CN110852867A - Loan risk control method and device based on big data, equipment and storage medium - Google Patents
Loan risk control method and device based on big data, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a loan risk control method based on big data, a loan risk control device based on big data, equipment and a storage medium, wherein the method comprises the following steps: obtaining basic information data of a loan object in a bank, wherein the basic information data comprises loan data of the loan object corresponding to a loan account and supplementary data of other accounts of the loan object except the loan account; generating a sample identification code according to example sample data formed by each loan record in the loan data; performing multi-dimensional behavior and action analysis based on the sample identification code and the supplementary data to generate an action identification code corresponding to example sample data; and identifying a suspected result of the sample data of the example based on the action identification code, and storing the suspected result into a corresponding database. By adopting the invention, large-scale non-purpose checking can be automatically reduced through a large-data multi-dimensional analysis technology, and the efficiency and accuracy of risk supervision are improved.
Description
Technical Field
The invention relates to the technical field of bank wind control, in particular to a loan risk control method and device based on big data, equipment and a storage medium.
Background
With the continuous development of society, the consumption level of people increases, the consumption view of people changes greatly, more and more people choose to loan from banks or financial institutions thereof, and after the financial institutions put money, behavior actions after loan accounts are reasonably monitored so as to find, identify and process risks after loan in time, for example, whether customers corresponding to the loan accounts have the following suspected behaviors can be monitored: loan fund flow back, excessive credit granting, targeted quota consumption, etc. At present, many banks still adopt the mode of manual identification verification to supervise, and the accuracy to risk identification can reduce because of artificial erroneous judgement, and along with the increase of bank loan in the account, the work load of artificial identification supervision is great, also can influence the accuracy of discernment when influencing supervision efficiency.
Disclosure of Invention
The embodiment of the invention provides a loan risk control method, a loan risk control device, loan risk control equipment and a loan risk control storage medium based on big data.
In a first aspect, an embodiment of the present invention provides a loan risk control method based on big data, which may include:
obtaining basic information data of a loan object in a bank, wherein the basic information data comprises loan data of the loan object corresponding to a loan account and supplementary data of other accounts of the loan object except the loan account;
generating a sample identification code according to example sample data formed by each loan record in the loan data;
performing multi-dimensional behavior and action analysis based on the sample identification code and the supplementary data to generate an action identification code corresponding to example sample data;
and identifying a suspected result of the sample data of the example based on the action identification code, and storing the suspected result into a corresponding database.
Further, performing multi-dimensional behavior action analysis based on the sample identification code and the supplementary data, and generating an action identification code corresponding to the sample data of the instance, including:
completing missing information for example sample data based on the supplementary data;
and carrying out multi-dimensional behavior and action analysis on the supplemented real sample data by combining the sample identification code to generate a corresponding action identification code.
Further, performing multi-dimensional behavior and action analysis on the supplemented real sample data by combining the sample identification code, and generating a corresponding action identification code, including:
carrying out standardization processing on information content items on the supplemented real sample data to generate new label information content;
performing collision fission on the supplemented real force sample data based on a big data information association retrieval method to generate a corresponding multi-dimensional behavior action record;
and converting the sample identification code corresponding to the multi-dimensional behavior action record into an action identification code according to a fixed rule.
Further, identifying a suspected result of the sample data of the instance based on the action identification code, and storing the suspected result into a corresponding database, includes:
identifying action records in the multi-dimensional action records by adopting a suspected data algorithm;
performing error correction processing on the identified action record based on an error identification library;
and storing the action records after the error correction processing into a corresponding suspected target library, and storing the error information and the white list actions in the action records into an error identification library.
Further, the method further comprises:
and outputting early warning prompt information according to the identified action record.
A second aspect of an embodiment of the present invention provides a loan risk control apparatus based on big data, which may include:
the basic data acquisition module is used for acquiring basic information data of the loan object in the bank, wherein the basic information data comprises loan data of the loan object corresponding to the loan account and supplementary data of other accounts of the loan object except the loan account;
the sample data processing module is used for generating a sample identification code according to the sample data of the example formed by each loan record in the loan data;
the multidimensional action analysis module is used for carrying out multidimensional behavior action analysis based on the sample identification code and the supplementary data to generate an action identification code corresponding to example sample data;
and the suspected result identification module is used for identifying the suspected result of the sample data of the example based on the sample identification code and the action identification code and storing the suspected result into the corresponding database.
Further, the multidimensional action analysis module comprises:
the information completion unit is used for completing missing information based on the supplementary data as the sample data of the example;
and the multidimensional action identification unit is used for carrying out multidimensional behavior and action analysis on the supplemented real force sample data by combining the sample identification code to generate a corresponding action identification code.
