1. A business risk and credit risk assessment method, the method comprising:

receiving a financing request of a financing requester;

responding to the financing request, and acquiring a tax data sample of the financing requester;

constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm, wherein training samples for training the operation risk scoring model and the credit risk scoring model are tax data samples of the financing requester;

calculating the operation risk score of the financing requester according to the operation risk score model, and calculating the credit risk score of the financing requester according to the credit risk score model;

and executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester.

2. The method of claim 1, wherein the machine learning algorithm-based construction of the business risk scoring model and the credit risk scoring model comprises:

constructing the operation risk scoring model based on feature engineering;

and constructing the credit risk scoring model based on feature engineering and logistic regression algorithm.

3. The method of claim 2, wherein the feature-engineering-based construction of the business risk scoring model comprises:

extracting an evaluation index from the tax data sample of the financing requester;

measuring and calculating the importance of the evaluation index based on the characteristic engineering;

scoring the evaluation index according to the importance of the evaluation index by using an analytic hierarchy process to obtain the score of the evaluation index;

comparing the scores of the evaluation indexes pairwise through a judgment matrix, and carrying out consistency check on the scores of the evaluation indexes;

if the consistency check is passed, determining the weight of the evaluation index according to the score of the evaluation index;

determining the business risk scoring model in the form of a scoring card based on the score and the weight of the assessment index.

4. The method of claim 3, wherein the feature engineering comprises one or more of a decision tree method, a variance screening method, a Pearson significance method, and a GBDT _ RFE recursive feature elimination method.

5. The method of claim 2, wherein the constructing the credit risk scoring model based on feature engineering and logistic regression algorithm comprises:

extracting a plurality of evaluation indexes from tax data samples of the financing requester;

extracting the characteristics of the evaluation indexes by using a characteristic engineering method, setting FP-Tree for the characteristics of the evaluation indexes, and determining the evaluation indexes to be modeled from the evaluation indexes according to the set FP-Tree;

performing box separation and WOE conversion on the evaluation index to be subjected to mode entering, and screening the evaluation index to be subjected to mode entering by utilizing a KS value, an AR value, an IV value and a VIF value;

fitting the relation between the evaluation indexes to be molded and target evaluation indexes through a logistic regression algorithm, and determining the target evaluation indexes from the evaluation indexes to be molded;

and constructing the credit risk scoring model, wherein the credit risk scoring model is obtained by multiplying and summing products of the coefficients of the sub-boxes of each target evaluation index and the WOE value corresponding to the target evaluation index, and the obtained sum value is used as a credit risk score.

6. The method of claim 1, wherein said performing a financing request processing operation for the financing requester based on an operational risk score and a credit risk score of the financing requester comprises:

placing the operation risk score and the credit risk score in a cross matrix for comparison, and determining a matrix area meeting a preset requirement from the cross matrix according to the preset requirement;

and executing the processing operation of the financing request of the financing requester according to the matrix area.

7. The method of any one of claims 1 to 6, wherein the tax data samples include one or more of tax registration information, stockholder information, income declaration information, tax overdue information, historical repayment performance information.

8. A risk assessment device, characterized in that the device comprises:

a receiving unit, configured to receive a financing request of a financing requester;

the acquiring unit is used for responding to the financing request and acquiring a tax data sample of the financing requester;

the construction unit is used for constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm, wherein a training sample for training the operation risk scoring model and the credit risk scoring model is a tax data sample of the financing requester;

the computing unit is used for computing the operation risk score of the financing requester according to the operation risk score model and computing the credit risk score of the financing requester according to the credit risk score model;

and the processing unit is used for executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester.

9. A risk assessment device, characterized in that the device comprises:

a memory for storing a computer program; a processor for implementing the steps of the business risk and credit risk assessment method according to any one of claims 1 to 7 when executing said computer program.

10. A computer storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 7.

Background

With the rapid development of economic society, the number of small and medium-sized micro-manufacturing enterprises is also rapidly increasing. When operating, small and medium-sized micro-manufacturing enterprises have the requirement of applying for loans to financial institutions by using production equipment of the small and medium-sized micro-manufacturing enterprises. For financial institutions, in order to guarantee the economic safety of the financial institutions, the overall operation condition, the equipment value condition and the like of small and medium-sized micro manufacturing enterprises need to be comprehensively judged, and the equipment financing risk of the small and medium-sized micro manufacturing enterprises is evaluated to determine whether the small and medium-sized micro manufacturing enterprises are credited or not. One item in the equipment financing risk assessment is to assess the operation risk and credit risk of the financing enterprise, namely, assess whether the financing enterprise has bad operation condition or whether the financing enterprise has bad credit behavior, and determine whether to accept the equipment financing request of the financing enterprise according to the operation risk and the credit risk, thereby realizing the preliminary screening of the financing enterprise.

