CN114187097A

CN114187097A - Data analysis method, device, equipment, medium and product

Info

Publication number: CN114187097A
Application number: CN202111525420.8A
Authority: CN
Inventors: 苟廷熹; 李诗寰; 牛飞
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-12-14
Filing date: 2021-12-14
Publication date: 2022-03-15

Abstract

The disclosure provides a data analysis method, device, equipment, medium and product, which are applied to the field of sharing systems. The method comprises the following steps: determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object respectively; acquiring a target object with account information in any two financial institutions to acquire operation data corresponding to the target object in at least two financial institutions respectively; performing anomaly analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an anomaly analysis result; the abnormal analysis result comprises that the target object has operation abnormity or does not have operation abnormity; and outputting the abnormity analysis result of the target object. The technical scheme of the data analysis method and the data analysis device improves the accuracy of data analysis.

Description

Data analysis method, device, equipment, medium and product

Technical Field

The present disclosure relates to the field of sharing systems, and in particular, to a data analysis method, apparatus, device, medium, and product.

Background

In the financial field, in order to obtain policy benefits such as credit, tax, expense exemption and the like, enterprise users package data such as own financial reports, cash flows and the like so as to obtain improper benefits. Meanwhile, in order to improve the personal performance, a customer manager of the bank may also guide the user to adjust the relevant business data according to a preferential policy, so that the enterprise meets the corresponding policy. Due to the phenomenon, the risk prevention efficiency of enterprises is low, and the operation abnormity discovery speed of the enterprises is low.

Disclosure of Invention

The present disclosure provides a data analysis method, apparatus, device, medium and product for enterprise risk analysis. By analyzing the account intersection of any two financial institutions, the operation condition of the automatic target object is accurately analyzed, and the analysis accuracy and the analysis efficiency are improved.

According to a first aspect of the present disclosure, there is provided a data analysis method comprising:

determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object respectively;

acquiring a target object with account information in any two financial institutions to acquire operation data corresponding to the target object in at least two financial institutions respectively;

performing anomaly analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an anomaly analysis result; the abnormal analysis result comprises that the target object has operation abnormity or does not have operation abnormity;

and outputting the abnormity analysis result of the target object.

According to a second aspect of the present disclosure, there is provided a data analysis method comprising:

receiving account information which is respectively sent by at least two financial institutions and respectively corresponds to at least one object; account information corresponding to the at least one object respectively indicates that the electronic equipment feeds back by the at least one financial institution;

performing account information intersection analysis on every two financial institutions of the at least two financial institutions to obtain a target object with account information in any two financial institutions;

and sending the target object with account information of any two financial institutions to the electronic equipment so that the electronic equipment can obtain operation data of the target object corresponding to at least two financial institutions respectively, and performing exception analysis processing on the target object to obtain an exception analysis result.

According to a third aspect of the present disclosure, there is provided a data analysis apparatus comprising:

a first determination unit for determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object respectively;

the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target object with account information in any two financial institutions so as to acquire operation data corresponding to the target object in at least two financial institutions respectively;

the abnormality analysis unit is used for performing abnormality analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an abnormality analysis result; the abnormal analysis result comprises that the target object has operation abnormity or does not have operation abnormity;

a result output unit for outputting an abnormality analysis result of the target object.

According to a fourth aspect of the present disclosure, there is provided a data analysis apparatus comprising:

a data analysis device comprising:

the object receiving unit is used for receiving account information which is respectively sent by at least two financial institutions and respectively corresponds to at least one object; account information corresponding to the at least one object respectively indicates that the electronic equipment feeds back by the at least one financial institution;

the intersection analysis unit is used for carrying out intersection analysis on account information of every two financial institutions in at least two financial institutions and obtaining a target object with account information in any two financial institutions;

the object sending unit is used for sending a target object with account information of any two financial institutions to the electronic equipment so that the electronic equipment can obtain operation data of the target object corresponding to at least two financial institutions respectively, and performing exception analysis processing on the target object to obtain an exception analysis result.

According to a fifth aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.

According to a sixth aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the second aspect.

According to a seventh aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.

According to an eighth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the second aspect.

According to a ninth aspect of the present disclosure, there is provided a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first aspect.

According to a tenth aspect of the present disclosure, there is provided a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the second aspect.