Further, the multi-dimensional motion recognition unit includes:
the standardization processing subunit is used for carrying out standardization processing on the information content item on the supplemented real sample data to generate new label information content;
the action record generating subunit is used for performing collision fission on the complemented real force sample data based on a big data information association retrieval method to generate a corresponding multidimensional action record;
and the identification code conversion unit is used for converting the sample identification codes corresponding to the multi-dimensional behavior action records into action identification codes according to a fixed rule.
Further, the suspected result identification module comprises:
the action record identification unit is used for identifying action records in the multi-dimensional action records by adopting a suspected data algorithm;
an error correction unit for performing error correction processing on the recognized action record based on the error recognition library;
and the suspected result processing unit is used for storing the action records subjected to the error correction processing into a corresponding suspected target library and storing the error information and the white list actions in the action records into an error identification library.
Further, the above apparatus further comprises:
and the early warning prompting module is used for outputting early warning prompting information according to the identified action record.
A third aspect of embodiments of the present invention provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the big-data based loan risk control method according to the above aspect.
A fourth aspect of the embodiments of the present invention provides a computer storage medium having at least one instruction, at least one program, a set of codes, or a set of instructions stored therein, which is loaded and executed by a processor to implement the big-data based loan risk control method according to the above aspect.
In the embodiment of the invention, the loan data of the loan account of the loan object and the supplementary data of other accounts of the loan object are combined, the behavior action record of the loan object is comprehensively analyzed, the data range of supervision is preliminarily reduced, and the suspected action is identified by adopting the suspected action identification model and is stored in the corresponding database, so that the full-automatic accurate supervision is realized, and the efficiency and the accuracy of risk supervision are 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 described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a big data-based loan risk control method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another big data based loan risk control method according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a big data-based loan risk control apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a multi-dimensional motion analysis module according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a multi-dimensional motion recognition unit according to an embodiment of the present invention;
FIG. 6 is a block diagram of a suspected result identification module according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 terms "including" and "having," and any variations thereof, in the description and claims of this invention and the above-described drawings are intended to cover a non-exclusive inclusion, and the terms "first" and "second" are used for distinguishing designations only and do not denote any order or magnitude of a number. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that the loan risk control method based on big data provided by the application can be applied to an application scene of supervising the behavior record of a loan account after a bank loan.
In the embodiment of the present invention, the loan risk control method based on big data may be applied to a Computer device, where the Computer device may be a terminal such as a tablet Computer or a Personal Computer (PC), or may be other electronic devices with computing capability.
As shown in fig. 1, the big data based loan risk control method may at least include the following steps:
and S101, acquiring basic information data of the loan object in the bank.
Specifically, the computer device may obtain basic information data of a loan object in a bank, wherein the loan object may be all clients loan in the bank, and the basic information data may include loan data related to all loan accounts (e.g., all accounting records and all accounting records of the loan clients), and may also include account information and related information of other accounts than the loan accounts of the clients (e.g., other loan records of the loan accounts than the loan account made by the bank), which may be referred to as supplementary data. For example, for the same loan object a, the loan data of the loan account of the loan object a in the bank may be acquired, and the related information of the loan object a in other accounts of the bank may also be acquired. It can be understood how many loan objects are in the bank, and how much loan data is in the obtained loan account.
And S102, generating a sample identification code according to example sample data formed by each loan record in the loan data.
Further, the computer device may generate an identification code based on sample data from each loan record in the loan data, it being understood that the sample identification code may be used to uniquely identify each loan record for subsequent identification of risk events.
And S103, performing multi-dimensional behavior and action analysis based on the sample identification code and the supplementary data, and generating an action identification code corresponding to the sample data of the example.
Specifically, the computer device may perform multidimensional behavior and action analysis based on the sample identification code and the supplemental data, and generate an action identification code corresponding to the sample data of the example, where the action identification code may be used to uniquely identify a behavior and action corresponding to each loan record.
Preferably, the device may be responsible for complementing missing information content, performing information standardization processing, generating multi-dimensional behavior action record information of the instance analysis sample by retrieving and fission of the information, acquiring useful characteristic information, and performing split storage on the information after fission by combining the acquired complementary information with the sample analysis sample data. And adding the serial number according to a fixed sorting algorithm based on the sample identification code of the sample analysis sample to generate a corresponding action identification code.
And S104, identifying the suspected result of the sample data of the example based on the action identification code, and storing the suspected result into a corresponding database.