In the prior art, the evaluation of the operation risk and the credit risk of small and medium-sized micro manufacturing enterprises is generally judged by deep investigation and analysis under a manual line. After the financial institution receives the equipment financing request of small and medium-sized micro-manufacturing enterprises and the financing enterprises provide paper materials such as various finances, streams, invoices and the like in the enterprise operation activities, the financial institution needs to arrange different examiners to visit the financing enterprises in a field investigation mode according to the internal loan auditing flow and the flow progress. During this period, the financing enterprise still needs to continuously provide or make various financing materials according to the requirements of the financial institution, and provide a financing enterprise risk assessment report to describe the related financing risk of the enterprise.

However, the financial institution arranges different examiners according to the loan flow to perform judgment through deep investigation and analysis under a manual line, which not only has low efficiency and needs to consume a large amount of human resources, but also easily mixes artificial subjective factors in the judgment process, so that the judgment result and the risk assessment result are inaccurate.

Disclosure of Invention

The embodiment of the application provides an operation risk and credit risk assessment method, an operation risk and credit risk assessment device and a computer storage medium, which are used for improving the efficiency of operation risk and credit risk assessment and improving the accuracy of a risk assessment result.

The first aspect of the embodiments of the present application provides an operation risk and credit risk assessment method, where the method includes:

receiving a financing request of a financing requester;

responding to the financing request, and acquiring a tax data sample of the financing requester;

constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm, wherein training samples for training the operation risk scoring model and the credit risk scoring model are tax data samples of the financing requester;

calculating the operation risk score of the financing requester according to the operation risk score model, and calculating the credit risk score of the financing requester according to the credit risk score model;

and executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester.

A second aspect of the embodiments of the present application provides a risk assessment apparatus, including:

a receiving unit, configured to receive a financing request of a financing requester;

the acquiring unit is used for responding to the financing request and acquiring a tax data sample of the financing requester;

the construction unit is used for constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm, wherein a training sample for training the operation risk scoring model and the credit risk scoring model is a tax data sample of the financing requester;

the computing unit is used for computing the operation risk score of the financing requester according to the operation risk score model and computing the credit risk score of the financing requester according to the credit risk score model;

and the processing unit is used for executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester.

A third aspect of embodiments of the present application provides a risk assessment apparatus, including:

a memory for storing a computer program; a processor for implementing the steps of the business risk and credit risk assessment method according to the first aspect when executing the computer program.

A fourth aspect of embodiments of the present application provides a computer storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the method of the first aspect.

According to the technical scheme, the embodiment of the application has the following advantages:

in the embodiment of the application, the risk assessment device constructs the operation risk scoring model and the credit risk scoring model according to the tax data samples, calculates the operation risk scoring by using the operation risk scoring model, calculates the credit risk scoring according to the credit risk scoring model, and executes the processing operation of the financing request of the financing requester according to the operation risk scoring and the credit risk scoring, so that the operation risk and the credit risk assessment do not need to depend on manual field investigation, the operation risk and the credit risk are determined compared with deep investigation analysis under a manual line, the working efficiency of the risk assessment can be improved, meanwhile, the risk assessment is prevented from being doped with artificial subjective factors, and the accuracy of a risk assessment result can be improved.

Drawings

FIG. 1 is a schematic flow chart illustrating a method for assessing business risk and credit risk according to an embodiment of the present disclosure;

FIG. 2 is another schematic flow chart illustrating an operation risk and credit risk assessment method according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of a risk assessment device according to an embodiment of the present application;

fig. 4 is another schematic structural diagram of a risk assessment apparatus in an embodiment of the present application.

Detailed Description

The embodiment of the application provides an operation risk and credit risk assessment method, an operation risk and credit risk assessment device and a computer storage medium, which are used for improving the efficiency of operation risk and credit risk assessment and improving the accuracy of a risk assessment result.

Referring to fig. 1, an embodiment of a method for assessing business risk and credit risk in the embodiment of the present application includes:

101. receiving a financing request of a financing requester;

the method of the embodiment can be applied to a risk assessment device which can perform the operation of assessing the operation risk and the credit risk of the financing requester, and the device can be a computer device such as a terminal, a server and the like.