According to the technology disclosed by the invention, the problem of low efficiency of discovering the enterprise operation abnormity is solved, and the account information and the operation data respectively corresponding to at least one object can be stored in the financial institutions by determining at least two financial institutions. By acquiring the target object with account information in any two financial institutions, the method can acquire the experience data of the target object corresponding to at least two financial institutions respectively, so as to perform anomaly analysis processing on the operation data of the target object corresponding to at least two financial institutions respectively, and acquire an anomaly analysis result, wherein the anomaly analysis result comprises the existence of operation anomaly or the absence of operation anomaly, so as to output the anomaly analysis result of the target object. By analyzing the account intersection of any two financial institutions, the target object with a plurality of financial accounts can be accurately determined, the operation condition of the target object can be accurately analyzed, the target object is automatically analyzed in the process, and the analysis accuracy and the analysis efficiency are improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become readily apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a system architecture diagram of a data analysis method provided in accordance with an embodiment of the present disclosure;

FIG. 2 is a flow chart diagram for one embodiment of a data analysis method provided in accordance with embodiments of the present disclosure;

FIG. 3 is a flow chart of yet another embodiment of a method of data analysis provided in accordance with an embodiment of the present disclosure;

FIG. 4 is a flow chart of yet another embodiment of a method of data analysis provided in accordance with an embodiment of the present disclosure;

FIG. 5 is a flow chart of yet another embodiment of a method of data analysis provided in accordance with an embodiment of the present disclosure;

FIG. 6 is a schematic structural diagram illustrating an embodiment of a data analysis device according to the present disclosure;

FIG. 7 is a schematic structural diagram of yet another embodiment of a data analysis device provided in accordance with an embodiment of the present disclosure;

FIG. 8 is a block diagram of an electronic device for implementing a data analysis method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The technical scheme disclosed by the invention can be applied to the scene of rapidly discovering the enterprise operation abnormity in the financial field, and the abnormity analysis efficiency is improved by acquiring and analyzing the account information and the operation data respectively corresponding to at least one object respectively corresponding to a plurality of financial institutions.

In the prior art, according to the ministry of government, ministry of industry, national statistical bureau, development and reform committee, finance department and the like, in the published small and medium-sized enterprise division standard, data such as enterprise business income, total amount of employees and assets are used as the division basis of enterprise scale. The financial institution determines financial preferential policies of the enterprise according to the enterprise scale, such as a specified directional degradation policy, bank loan interest rate deduction, tax deduction and the like. Typically, an enterprise user may register with multiple banks to obtain financial benefit policies for multiple banks simultaneously. When the enterprise user has an operation risk and the basic financial data is reported in a non-timely manner, the policy issuing may fail. Data analysis of the existing financial institutions is not timely enough, timeliness is poor when the analysis process mostly depends on financial institution staff to manually inquire the operated data in each financial institution, and the staff is required to manually perform financial analysis on the operation data on the basis of manual inquiry, so that risk discovery timeliness and efficiency of enterprise data are low.

In order to solve the above technical problem, in the embodiment of the present disclosure, it is considered that data is simultaneously queried for different financial institutions in an automatic query manner. Meanwhile, in order to ensure the security of various financial data in the financial institutions, when all data are processed, data query is not performed on a single financial institution or a plurality of financial institutions, account intersection detection is performed between every two financial data institutions, so that data analysis is performed on an object with accounts in two financial institutions at the same time, abnormal analysis is performed on the operation data of the object under the condition of high security and confidentiality, and an accurate analysis result is obtained.

In the embodiment of the present disclosure, at least two financial institutions may be determined, and account information and operation data corresponding to at least one object may be stored in the financial institutions. By acquiring the target object with account information in any two financial institutions, the method can acquire the experience data of the target object corresponding to at least two financial institutions respectively, so as to perform exception analysis processing on the operation data of the target object corresponding to at least two financial institutions respectively, and acquire an exception analysis result, wherein the exception analysis result includes the existence of an operation exception or the absence of an operation exception, so as to output the exception analysis result of the target object. By analyzing the account intersection of any two financial institutions, the target object with a plurality of financial accounts can be accurately determined, the operation condition of the target object can be accurately analyzed, the target object is automatically analyzed in the process, and the analysis accuracy and the analysis efficiency are improved.

The technical solution of the present disclosure will be described in detail with reference to the accompanying drawings.

For ease of understanding, fig. 1 is a system architecture diagram of a data analysis method provided according to an embodiment of the present disclosure, and the system architecture may include an electronic device 1 and an electronic device 2 corresponding to at least two financial institutions, respectively. The electronic apparatus 1 can establish a communication connection with at least two electronic apparatuses 2 through a local area network or a wide area network. The

electronic device

1 or 2 may include, for example: the electronic device comprises a computer, a notebook, a super personal computer, a terminal device with data processing and storing functions, a common server, a cloud server, and the like.