Specifically, the device may identify a suspected result of the sample data of the instance based on the action identification code, and store the suspected result in a corresponding database, where the suspected result may include suspected fund backflow, suspected excessive credit granting, suspected directional quota consumption, and the like, and correspondingly, the database may include a loan fund backflow warning library, an excessive credit granting warning library, a directional quota consumption warning library, and the like.
Preferably, the device may identify the example sample action record information by using a suspected data algorithm according to a target algorithm, perform automatic error correction processing by using an error identification library, submit the identified record to a corresponding suspected target library, and store the error information or white list action in the error identification library, and optionally, the white list and the error identification library may also be manually added.
It should be noted that the self-learning ability of the system model is improved by continuously expanding the error recognition library, and the accuracy of the subsequent suspected result recognition is increased.
Further, the device can give an early warning prompt to the customer or the bank according to the recognition result.
In the embodiment of the invention, the loan data of the loan account of the loan object and the supplementary data of other accounts of the loan object are combined, the behavior action record of the loan object is comprehensively analyzed, the data range of supervision is preliminarily reduced, and the suspected action is identified by adopting the suspected action identification model and is stored in the corresponding database, so that the full-automatic accurate supervision is realized, and the efficiency and the accuracy of risk supervision are improved.
Referring to fig. 2, another implementation of the big data based loan risk control flow of the present application is shown:
as can be seen from fig. 2, the process specifically includes three modules: basic information acquisition and storage, information integration and distribution and suspected data algorithm identification.
It should be noted that, in this embodiment, the process of jointly attributing the information acquisition and the sample identification code generation to the basic information acquisition and storage specifically includes receiving all loan account related information of the bank customer, acquiring account information and customer related information other than the loan account of the replenishment customer, where each loan record is an example analysis sample, and in this process, synchronously completing the output of an identification code for each example analysis sample as a key component of the subsequent analysis of risk events.
In the process of information integration and distribution, each sample data of instance analysis can be supplemented with missing information content by combining the acquired supplementary data, information standardization processing is carried out, information retrieval and fission generate multi-dimensional behavior action record information of the sample analysis, useful characteristic information is acquired, the information after fission is distributed and stored, and then a sample behavior action identification code is generated by adding a serial number according to a fixed sorting algorithm based on an identification code of the sample analysis.
In the suspected data algorithm identification process, the behavior action records of the example analysis samples can be identified according to a target algorithm, suspected results which accord with the algorithm identification are stored in a corresponding suspected target library, and error information or white list actions are stored in an error identification library.
According to the method, through storage and preprocessing of basic information data, a technology of deep processing of information and big data information correlation retrieval is carried out through an information integration and distribution module, sample data and other behavior data are subjected to collision fission, multi-dimensional and multi-label behavior action record data related to an example analysis sample are generated, an information base is provided for a subsequent algorithm, and finally, a suspected data algorithm identification module is utilized, an error identification library is combined, and a model algorithm is utilized, so that rapid and efficient risk event identification with high accuracy is achieved. The method and the system realize the conversion from manual and large-range purposeless checking to automatic and relatively small-range accurate business process by assisting the user, and enable the user to find, distinguish and process the risk more quickly and accurately.
The loan risk control apparatus based on big data according to the embodiment of the present invention will be described in detail with reference to fig. 3 to 6. It should be noted that, the loan risk control apparatus shown in fig. 3 to fig. 6 is used for executing the method of the embodiment of the invention shown in fig. 1 and fig. 2, for convenience of description, only the part related to the embodiment of the invention is shown, and the details are not disclosed, please refer to the embodiment of the invention shown in fig. 1 and fig. 2.
Referring to fig. 3, a schematic structural diagram of a loan risk control device based on big data is provided in an embodiment of the present invention. As shown in fig. 3, the loan risk control apparatus 1 according to the embodiment of the present invention may include: the system comprises a basic data acquisition module 11, a sample data processing module 12, a multi-dimensional action analysis module 13, a suspected result identification module 14 and an early warning prompt module 15. As shown in fig. 4, the multidimensional motion analysis module 13 includes an information complementing unit 131 and a multidimensional motion recognition unit 132; the multidimensional motion recognition unit 132, as shown in fig. 5, includes a normalization processing subunit 1321, a motion record generation subunit 1322, and an identification code conversion subunit 1323; the suspected result identifying module 14 includes, as shown in fig. 6, an action record identifying unit 141, an error correcting unit 142, and a suspected result processing unit 143.
