitness value of a cuckoo search algorithm, and performing iterative optimization searching on a penalty factor and a kernel parameter in the credit and debt risk prediction model through the cuckoo search algorithm to obtain a risk prediction model;
and the risk prediction module is used for carrying out risk prediction on target transaction data in the target credit/debt investment transaction process based on the risk prediction model.
7. The credit-debt investment transaction risk prediction apparatus of claim 6, further comprising:
and the normalization module is used for performing normalization processing on the training set data and the prediction set data.
8. The credit-debt investment transaction risk prediction device of claim 6 or 7, wherein the data collection module comprises:
the risk registration unit is used for adding a risk registration label to each piece of data in the transaction data;
wherein each risk registration tag corresponds to a risk assessment value.
9. The credit-debt investment transaction risk prediction apparatus of claim 6 or 7, wherein the iterative optimization module comprises:
an initialization unit, configured to initialize initial parameters of a cuckoo search algorithm, where the initial parameters include: the number of bird nests, the iteration times, the search range of penalty factors and the search range of nuclear parameters;
the initial bird nest unit is used for randomly generating an initial bird nest position; the horizontal mark of the initial bird nest position is a penalty factor in the credit and debt risk prediction model, and the vertical mark is a nuclear parameter in the credit and debt risk prediction model;
the bird nest updating unit is used for updating the position of a bird nest in a Laiwei flying manner;
the setting unit is used for taking the prediction accuracy as a fitness value corresponding to each bird nest;
and the first output unit is used for outputting a penalty factor and a kernel parameter of the iteration optimizing processing after the maximum iteration times is reached.
10. The credit-debt investment transaction risk prediction device of claim 9, wherein the iterative optimization module further comprises:
the bird nest replacing unit is used for comparing with the previous generation bird nest and replacing the bird nest with low prediction accuracy;
a judging unit for judging whether the generated random number is greater than the host discovery probability; if so, randomly changing the position of the bird nest and replacing the found bird nest; calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy; if not, calculating the fitness value of each bird nest and determining the current optimal bird nest and the optimal risk prediction accuracy;
the second output unit is used for outputting a penalty factor and a kernel parameter of iterative optimization processing after the accuracy of the optimal risk prediction is greater than the preset required accuracy;
wherein the initial parameters further include: host discovery probability.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for predicting risk of a credit-debt investment transaction of any one of claims 1 to 5.
12. A computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for predicting risk of investment transaction for credit bonds of any of claims 1 to 5.
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