CN115239215B - Enterprise risk identification method and system based on deep anomaly detection - Google Patents
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Abstract
The application provides an enterprise risk identification method and system based on deep anomaly detection. The method comprises the following steps: acquiring basic information of an enterprise, processing the basic information into index items, and integrating a plurality of index items to construct a structured enterprise information table; learning the internal distribution relation of each index item in the structured enterprise information table, and constructing abnormal detection indexes of abnormality in the index items; learning the correlation among the index items in the structured enterprise information table, and constructing abnormal detection indexes of abnormality among the index items; learning the structural semantic information of the whole enterprise which is formed and reflected by all index items of the enterprise together, and constructing an abnormal detection index of abnormal structural semantic information; fusing the indexes of the three layers to construct a comprehensive abnormity detection index; and obtaining enterprise abnormal scores according to the comprehensive abnormal detection indexes. The invention has the advantages that a hierarchical anomaly detection index system is constructed, and further, the omnibearing and multi-level enterprise anomaly detection is realized.
Description
Technical Field
The application belongs to the field of enterprise risk identification research, and particularly relates to an enterprise risk identification method and system based on deep anomaly detection.
Background
At present, the identification of enterprise risks is mainly applied to the business fields of loan, credit granting and the like of financial institutions. Generally speaking, there are two ways of enterprise risk identification. One method is risk identification based on enterprise transaction information, and the method focuses on misoperation according to a solidified rule, so that the identification risk is single and the limitation is high. And the other method is to identify risks aiming at various operation information of the enterprise, however, the model is mainly applied to a specific business field, the application form is single, and the risk is difficult to distinguish and identify according to indexes, so that the comprehensive and multilevel enterprise abnormity detection capability is not provided.
Anomaly detection algorithms aim at finding data patterns in the data that do not conform to expected behavior. In the bank wind control field, behaviors such as money laundering, credit card fraud, enterprise fraud loan and the like are considered to be abnormal; in the field of medical abnormality detection, rare diseases, false medical visits, medical accidents, and the like are considered as abnormalities. In addition, the anomaly detection is widely applied to network security intrusion detection, fault detection and video monitoring. In the enterprise risk detection field, the abnormity detection can provide the perception and warning of enterprise operation abnormity. However, the application of the anomaly detection algorithm to the enterprise risk detection field has the following technical problems. On one hand, how to convert complex tabular enterprise data into information usable by an anomaly detection algorithm is a difficult point. On the other hand, most anomaly detection methods focus more on structured information (such as anomaly detection of images), but ignore the influence of unstructured information on semantics. In enterprise data, a single index (such as an asset liability rate) often plays a decisive role in overall semantics (such as enterprise business conditions).
In order to solve the problems, the application provides an enterprise risk identification method and system based on deep anomaly detection.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides an enterprise risk identification method and system based on deep anomaly detection to solve the technical problems.
The invention discloses a method for identifying enterprise risks based on deep anomaly detection in a first aspect; the method comprises the following steps:
s1, acquiring basic information of an enterprise, processing the basic information into index items, and integrating a plurality of index items to construct a structured enterprise information table;
s2, learning the internal distribution relation of each index item in the structured enterprise information table, and constructing abnormal detection indexes of abnormality in the index items;
s3, learning the correlation among the index items in the structured enterprise information table, and constructing abnormal detection indexes of abnormality among the index items;
s4, learning the structural semantic information of the whole enterprise, which is formed by all index items of the enterprise together and reflected, and constructing an abnormal detection index of abnormal structural semantic information;
s5, fusing abnormal detection indexes of abnormality in the index items, abnormal detection indexes of abnormality among the index items and abnormal detection indexes of structural semantic information abnormality to construct a comprehensive abnormal detection index;
and S6, obtaining enterprise abnormal scores according to the comprehensive abnormal detection indexes.
According to the method of the first aspect of the present invention, in the step S6, the method further comprises: and determining the source of enterprise risk according to the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the abnormality of the structural semantic information, wherein the source of the enterprise risk comprises single index item abnormality, incidence relation abnormality among the index items and enterprise data integral presentation abnormality.
According to the method of the first aspect of the present invention, in the step S1, the method for processing the basic information into index items and integrating a plurality of index items to construct the structured enterprise information table includes:
arranging the acquired basic information into a formal representation of a triple:
in which
To represent
A collection of home business entities that are,
is shown as
The number of the home enterprise,
to represent
The collection of the items of the individual indexes,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
An enterprise-specific indicator value is assigned to the enterprise,
is shown as
The value range of each index item; on the basis of the triple formal representation, an enterprise information table is constructed, and the specific process is as follows: will be provided with
In the longitudinal direction, will
Constructing a table according to horizontal arrangement; will be provided with
And
value assignment of corresponding position in table
(ii) a In the enterprise information table, the
The index value of the home enterprise is expressed in a vector form:
。
according to the method of the first aspect of the present invention, in step S2, the method for learning the internal distribution relationship of each index item in the structured business information table to construct the abnormal detection index of the abnormality in the index item includes:
reflecting the distribution rule of each index value by adopting a frequency statistical method, and converting the learning process of the distribution relation in the index into a frequency statistical process of the index values;
for each index item
Learning the distribution function of index values
The index item
Index value of (1)
Mapping to frequency of occurrence thereof
；
The abnormity detection indexes for establishing abnormity in the index item are as follows:
wherein,
denotes the first
Abnormal detection indexes of abnormality in the index items of the home enterprise,
indicates the total number of index items.