In this embodiment, the financing requester may be any business entity in the social and economic activities, for example, a business entity such as an enterprise, an individual industrial business, and the like. When any business entity has financing requirement, it can send financing request to financial institution. The financial institution can use the risk assessment device to assess whether the financing requester has an operation risk and a credit risk, and the risk assessment device receives the financing request of the financing requester and performs a processing operation on the request in a subsequent step.

102. Responding to the financing request, and acquiring a tax data sample of the financing requester;

after receiving a financing request of a financing requester, the risk assessment device responds to the financing request to obtain a tax data sample of the financing requester so as to construct an operation risk scoring model and a credit risk scoring model according to the tax data sample in the subsequent steps.

103. Constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm;

in the embodiment, a machine learning algorithm is adopted to train the operation risk scoring model and the credit risk scoring model, and a training sample of the training model is a tax data sample of a financing requester. And after the model training is completed, constructing a business risk scoring model and a credit risk scoring model.

104. Calculating the operation risk score of the financing requester according to the operation risk score model, and calculating the credit risk score of the financing requester according to the credit risk score model;

after the business risk scoring model and the credit risk scoring model are constructed, the business risk scoring of the financing requester can be calculated according to the business risk scoring model, and the credit risk scoring of the financing requester can be calculated according to the credit risk scoring model. Wherein, calculating the operation risk score and the credit risk score can be calculated according to the tax data of the financing requester.

105. Executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester;

after calculating the credit risk score and the business risk score, if the score is lower than a preset acceptable range of risk, the processing operation of the financing request can be to refute the financing request without passing the financing request of the financing requester; if the score is relatively high and is within the preset acceptable range of the risk, the financing request of the financing requester is allowed to pass, and other operations related to the financing evaluation process are further executed.

In this embodiment, the risk assessment apparatus constructs an operation risk scoring model and a credit risk scoring model according to the tax data sample, calculates an operation risk score using the operation risk scoring model, calculates a credit risk score according to the credit risk scoring model, and performs a processing operation on a financing request of a financing requester according to the operation risk score and the credit risk score, so that the operation risk and the credit risk assessment do not need to depend on manual field investigation, and the operation risk and the credit risk are determined compared with deep investigation analysis under a manual line, so that the work efficiency of risk assessment can be improved, meanwhile, the risk assessment is prevented from being mixed with artificial subjective factors, and the accuracy of a risk assessment result can be improved.

The embodiments of the present application will be described in further detail below on the basis of the aforementioned embodiment shown in fig. 1. Referring to fig. 2, another embodiment of the operation risk and credit risk assessment method in the embodiment of the present application includes:

201. receiving a financing request of a financing requester;

in this embodiment, the financing requester may send a financing request to the risk assessment apparatus through its own terminal, where the financing request carries personal information of the financing requester and an authorization identifier, and the authorization identifier is used to indicate an right granted to the risk assessment apparatus to obtain tax data of the financing requester.

Specifically, the financing request may be a request for financing equipment, a request for financing product, or a request for financing assets in other forms, and the specific financing form is not limited.

202. Responding to the financing request, and acquiring a tax data sample of the financing requester;

specifically, the tax data sample may be data related to credit collection and operation of the financing requester, and may include one or more of tax registration information, stockholder information, income declaration information, overdue tax information, and historical repayment performance information.

203. Constructing an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm;

in this embodiment, the business risk scoring model and the credit risk scoring model may be constructed based on a feature engineering method and a logistic regression algorithm in a machine learning algorithm.

Specifically, an operation risk scoring model is established based on the characteristic engineering, and the specific mode can be that an evaluation index is extracted from a tax data sample of a financing requester, and the importance of the evaluation index is measured and calculated based on the characteristic engineering; after the importance of the evaluation index is measured, scoring the evaluation index according to the importance of the evaluation index by using an analytic hierarchy process to obtain a score of the evaluation index; and comparing the scores of the evaluation indexes pairwise through the judgment matrix, carrying out consistency check on the scores of the evaluation indexes, if the consistency check is passed, determining the weight of the evaluation indexes according to the scores of the evaluation indexes, and determining an operation risk scoring model in a scoring card form based on the scores and the weight of the evaluation indexes, namely finally constructing the obtained operation risk scoring model as the scoring card form model.

When CR <0.1, the judgment matrix is considered to pass the consistency check when the consistency check is executed, and the judgment matrix has satisfactory consistency.

The specific feature engineering method may be one or more of a decision tree method, a variance screening method, a Pearson significance method, and a GBDT _ RFE recursive feature elimination method. Besides, the importance of the evaluation index can be measured and calculated through expert experience in the field. Therefore, according to the embodiment, the importance of the evaluation index is measured and calculated according to the characteristic engineering method and the expert experience, and the evaluation index suitable for evaluating the operation risk can be screened out.