The basic data acquisition module 11 is configured to acquire basic information data of the loan object in the bank, where the basic information data includes loan data of the loan object corresponding to the loan account and supplementary data of other accounts of the loan object except the loan account.
And the sample data processing module 12 is configured to generate a sample identification code according to the sample data of the example formed by each loan record in the loan data.
And the multidimensional action analysis module 13 is configured to perform multidimensional behavior action analysis based on the sample identification code and the supplementary data, and generate an action identification code corresponding to the sample data of the example.
In an alternative embodiment, the multidimensional motion analysis module 13 comprises:
and an information completing unit 131, configured to complete missing information based on the supplemental data as the example sample data.
And the multidimensional action identification unit 132 is configured to perform multidimensional behavior and action analysis on the supplemented real sample data by combining the sample identification code, and generate a corresponding action identification code.
In an alternative embodiment, the multi-dimensional motion recognition unit 132 includes:
the normalization processing subunit 1321 is configured to perform normalization processing on the information content item for the complemented strength sample data, and generate new tag information content.
And the action record generating subunit 1322 is configured to perform collision fission on the complemented real force sample data based on a big data information association retrieval method, and generate a corresponding multidimensional behavior action record.
And the identification code conversion unit 1323 is configured to convert the sample identification code corresponding to the multi-dimensional behavior record into the action identification code according to a fixed rule.
And the suspected result identification module 14 is configured to identify a suspected result of the sample data of the instance based on the sample identification code and the action identification code, and store the suspected result in a corresponding database.
In an alternative embodiment, the suspected result identifying module 14 includes:
the action record identification unit 141 is configured to identify an action record in the multi-dimensional action record by using a suspected data algorithm.
And an error correction unit 142, configured to perform error correction processing on the identified action record based on the error identification library.
And the suspected result processing unit 143 is configured to store the action record after the error correction processing into the corresponding suspected target library, and store the error information and the white list action in the action record into the error identification library.
And the early warning prompting module 15 is used for outputting early warning prompting information according to the identified action record.
In the embodiment of the invention, the loan data of the loan account of the loan object and the supplementary data of other accounts of the loan object are combined, the behavior action record of the loan object is comprehensively analyzed, the data range of supervision is preliminarily reduced, and the suspected action is identified by adopting the suspected action identification model and is stored in the corresponding database, so that the full-automatic accurate supervision is realized, and the efficiency and the accuracy of risk supervision are improved.
An embodiment of the present invention further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiments shown in fig. 1 and fig. 2, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 and fig. 2, which are not described herein again.
The embodiment of the application also provides computer equipment. As shown in fig. 7, the computer device 20 may include: the at least one processor 201, e.g., CPU, the at least one network interface 204, the user interface 203, the memory 205, the at least one communication bus 202, and optionally, a display 206. Wherein a communication bus 202 is used to enable the connection communication between these components. The user interface 203 may include a touch screen, a keyboard or a mouse, among others. The network interface 204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and a communication connection may be established with the server via the network interface 204. The memory 205 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory, and the memory 205 includes a flash in the embodiment of the present invention. The memory 205 may optionally be at least one memory system located remotely from the processor 201. As shown in fig. 7, the memory 205, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and program instructions.
It should be noted that the network interface 204 may be connected to a receiver, a transmitter or other communication module, and the other communication module may include, but is not limited to, a WiFi module, a bluetooth module, etc., and it is understood that the computer device in the embodiment of the present invention may also include a receiver, a transmitter, other communication module, etc.
obtaining basic information data of a loan object in a bank, wherein the basic information data comprises loan data of the loan object corresponding to a loan account and supplementary data of other accounts of the loan object except the loan account;
generating a sample identification code according to example sample data formed by each loan record in the loan data;
performing multi-dimensional behavior and action analysis based on the sample identification code and the supplementary data to generate an action identification code corresponding to example sample data;
and identifying a suspected result of the sample data of the example based on the action identification code, and storing the suspected result into a corresponding database.
In some embodiments, when performing multidimensional behavior and action analysis based on the sample identifier and the supplemental data and generating the action identifier corresponding to the example sample data, the device 20 is specifically configured to:
completing missing information for example sample data based on the supplementary data;
and carrying out multi-dimensional behavior and action analysis on the supplemented real sample data by combining the sample identification code to generate a corresponding action identification code.
In some embodiments, when performing multidimensional behavior and action analysis on the supplemented real sample data in combination with the sample identifier to generate a corresponding action identifier, the device 20 is specifically configured to:
carrying out standardization processing on information content items on the supplemented real sample data to generate new label information content;
performing collision fission on the supplemented real force sample data based on a big data information association retrieval method to generate a corresponding multi-dimensional behavior action record;
and converting the sample identification code corresponding to the multi-dimensional behavior action record into an action identification code according to a fixed rule.