According to the method of the first aspect of the present invention, in step S3, the method for learning the correlation between the index items in the structured business information table to construct an abnormal detection index of abnormality between the index items includes:
the mutual information of a single index item and other index items is adopted to reflect the co-occurrence rule among the index items, and the learning process of the correlation relation among the index items is converted into the learning process of the mutual information measuring function among the index items;
for each index item
Learning the mutual information metric function between the index item and other index items
Will index the item
Index value of (2)
I.e. by
And the index value
Vector formed by index values corresponding to other index items
Mapping to an index value
And the index value
Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Means for removing
Other index items form the space of the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
indicates the total number of index items.
According to the method of the first aspect of the present invention, in step S4, the method for learning the structural semantic information of the whole enterprise, which is composed of and reflected by the index items of the enterprise together, and constructing the abnormality detection index of the structural semantic information abnormality includes:
first, construct
A fourth deep neural network
; 
Will be provided with
Normalized result
Switch to
In a hidden space; wherein,
normalizing the index value to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped as depth space
Dimensional vectors, i.e.
And
(ii) a For simplicity of description, note
Is composed of
Memory for recording
Is composed of
In the depth space, an exponential cosine similarity is adopted to define a similarity measurement function, and calculation is carried out
And
degree of similarity of
；
The abnormity detection indexes for constructing the structural semantic information abnormity are as follows:
wherein,
is shown as
And (4) an abnormal detection index of the structural semantic information abnormity of the home enterprise.
According to the method of the first aspect of the present invention, in step S5, the method for fusing the abnormality detection index of the abnormality in the index item, the abnormality detection index of the abnormality between the index items, and the abnormality detection index of the abnormality in the structured semantic information to construct the comprehensive abnormality detection index includes:
wherein,
first, the
The comprehensive abnormal detection indexes of the home enterprises,
is shown as
Abnormal detection indexes of abnormality in the index items of the home enterprise,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
is shown as
Abnormality detection indexes of structural semantic information abnormality of the home enterprise;
to represent
The weighting coefficient of (1) is a human set hyper-parameter;
represent
The weighting coefficient of (4) is a hyper-parameter set by a person;
represent
The weighting coefficient of (b) is a hyper-parameter set for the person.
The invention discloses a second aspect of an enterprise risk identification system based on deep anomaly detection; the system comprises:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is configured to collect basic information of an enterprise, process the basic information into index items and integrate a plurality of index items to construct a structured enterprise information table;
the second processing module is configured to learn the internal distribution relation of each index item in the structured enterprise information table and construct abnormal detection indexes of abnormality in the index items;
the third processing module is configured to learn the correlation among the index items in the structured enterprise information table, and construct abnormal detection indexes of abnormality among the index items;
the fourth processing module is configured to learn structural semantic information of the whole enterprise, which is formed by and reflected by all index items of the enterprise together, and construct an abnormal detection index of abnormal structural semantic information;
a fifth processing module, configured to fuse abnormal detection indexes of abnormalities in the index items, abnormal detection indexes of abnormalities between the index items, and abnormal detection indexes of structural semantic information abnormalities to construct a comprehensive abnormal detection index;
and the sixth processing module is configured to obtain an enterprise anomaly score according to the comprehensive anomaly detection index.
According to the system of the second aspect of the present invention, the sixth processing module is configured to further include: and determining the source of the enterprise risk according to the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the structural semantic information abnormality, wherein the source of the enterprise risk comprises single index item abnormality, incidence relation abnormality among the index items and enterprise data integral presentation abnormality.
According to the system of the second aspect of the present invention, the first processing module configured to process the basic information into index items, and the integrating a plurality of index items to construct the structured enterprise information table includes:
arranging the collected basic information into a formal representation of a triple:
in which
To represent
A collection of home business entities that are,
is shown as
The number of the home enterprise,
to represent
A set of the items of the individual indices,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
An enterprise-specific indicator value is assigned to the enterprise,
is shown as
The value range of each index item; on the basis of the triple formal representation, an enterprise information table is constructed, and the specific process is as follows: will be provided with
In the longitudinal direction, will
Constructing a table according to horizontal arrangement; will be provided with
And
value assignment of corresponding position in table
(ii) a In the enterprise information table, the first
The index value of the home enterprise is expressed in a vector form:
。
according to the system of the second aspect of the present invention, the second processing module is configured to learn the internal distribution relationship of each index item in the structured enterprise information table, and the constructing an abnormal detection index of an abnormality in an index item includes:
reflecting the distribution rule of each index value by adopting a frequency statistical method, and converting the learning process of the distribution relation in the index into a frequency statistical process of the index values;
for each index item
Learning the distribution function of index values
The index item
Index value of (1)
Mapping to frequency of its occurrence
；
The abnormity detection indexes for establishing abnormity in the index item are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormality in the index items of the home enterprise,
indicates the total number of index items.