Among them, GBDT is a gradient boosting decision tree (gradient decision tree), and RFE is recursive feature elimination (recursive feature elimination).

The credit risk scoring model is constructed based on feature engineering and a logistic regression algorithm, wherein the specific mode of the credit risk scoring model can be that a plurality of evaluation indexes are extracted from tax data samples of a financing requester, the features of the evaluation indexes are extracted by using a feature engineering method, FP-Tree is set for the features of the evaluation indexes, and the evaluation indexes to be modelled are determined from the evaluation indexes according to the set FP-Tree, wherein the evaluation indexes to be modelled are the evaluation indexes to be determined and used for training the model; after the evaluation indexes to be molded are determined, performing box separation and evidence weight conversion (WOE) on the evaluation indexes to be molded, screening the evaluation indexes to be molded by using a KS value, an AR value, an IV value and a VIF value, fitting the relation between the evaluation indexes to be molded and the target evaluation indexes through a logistic regression algorithm, and determining the target evaluation indexes from the evaluation indexes to be molded; after the target evaluation indexes are determined, a credit risk score model can be constructed, wherein the credit risk score model is obtained by multiplying and summing products of coefficients of the sub-boxes of each target evaluation index and WOE values corresponding to the target evaluation indexes, and the obtained sum value is used as a credit risk score.

When the feature of the evaluation index is extracted by using the feature engineering method, factors such as a feature missing value, a feature importance, an IV value (information quantity), a service explanatory property, a feature correlation and the like are considered, and a specific feature extraction process includes a plurality of steps such as data preprocessing, feature scaling and encoding, feature selection, feature conversion and extraction and the like.

In this embodiment, the specific manner for constructing the business risk scoring model and the credit risk scoring model is not limited, as long as the constructed business risk scoring model can accurately quantify the business risk and the credit risk scoring model can accurately quantify the credit risk, and the specific manner is not limited herein.

204. Calculating the operation risk score of the financing requester according to the operation risk score model, and calculating the credit risk score of the financing requester according to the credit risk score model;

after the operation risk scoring model and the credit risk scoring model are established, the actual tax data of the financing requester can be input into the operation risk scoring model and the credit risk scoring model, the operation risk scoring model and the credit risk scoring model process the actual tax data of the financing requester based on the data processing logic obtained by previous training, and the operation risk scoring and the credit risk scoring are output.

205. Executing the processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester;

in this embodiment, the cross matrix may be used to comprehensively evaluate the influence degree of the operation risk score and the credit risk score on the operation risk and the credit risk of the financing requester. The operation risk score and the credit risk score are placed in a cross matrix for comparison, the matrix area meeting the preset requirement is determined from the cross matrix according to the preset requirement, and then the processing operation of the financing request of the financing requester is executed according to the matrix area, wherein the preset requirement can be the actual requirement of the financing service.

In addition, the influence degree of the business risk score and the credit risk score on the business risk and the credit risk can also be comprehensively evaluated in other ways. For example, thresholds may be set for business risk scores and credit risk scores, and when exceeded, business risk scores or credit risk scores may be considered to be outside of a preset acceptable range of risk. The manner of comprehensively evaluating the influence degree of the business risk score and the credit risk score on the business risk and the credit risk is not limited, as long as the corresponding relationship between the risk score and the risk degree can be established.

In the embodiment, the specific construction method of the operation risk scoring model and the credit risk scoring model is provided, so that the operation risk and the credit risk can be quantified more accurately, the risk assessment efficiency is improved, and the accuracy of a risk assessment result is improved.

The operation risk and credit risk assessment method in the embodiment of the present application is described above, and the risk assessment apparatus in the embodiment of the present application is described below with reference to fig. 3, where an embodiment of the risk assessment apparatus in the embodiment of the present application includes:

a receiving unit 301, configured to receive a financing request of a financing requester;

an obtaining unit 302, configured to respond to the financing request and obtain a tax data sample of the financing requester;

a constructing unit 303, configured to construct an operation risk scoring model and a credit risk scoring model based on a machine learning algorithm, where a training sample for training the operation risk scoring model and the credit risk scoring model is a tax data sample of the financing requester;

a calculating unit 304, configured to calculate an operational risk score of the financing requester according to the operational risk score model, and calculate a credit risk score of the financing requester according to the credit risk score model;

the processing unit 305 is configured to perform a processing operation of the financing request of the financing requester according to the operation risk score and the credit risk score of the financing requester.