In some embodiments, when identifying a suspected result of the sample data of the instance based on the action identifier and storing the suspected result in the corresponding database, the device 20 is specifically configured to:
identifying action records in the multi-dimensional action records by adopting a suspected data algorithm;
performing error correction processing on the identified action record based on an error identification library;
and storing the action records after the error correction processing into a corresponding suspected target library, and storing the error information and the white list actions in the action records into an error identification library.
In some embodiments, apparatus 20 is further configured to:
and outputting early warning prompt information according to the identified action record.
In the embodiment of the invention, the loan data of the loan account of the loan object and the supplementary data of other accounts of the loan object are combined, the behavior action record of the loan object is comprehensively analyzed, the data range of supervision is preliminarily reduced, and the suspected action is identified by adopting the suspected action identification model and is stored in the corresponding database, so that the full-automatic accurate supervision is realized, and the efficiency and the accuracy of risk supervision are improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A loan risk control method based on big data is characterized by comprising the following steps:
obtaining basic information data of a loan object in a bank, wherein the basic information data comprises loan data of a loan account corresponding to the loan object and supplement data of other accounts of the loan object except the loan account;
generating a sample identification code according to example sample data formed by each loan record in the loan data;
performing multi-dimensional behavior and action analysis based on the sample identification code and the supplementary data to generate an action identification code corresponding to the example sample data;
and identifying the suspected result of the sample data of the example based on the action identification code, and storing the suspected result into a corresponding database.
2. The method of claim 1, wherein performing a multidimensional behavior action analysis based on the sample identifier and the supplemental data to generate an action identifier corresponding to the instance sample data comprises:
completing missing information for the example sample data based on the supplementary data;
and performing multi-dimensional behavior and action analysis on the supplemented real force sample data by combining the sample identification code to generate a corresponding action identification code.
3. The method according to claim 2, wherein performing multidimensional behavior and action analysis on the supplemented real force sample data in combination with the sample identification code to generate a corresponding action identification code comprises:
carrying out standardization processing on information content items on the supplemented real sample data to generate new label information content;
performing collision fission on the supplemented real force sample data based on a big data information association retrieval method to generate a corresponding multi-dimensional behavior action record;
and converting the sample identification code corresponding to the multi-dimensional behavior action record into an action identification code according to a fixed rule.
4. The method of claim 3, wherein identifying suspected results of the instance sample data based on the action identifier and storing the suspected results in a corresponding database comprises:
identifying action records in the multi-dimensional action records by adopting a suspected data algorithm;
performing error correction processing on the identified action record based on an error identification library;
and storing the action records after error correction processing into a corresponding suspected target library, and storing error information and white list actions in the action records into the error identification library.
5. The method of claim 4, further comprising:
and outputting early warning prompt information according to the identified action record.
6. A loan risk control apparatus based on big data, comprising:
the basic data acquisition module is used for acquiring basic information data of a loan object in a bank, wherein the basic information data comprises loan data of a loan account corresponding to the loan object and supplementary data of other accounts of the loan object except the loan account;
the sample data processing module is used for generating a sample identification code according to sample data of an example formed by each loan record in the loan data;
the multidimensional action analysis module is used for carrying out multidimensional behavior action analysis based on the sample identification code and the supplementary data and generating an action identification code corresponding to the sample data of the example;
and the suspected result identification module is used for identifying the suspected result of the sample data of the example based on the sample identification code and the action identification code and storing the suspected result into a corresponding database.
7. The apparatus of claim 6, wherein the multi-dimensional motion analysis module comprises:
the information completion unit is used for completing missing information for the example sample data based on the supplementary data;
and the multidimensional action identification unit is used for carrying out multidimensional behavior action analysis on the supplemented real force sample data by combining the sample identification code to generate a corresponding action identification code.
8. The apparatus of claim 7, wherein the multi-dimensional motion recognition unit comprises:
the standardization processing subunit is used for carrying out standardization processing on the information content item on the supplemented real sample data to generate new label information content;
the action record generating subunit is used for performing collision fission on the complemented real force sample data based on a big data information association retrieval method to generate a corresponding multidimensional action record;
and the identification code conversion unit is used for converting the sample identification codes corresponding to the multi-dimensional behavior action records into action identification codes according to a fixed rule.
9. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the big data based loan risk control method according to any one of claims 1 to 5.
10. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the big data based loan risk control method according to any one of claims 1 to 5.
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