According to the system of the second aspect of the present invention, the third processing module is configured to learn the correlation between the index items in the structured enterprise information table, and the constructing an abnormal detection index of the abnormality between the index items includes:
mutual information of a single index item and other index items is adopted to reflect a co-occurrence rule among the index items, and a mutual association relation learning process among the index items is converted into a learning process of a mutual information measuring function among the index items;
for each index item
Learning the mutual information metric function between the index item and other index items
Will index the item
Index value of (1)
I.e. by
And the index value
Vector formed by index values corresponding to other index items
Mapping to an index value
And the index value
Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Express except
Other index items form the space of the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
indicates the total number of index items.
According to the system of the second aspect of the present invention, the fourth processing module is configured to learn the structural semantic information of the whole enterprise, which is composed of and reflected by all index items of the enterprise together, and the constructing of the abnormality detection index of the structural semantic information abnormality includes:
first, construct
A fourth deep neural network
; 
Will be provided with
Normalized result
Switch over to
In a hidden space; wherein the index value is normalized to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped as depth space
Dimensional vectors, i.e.
And
(ii) a For simplicity of description, note
To record
Is composed of
In the depth space, an exponential cosine similarity is adopted to define a similarity measurement function, and calculation is carried out
And
degree of similarity of (2)
；
The abnormity detection indexes for constructing the structural semantic information abnormity are as follows:
wherein,
denotes the first
And (4) anomaly detection indexes of anomaly of the structured semantic information of the home enterprise.
According to the system of the second aspect of the present invention, the fifth processing module is configured to fuse the abnormality detection index of abnormality in the index items, the abnormality detection index of abnormality between the index items, and the abnormality detection index of abnormality in the structured semantic information, and the constructing of the comprehensive abnormality detection index includes:
wherein,
first, the
The comprehensive abnormal detection index of the home enterprise,
denotes the first
Abnormal detection indexes of abnormality in the index items of the home enterprise,
denotes the first
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
denotes the first
Abnormality detection indexes of structural semantic information abnormality of the home enterprise; to represent
The weighting coefficient of (1) is a human set hyper-parameter;
represent
The weighting coefficient of (1) is a human set hyper-parameter;
represent
The weighting coefficient of (2) is a hyper-parameter set for a person.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the enterprise risk identification method based on deep anomaly detection in any one of the first aspect of the invention.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program, which when executed by a processor, implements the steps in a method for enterprise risk identification based on deep anomaly detection according to any one of the first aspect of the present invention.
The technical effect that this application will reach is realized through following scheme: after the deep learning from the index item to the three levels of semantic structural information is carried out, the abnormal clues of the three levels are fused to construct a hierarchical abnormal detection index system, and further the omnibearing and multilevel enterprise abnormal detection is realized.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions of the present application, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present application, and that other drawings can be obtained by those skilled in the art without inventive labor.
Fig. 1 is a flowchart of an enterprise risk identification method based on deep anomaly detection according to an embodiment of the present application;
FIG. 2 is a block diagram of an enterprise risk identification system based on deep anomaly detection according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following embodiments and accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all 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 application.
Various non-limiting embodiments of the present application are described in detail below with reference to the accompanying drawings.
The invention discloses an enterprise risk identification method based on deep anomaly detection. Fig. 1 is a flowchart of an enterprise risk identification method based on deep anomaly detection according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s1, acquiring basic information of an enterprise, processing the basic information into index items, and integrating a plurality of index items to construct a structured enterprise information table;
s2, learning the internal distribution relation of each index item in the structured enterprise information table, and constructing abnormal detection indexes of abnormality in the index items;
s3, learning the correlation among the index items in the structured enterprise information table, and constructing abnormal detection indexes of abnormality among the index items;
s4, learning the structural semantic information of the whole enterprise, which is formed by all index items of the enterprise together and reflected, and constructing an abnormal detection index of abnormal structural semantic information;
s5, fusing abnormal detection indexes of abnormality in the index items, abnormal detection indexes of abnormality between the index items and abnormal detection indexes of structural semantic information abnormality to construct a comprehensive abnormal detection index;
and S6, obtaining enterprise abnormal scores according to the comprehensive abnormal detection indexes.