In a preferred implementation manner of this embodiment, the constructing unit 303 is specifically configured to construct the business risk scoring model based on feature engineering; and constructing the credit risk scoring model based on feature engineering and logistic regression algorithm.

In a preferred implementation manner of this embodiment, the constructing unit 303 is specifically configured to extract an evaluation index from the tax data sample of the financing requester; measuring and calculating the importance of the evaluation index based on the characteristic engineering; scoring the evaluation index according to the importance of the evaluation index by using an analytic hierarchy process to obtain the score of the evaluation index; comparing the scores of the evaluation indexes pairwise through a judgment matrix, and carrying out consistency check on the scores of the evaluation indexes; if the consistency check is passed, determining the weight of the evaluation index according to the score of the evaluation index; determining the business risk scoring model in the form of a scoring card based on the score and the weight of the assessment index.

In a preferred implementation manner of this embodiment, the feature engineering includes one or more feature engineering methods of a decision tree method, a variance screening method, a Pearson significance method, and a GBDT _ RFE recursive feature elimination method.

In a preferred implementation manner of this embodiment, the constructing unit 303 is specifically configured to extract a plurality of evaluation indexes from the tax data sample of the financing requester; extracting the characteristics of the evaluation indexes by using a characteristic engineering method, setting FP-Tree for the characteristics of the evaluation indexes, and determining the evaluation indexes to be modeled from the evaluation indexes according to the set FP-Tree; performing box separation and WOE conversion on the evaluation index to be subjected to mode entering, and screening the evaluation index to be subjected to mode entering by utilizing a KS value, an AR value, an IV value and a VIF value; fitting the relation between the evaluation indexes to be molded and target evaluation indexes through a logistic regression algorithm, and determining the target evaluation indexes from the evaluation indexes to be molded; and constructing the credit risk scoring model, wherein the credit risk scoring model is obtained by multiplying and summing products of the coefficients of the sub-boxes of each target evaluation index and the WOE value corresponding to the target evaluation index, and the obtained sum value is used as a credit risk score.

In a preferred implementation manner of this embodiment, the processing unit 305 is specifically configured to place the business risk score and the credit risk score in a cross matrix for comparison, and determine a matrix area meeting a preset requirement from the cross matrix according to the preset requirement; and executing the processing operation of the financing request of the financing requester according to the matrix area.

In a preferred embodiment of this embodiment, the tax data sample includes one or more of tax registration information, stockholder information, income declaration information, overdue tax information, and historical repayment performance information.

In this embodiment, the operations performed by the units in the risk assessment apparatus are similar to those described in the embodiments shown in fig. 1 to 2, and are not described again here.

In this embodiment, the construction unit 303 constructs an operation risk score model and a credit risk score model according to the tax data sample, the calculation unit 304 calculates an operation risk score using the operation risk score model, calculates a credit risk score according to the credit risk score model, and the processing unit 305 performs a processing operation on a financing request of a financing requester according to the operation risk score and the credit risk score, so that the operation risk and the credit risk assessment do not need to depend on manual field investigation, the operation risk and the credit risk are determined compared with deep investigation analysis under a manual line, the work efficiency of the risk assessment can be improved, meanwhile, the risk assessment is prevented from being doped with artificial subjective factors, and the accuracy of a risk assessment result can be improved.

Referring to fig. 4, a risk assessment apparatus in an embodiment of the present application is described below, where an embodiment of the risk assessment apparatus in the embodiment of the present application includes:

the risk assessment device 400 may include one or more Central Processing Units (CPUs) 401 and a memory 405, where the memory 405 stores one or more applications or data.

Memory 405 may be volatile storage or persistent storage, among other things. The program stored in memory 405 may include one or more modules, each of which may include a sequence of instructions operating on a risk assessment device. Still further, the central processor 401 may be configured to communicate with the memory 405 to execute a series of instruction operations in the memory 405 on the risk assessment device 400.

The risk assessment device 400 may also include one or more power supplies 402, one or more wired or wireless network interfaces 403, one or more input-output interfaces 404, and/or one or more operating systems, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

The central processing unit 401 may perform the operations performed by the risk assessment apparatus in the embodiments shown in fig. 1 to fig. 2, and detailed descriptions thereof are omitted here.

An embodiment of the present application further provides a computer storage medium, where one embodiment includes: the computer storage medium has stored therein instructions that, when executed on a computer, cause the computer to perform the operations performed by the risk assessment device in the embodiments of fig. 1-2.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.