In step S1, basic information of an enterprise is collected, the basic information is processed into index items, a plurality of index items are integrated to construct a structured enterprise information table, and a data basis is provided for deep detection of abnormal information of the enterprise.
In some embodiments, in said step S1, the underlying information sources include, but are not limited to, enterprise-disclosed financial data, underlying business data, capital market data, and the like.
The method for processing the basic information into index items and integrating a plurality of index items to construct the structured enterprise information table comprises the following steps:
arranging the acquired basic information into a formal representation of a triple:
in which
To represent
A collection of home business entities that are,
denotes the first
The number of the home enterprise,
represent
The collection of the items of the individual indexes,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
An enterprise-specific indicator value is assigned to the enterprise,
is shown as
The value range of each index item; on the basis of the triple formal representation, an enterprise information table is constructed, and the specific process is as follows: will be provided with
In the longitudinal direction, will
Constructing a table according to horizontal arrangement; will be provided with
And with
Value assignment of corresponding position in table
(ii) a In the enterprise information table, the
The index value of the home enterprise is expressed in a vector form:
. For convenience of presentation, the following description uses
Representing a vector
To (1) a
Item, i.e.
。
In step S2, the internal distribution relationship of each index item in the structured enterprise information table is learned, and an abnormal detection index of abnormality in the index item is constructed, so as to provide a basis for effective detection of an enterprise when a single index item is abnormal.
In some embodiments, in step S2, the learning of the internal distribution relationship of each index item in the structured enterprise information table, and the method for constructing an abnormal detection index of an abnormality in an index item includes:
a frequency statistical process for converting the learning process of the distribution relation in the index into the index value by reflecting the distribution rule of each index value by adopting a frequency statistical method;
for each index item
Learning the distribution function of index values
The index item
Index value of (1)
Mapping to frequency of its occurrence
；
The abnormal detection indexes for establishing the abnormal indexes in the index item are as follows:
wherein,
denotes the first
An abnormal detection index of abnormality in the index items of the home enterprise,
indicates the total number of index items.
Specifically, for index items of the category type, such as the enterprise credit rating data, the present embodiment directly counts the frequency of occurrence of each category type value. If it is
Is a category type index item, and the category type index value of the item is composed of a set
I.e. by
Then, then
Can be calculated according to the following formula:
wherein
Indicates the number of elements in the set,
is as follows
The mapping function of the index value in each index item to the enterprise set assigned with the value is defined as formula (2):
for numerical indicators, such as total enterprise asset return rate data, the embodiment first groups the numerical indicators by using dynamic bandwidth statistics
Wherein the items of the index are grouped
The set of index values in the index item corresponds to a group of enterprises. Then, the embodiment calculates the density of each group of index values according to the reciprocal of the difference between the maximum value and the minimum value of each group of numerical index values
. Finally, for a certain value in the index item
In this embodiment, the frequency of occurrence is calculated based on the density value of the packet in which the value is located
The specific process is shown as formula (3):
(3)
the dynamic bandwidth statistics method specifically comprises the following steps: sorting numerical data from large to small, starting from maximum, and sequentially sorting the continuous data
The individual values are grouped into the same data group, wherein: 8968; marked character: 8969; meaning rounding up; if the number of the index items having the same certain value exceeds the number
Then the numbers are equally divided into the same group; if the data in a certain group are found to be equal after the group is constructed, the value is classified into the group after the value. For data packet
The density value calculation method for the data packet described above is shown in equation (4):
wherein the step (B),
and
the maximum value and the minimum value in a certain set are respectively returned as a function of the maximum value and the minimum value.
In step S3, the correlation between the index items in the structured enterprise information table is learned, an abnormal detection index for abnormality between the index items is constructed, and an abnormal situation that may occur in the enterprise is prompted by finding an abnormal change sign of the combination relationship between the index items.
In some embodiments, in step S3, the learning of the correlation between the index items in the structured enterprise information table, and the method for constructing an abnormal detection index of abnormality between the index items includes:
the mutual information of a single index item and other index items is adopted to reflect the co-occurrence rule among the index items, and the learning process of the correlation relation among the index items is converted into the learning process of the mutual information measuring function among the index items;
for each index item
Learning the mutual information metric function between the index item and other index items
Will index the item
Index value of (2)
I.e. by
And the index value
Vector formed by index values corresponding to other index items
Mapping to an index value
And the index value
Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Representing the space where other index items except the other index items form the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
indicates the total number of index items.
Specifically, the embodiment constructs the mutual information measuring function by using the similarity of two samples in the deep neural network mapping space
. Specifically, a first deep neural network is constructed
And a second deep neural network
；
Will be provided with
Normalized result of (2)
And with
One-hot coding of
(wherein the content of the first and second components,
if it is determined that
Is equal to
；
If, if
Is not equal to
) Mapped as depth space
Dimension vector
；
Will be provided with
Normalized result of (2)
And
one-hot coding of
Mapped as depth space
Dimension vector
(ii) a In the above-mentioned context,
and
normalizing the values to between-1 and 1; for the
And
the mutual information metric function is defined as shown in formula (5):
the embodiment adopts a comparison learning mode to learn the mutual information weighing function
. First, the present embodiment constructs a sample of comparative learning for the second
The second of an enterprise
Item of individual index
Will be
As a positive sample, will
Is used as a negative sample, and thus, 1 positive sample sum in total can be constructed
A negative example. The comparative learning process employed in the present embodiment is such that
The mutual information with the positive samples is maximized,
mutual information with negative examples is minimized. The loss function for training the deep neural network is shown in equation (6):
and S4, learning the structural semantic information of the whole enterprise, which is formed by all index items of the enterprise together and reflected, and constructing an abnormal detection index of abnormal structural semantic information.
In some embodiments, in step S4, the method for learning the structural semantic information of the whole enterprise, which is composed of and reflected by the index items of the enterprise together, and constructing the abnormality detection index for abnormality of the structural semantic information includes:
the data overall structural semantic information is learned and focused on structural semantic information which is formed by all index items and can reflect the overall condition of an enterprise. In the embodiment, through a deep learning method, mutually independent hidden spaces in which various types of structural semantic information of the whole data under normal conditions are embedded are learned. First, construct
A fourth deep neural network
;
Will be provided with
Normalized result
Switch to
In a hidden space; wherein,
normalizing the index value to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped as depth space
Dimension vector, i.e.
And
(ii) a For simplicity of description, note
Is composed of
Record of
Is composed of
Defining similarity in said depth space using an exponential cosine similarityFunction of quantity
Calculating
And
degree of similarity of
；
The anomaly detection index for constructing the structural semantic information anomaly is formula (7):
wherein,
is shown as
And (4) anomaly detection indexes of anomaly of the structured semantic information of the home enterprise.
Specifically, the method trains the constructed deep neural network to be in a deep space
Under the measurement, the original data space and each hidden space have a larger similarity (namely, the data structured semantic information is embedded in the hidden space), and each hidden space has a smaller similarity (namely, the hidden spaces are independent from each other). The loss function for training the deep neural network is shown in equation (8):
and S5, fusing the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the structural semantic information abnormality to construct a comprehensive abnormal detection index.
In some embodiments, in step S5, the method for fusing the abnormality detection indexes of the abnormalities in the index items, the abnormality detection indexes of the abnormalities between the index items, and the abnormality detection indexes of the abnormalities in the structured semantic information includes:
wherein,
first, the
The comprehensive abnormal detection indexes of the home enterprises,
is shown as
Abnormal detection indexes of abnormality in the index items of the home enterprise,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
denotes the first
Abnormality detection indexes of structural semantic information abnormality of the home enterprise;
represent
The weighting coefficient of (1) is a human set hyper-parameter;
represent
The weighting coefficient of (4) is a hyper-parameter set by a person;
to represent
The weighting coefficient of (b) is a hyper-parameter set for the person.
And S6, obtaining enterprise abnormal scores according to the comprehensive abnormal detection indexes.
In some embodiments, in step S6, it is determined whether the enterprise risk is due to a single index item abnormality, an association relation abnormality between index items, or an enterprise data overall presentation abnormality according to the abnormality detection index for an intra-index item abnormality, the abnormality detection index for an inter-index item abnormality, and the abnormality detection index for a structured semantic information abnormality.
Specifically, the embodiment only needs to perform training and learning once on the acquired enterprise data, and thus anomaly detection can be performed on the enterprise data. According to the comprehensive abnormality detection index
Enterprise anomaly scores can be obtained, and the larger the score is, the more suspected anomaly of the enterprise is represented, so that enterprise risks can be prompted. Meanwhile, the annual abnormality detection indexes of a single enterprise can be considered and compared
If, if
And if the risk is increased, prompting the enterprise to increase the risk. In addition, because the method adopts a hierarchical abnormality detection index system, the method can be realized by
、
、
And determining whether the enterprise risk is from single index item abnormality, association relation abnormality between index items or enterprise data overall presentation abnormality according to the value.
In summary, the scheme provided by the invention can fuse the abnormal clues of three layers after the deep learning of three layers from the index item to the semantic structural information is carried out, and construct a hierarchical abnormal detection index system, thereby realizing the omnibearing and multilevel enterprise abnormal detection.
The invention discloses an enterprise risk identification system based on deep anomaly detection in a second aspect. FIG. 2 is a block diagram of an enterprise risk identification system based on deep anomaly detection according to an embodiment of the present invention; as shown in fig. 2, the system 100 includes:
the system comprises a first processing module 101, a second processing module and a third processing module, wherein the first processing module is configured to collect basic information of an enterprise, process the basic information into index items, and integrate a plurality of index items to construct a structured enterprise information table;
the second processing module 102 is configured to learn the internal distribution relation of each index item in the structured enterprise information table, and construct an abnormal detection index of abnormality in the index item;
a third processing module 103, configured to learn the correlation between the index items in the structured enterprise information table, and construct an abnormal detection index of abnormality between the index items;
the fourth processing module 104 is configured to learn structural semantic information of the whole enterprise, which is composed and reflected by all index items of the enterprise together, and construct an abnormality detection index for abnormality of the structural semantic information;
a fifth processing module 105, configured to fuse the abnormal detection index of the abnormality in the index items, the abnormal detection index of the abnormality between the index items, and the abnormal detection index of the structural semantic information abnormality to construct a comprehensive abnormal detection index;
a sixth processing module 106, configured to obtain an enterprise anomaly score according to the comprehensive anomaly detection index.
According to the system of the second aspect of the present invention, the sixth processing module 106 is configured to further include: and determining the source of enterprise risk according to the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the abnormality of the structural semantic information, wherein the source of the enterprise risk comprises single index item abnormality, incidence relation abnormality among the index items and enterprise data integral presentation abnormality.
According to the system of the second aspect of the present invention, the first processing module 101 is configured to process the basic information into index items, and the integrating a plurality of index items to construct the structured enterprise information table includes:
arranging the acquired basic information into a formal representation of a triple:
wherein
To represent
A collection of home business entities that are,
denotes the first
The business-to-business communication system is provided with a plurality of communication devices,
to represent
A set of the items of the individual indices,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
An enterprise-specific indicator value is assigned to the enterprise,
is shown as
The value range of each index item; on the basis of the triple formal representation, an enterprise information table is constructed, and the specific process is as follows: will be provided with
In the longitudinal direction, will
Constructing a table according to horizontal arrangement; will be provided with
And
value assignment of corresponding position in table
(ii) a In the enterprise information table, the
The index value of the home enterprise is expressed in a vector form:
。
according to the system of the second aspect of the present invention, the second processing module 102 is configured to learn the internal distribution relationship of each index item in the structured enterprise information table, and constructing an anomaly detection index of an anomaly in an index item includes:
reflecting the distribution rule of each index value by adopting a frequency statistical method, and converting the learning process of the distribution relation in the index into a frequency statistical process of the index values;
for each index item
Learning the distribution function of index values
Will index the item
Index value of (1)
Mapping to frequency of occurrence thereof
；
The abnormity detection indexes for establishing abnormity in the index item are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormality in the index items of the home enterprise,
indicates the total number of index items.
According to the system of the second aspect of the present invention, the third processing module 103 is configured to learn the correlation between the index items in the structured enterprise information table, and constructing an abnormal detection index of an abnormality between the index items includes:
mutual information of a single index item and other index items is adopted to reflect a co-occurrence rule among the index items, and a mutual association relation learning process among the index items is converted into a learning process of a mutual information measuring function among the index items;
for each index item
Learning the mutual information metric function between the index item and other index items
The index item
Index value of (1)
I.e. by
And the index value
Vector formed by index values corresponding to other index items
Mapping to an index value
And the index value
Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Means for removing
Other index items form the space of the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
indicates the total number of index items.
According to the system of the second aspect of the present invention, the fourth processing module 104 is configured to learn the structural semantic information of the whole enterprise, which is composed of and reflected by the index items of the enterprise together, and the constructing of the abnormality detection index for the abnormality of the structural semantic information includes:
first, construct
A fourth deep neural network
;
Will be provided with
Normalized result
Switch to
In a hidden space; wherein,
normalizing the index value to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped to depth space
Dimension vector, i.e.
And
(ii) a For simplicity of description, note
Is composed of
Memory for recording
Is composed of
In the depth space, an exponential cosine similarity is adopted to define a similarity measurement function, and calculation is carried out
And
degree of similarity of
；
The abnormity detection indexes for constructing the structural semantic information abnormity are as follows:
wherein,
is shown as
And (4) an abnormal detection index of the structural semantic information abnormity of the home enterprise.
According to the system of the second aspect of the present invention, the fifth processing module 105 is configured to fuse the abnormality detection index of the abnormality in the index item, the abnormality detection index of the abnormality between the index items, and the abnormality detection index of the abnormality in the structured semantic information, and the constructing of the comprehensive abnormality detection index includes:
wherein,
first, the
The comprehensive abnormal detection index of the home enterprise,
is shown as
Abnormal detection indexes of abnormality in the index items of the home enterprise,
is shown as
Abnormal detection indexes of abnormal indexes among index items of the home enterprise,
is shown as
Abnormality detection indexes of structural semantic information abnormality of the home enterprise;
to represent
The weighting coefficient of (1) is a human set hyper-parameter;
to represent
The weighting coefficient of (4) is a hyper-parameter set by a person;
to represent
Weight of (2)The coefficient is a hyper-parameter set by a person.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the enterprise risk identification method based on deep anomaly detection in any one of the first aspect of the invention.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes a processor, a memory, a network interface, a display screen, and an input device, which are connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used for communicating with an external terminal in a wired or wireless mode, and the wireless mode can be realized through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the structure shown in fig. 3 is only a partial block diagram related to the technical solution of the present invention, and does not constitute a limitation of the electronic device to which the solution of the present application is applied, and a specific electronic device may include more or less components than those shown in the drawings, or combine some components, or have a different arrangement of components.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method for enterprise risk identification based on deep anomaly detection according to any one of the first aspect of the present disclosure.
Note that, the technical features of the above embodiments may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description in the present specification. The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (6)
1. An enterprise risk identification method based on deep anomaly detection is characterized by comprising the following steps:
s1, acquiring basic information of an enterprise, processing the basic information into index items, and integrating a plurality of index items to construct a structured enterprise information table;
s2, learning the internal distribution relation of each index item in the structured enterprise information table, and constructing abnormal detection indexes of abnormality in the index items;
s3, learning the correlation among the index items in the structured enterprise information table, and constructing abnormal detection indexes of abnormality among the index items;
s4, learning the structural semantic information of the whole enterprise, which is formed by all index items of the enterprise together and reflected, and constructing an abnormal detection index of abnormal structural semantic information;
s5, fusing abnormal detection indexes of abnormality in the index items, abnormal detection indexes of abnormality among the index items and abnormal detection indexes of structural semantic information abnormality to construct a comprehensive abnormal detection index;
s6, obtaining enterprise abnormal score according to the comprehensive abnormal detection index
In the step S1, the method for processing the basic information into index items and integrating multiple index items to construct a structured enterprise information table includes:
arranging the acquired basic information into a formal representation of a triple: s =<O,A,V>， O={o 1 ,o 2 ,⋯,o N Where represents a set of N enterprises, o i Represents the ith enterprise, A = { a = 1 ,a 2 ,⋯,a D Denotes a set of D index items,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
Assigning a business-specific index value, V j A value range representing the jth index item; on the basis of the three group formalization representation, an enterprise information table is constructed, and the specific process is as follows: arranging O in a longitudinal direction and A in a transverse direction to construct a table; will o i And
assigning v to the value of the corresponding position in the table j (o i ) (ii) a In the enterprise information table, the index value of the ith enterprise is expressed in a vector form:
；
in step S2, the method for learning the internal distribution relationship of each index item in the structured enterprise information table to construct an abnormal detection index of abnormality in an index item includes:
a frequency statistical process for converting the learning process of the distribution relation in the index into the index value by reflecting the distribution rule of each index value by adopting a frequency statistical method;
for each index item
Learning the distribution function of the index value
Will index the item
V index value of (1) j (o i ) Mapping to frequency of its occurrence
；
The abnormity detection indexes for establishing abnormity in the index item are as follows:
wherein,
an anomaly detection index indicating an anomaly in the index items of the i-first enterprise, and D indicating the total number of the index items;
in step S3, the method for learning the correlation between the index items in the structured enterprise information table and constructing an abnormal detection index of abnormality between the index items includes:
the mutual information of a single index item and other index items is adopted to reflect the co-occurrence rule among the index items, and the learning process of the correlation relation among the index items is converted into the learning process of the mutual information measuring function among the index items;
for each index item
Learning the mutual information metric function between the index item and other index items
The index item
Index value x in (1) i,j I.e. v j (o i ) And the index value x i,j Vector formed by index values corresponding to other index items
Mapping to an index value x i,j And the index value x i,j Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Denotes a means of removing a j Other index items form the space of the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein, I 2 (o i ) An abnormality detection index indicating abnormality between index items of the first enterprise, D indicating the total number of the index items;
in the step S4, the method for learning the structural semantic information of the whole enterprise, which is composed of and reflected by the index items of the enterprise together, and constructing the abnormality detection index of the structural semantic information abnormality includes:
firstly, K fourth deep neural networks are constructed
Will be provided with
Normalized result
Switching to the K hidden space; wherein,
normalizing the index value to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped as a dimensional vector of the depth space u, i.e.
And
(ii) a For simplicity of description, note
Is composed of
，
Is marked as
In the depth space, an exponential cosine similarity is adopted to define a similarity measurement function, and calculation is carried out
And
degree of similarity of
；
The abnormity detection indexes for constructing the structural semantic information abnormity are as follows:
wherein,
and the abnormality detection index represents the abnormality of the structured semantic information of the ith enterprise.
2. The enterprise risk identification method based on deep anomaly detection according to claim 1, wherein in the step S6, the method further comprises: and determining the source of enterprise risk according to the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the abnormality of the structural semantic information, wherein the source of the enterprise risk comprises single index item abnormality, incidence relation abnormality among the index items and enterprise data integral presentation abnormality.
3. The method for enterprise risk identification based on deep anomaly detection according to claim 1, wherein in step S5, the method for fusing the anomaly detection indexes of anomalies in the index items, the anomaly detection indexes of anomalies among the index items and the anomaly detection indexes of anomalies of structured semantic information to construct a comprehensive anomaly detection index comprises:
wherein,
the comprehensive abnormality detection index of the ith enterprise,
an abnormality detection index indicating an abnormality in the index item of the ith enterprise,
an abnormality detection index indicating abnormality between index items of an ith enterprise,
an anomaly detection index representing an anomaly of the structured semantic information of the ith enterprise;
represent
The weighting coefficient of (4) is a hyper-parameter set by a person;
represent
The weighting coefficient of (4) is a hyper-parameter set by a person;
represent
The weighting coefficient of (b) is a hyper-parameter set for the person.
4. An enterprise risk identification system for deep anomaly detection based, the system comprising:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is configured to collect basic information of an enterprise, process the basic information into index items and integrate a plurality of index items to construct a structured enterprise information table; arranging the collected basic information into a formal representation of a triple:
in which
A collection of N businesses is represented,
which represents the ith business, is a business,
a set of D index items is represented,
is a function set and a function for mapping the enterprise to the index value corresponding to each index item
Assigning a business-specific index value, V j A value range representing the jth index item; on the basis of the triple formal representation, an enterprise information table is constructed, and the specific process is as follows: arranging the O in a longitudinal direction, and arranging the A in a transverse direction to construct a table; will be provided with
And with
Value assignment of corresponding position in table
(ii) a In the enterprise information table, the index value of the ith enterprise is expressed in a vector form:
；
the second processing module is configured to learn the internal distribution relation of each index item in the structured enterprise information table and construct an abnormal detection index of abnormality in the index item; reflecting the distribution rule of each index value by adopting a frequency statistical method, and converting the learning process of the distribution relation in the index into a frequency statistical process of the index values;
for each index item
Learning the distribution function of index values
Will index the item
Index value mapping in (1)
Frequency of its occurrence
；
The abnormity detection indexes for establishing abnormity in the index item are as follows:
wherein,
an abnormality detection index indicating an abnormality in the index items of the ith enterprise, D indicating the total number of the index items;
the third processing module is configured to learn the correlation among the index items in the structured enterprise information table, and construct abnormal detection indexes of abnormality among the index items; the mutual information of a single index item and other index items is adopted to reflect the co-occurrence rule among the index items, and the learning process of the correlation relation among the index items is converted into the learning process of the mutual information measuring function among the index items;
for each index item
Learning mutual information measuring function between the index item and other index items
The index item
Index value of (2)
I.e. by
And the index value
Vector formed by index values corresponding to other index items
Mapping to an index value
And the index value
Corresponding to the mutual information size between the index values in other index items
(ii) a Wherein
Means for removing
Other index items form the space of the vector;
the abnormity detection indexes for constructing abnormity among the index items are as follows:
wherein,
an abnormality detection index indicating abnormality between index items of an ith enterprise, D indicating the total number of the index items;
the fourth processing module is configured to learn the structural semantic information of the whole enterprise, which is formed by and reflected by all index items of the enterprise together, and construct an abnormal detection index of abnormal structural semantic information; firstly, K fourth deep neural networks are constructed
; 
Will be provided with
Normalized result
Switching to the K hidden space; wherein,
normalizing the index value to be between-1 and 1; then, a third deep neural network is constructed
Will be
And
mapped as a u-dimensional vector of depth space, i.e.
And
(ii) a For simplicity of description, note
Is composed of
Record of
Is composed of
In the depth space, an exponential cosine similarity is adopted to define a similarity measurement function, and calculation is carried out
And
degree of similarity of
；
The abnormity detection indexes for constructing the structural semantic information abnormity are as follows:
wherein,
an anomaly detection index representing anomaly of structured semantic information of the ith enterprise;
the fifth processing module is configured to fuse abnormal detection indexes of abnormality in the index items, abnormal detection indexes of abnormality between the index items and abnormal detection indexes of structural semantic information abnormality to construct a comprehensive abnormal detection index;
the sixth processing module is configured to obtain an enterprise anomaly score according to the comprehensive anomaly detection index; and determining the source of enterprise risk according to the abnormal detection indexes of the abnormality in the index items, the abnormal detection indexes of the abnormality among the index items and the abnormal detection indexes of the abnormality of the structural semantic information, wherein the source of the enterprise risk comprises single index item abnormality, incidence relation abnormality among the index items and enterprise data integral presentation abnormality.
5. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the enterprise risk identification method based on deep anomaly detection according to any one of claims 1 to 3 when executing the computer program.
6. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program implements the steps of the enterprise risk identification method based on deep anomaly detection according to any one of claims 1 to 3.
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