Generally, after obtaining authorization from at least two financial institutions based on the data analysis method provided by the embodiment of the present disclosure, the electronic device 1 may obtain the operation data of the corresponding target object from the corresponding electronic device 2, so as to perform operation anomaly analysis on the target object, and obtain an anomaly analysis result. Wherein the determination of the target object may be performed by the cloud server 3. In addition, the electronic device may also output an abnormality analysis result of the target object. By automatically acquiring the target object with account information in more than two financial institutions, after the target object is confirmed, and by using the operation data stored in the corresponding financial institutions, the target object can be automatically subjected to exception analysis, and the user does not need to participate in the process, so that an accurate exception analysis result can be obtained, and the efficiency and accuracy of exception analysis are improved.

As shown in fig. 2, a flowchart of an embodiment of a data analysis method provided in the embodiment of the present disclosure may include the following steps:

201: at least two financial institutions are determined. The financial institution stores account information and operation data corresponding to at least one object respectively.

Alternatively, the financial institution may refer to a bank, a credit company, or the like that is set up everywhere.

Determining at least two financial institutions may include: at least two financial institutions that are input by a user are obtained in response to institution input operations that are triggered by the user. In order to improve the selection efficiency of the financial institution, a selection control of the financial institution can be provided, and the selection control is set for each candidate financial institution, so that when a user selects the financial institution, the user can directly select the control to obtain the selected financial institution.

An object of the present disclosure may refer to an enterprise object. Each financial institution may store therein object account information and operation data of an enterprise object having an account opened therein. The object account information may include, for example, an object name, a financial account identifier set for the object, and the like. The operational data may include: data such as the stored amount of money on the account of the object, bank flow, enterprise financial reports and the like.

202: and acquiring the target object with account information in any two financial institutions so as to acquire the operation data of the target object corresponding to at least two financial institutions.

The target object may be an object for which account information exists at least two financial institutions. When the target object is determined, account intersection processing can be performed on the accounts of any two financial institutions, corresponding account intersection is obtained, and corresponding account intersection processing is achieved. In practical applications, the target object may be acquired by the electronic device simultaneously with the two financial institutions where the account exists. The electronic equipment can acquire related information of the target object for multiple times, so that operation data of the target object in each financial institution can be acquired from the institutions of the target object acquired for multiple times, and after data of the financial institutions are deduplicated, empirical data corresponding to the target object in at least two financial institutions can be acquired.

203: and performing anomaly analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an anomaly analysis result. The abnormal analysis result comprises the existence of the operational abnormity or the nonexistence of the operational abnormity of the target object.

The anomaly analysis result may include at least two business data analysis results for the target object.

When the abnormity analysis result is that the target object is abnormal in operation, the state that the target object is abnormal in operation can be prompted, the operation risk can be prompted in time, and the abnormity prompting efficiency is improved.

When the abnormal analysis result shows that the target object has no abnormal operation, the target object can be prompted to be in a normal operation state, and the normal state of the target object can be prompted in time, so that the target object can quickly obtain the examination and approval of various policies, and the examination and approval efficiency is improved. As an optional manner, when there is no abnormal operation of the target object, the target object may be approved according to the operation data of the target object and an approval request initiated by the target object.

204: and outputting the abnormal analysis result of the target object.

Optionally, outputting the abnormality analysis result of the target object may include: and generating a result prompting page based on the abnormal analysis result of the target object, and outputting the result prompting page for the user. Specifically, the result prompt page can be output to the user by adopting a display screen, or the page link can be sent to the user terminal by adopting a short message, a short message and other modes, and the user terminal displays the page for the user when detecting a viewing request triggered by the user.

In the embodiment of the present disclosure, when at least two financial institutions are determined, account information and operation data corresponding to at least one object may be stored in the financial institutions. By acquiring the target object with account information in any two financial institutions, the method can acquire the experience data of the target object corresponding to at least two financial institutions, so as to perform anomaly analysis processing on the operation data of the target object corresponding to at least two financial institutions, and acquire an anomaly analysis result, wherein the anomaly analysis result comprises the operation anomaly or the absence of the operation anomaly, so as to output the anomaly analysis result of the target object. By analyzing the account intersection of any two financial institutions, the target object with a plurality of financial accounts can be accurately determined, the operation condition of the target object can be accurately analyzed, the target object is automatically analyzed in the process, and the analysis accuracy and the analysis efficiency are improved.

As one embodiment, obtaining a target object for which account information exists at any two financial institutions includes:

a target object for which account information exists at any two financial institutions is acquired from a specified server.

The target object is obtained by analyzing intersection of account information of every two financial institutions in at least two financial institutions after the designated server obtains the account information which is respectively corresponding to at least one object and is respectively sent by at least two financial institutions.

Optionally, the financial institution acquires an authorization request of the at least one object corresponding to the electronic device, responds to the authorization request, and after the authorization is confirmed, the financial institution may sequentially query whether each object in the at least one object is registered in the institution, and if so, sends the registered account information and the operation data provided by the object to the designated server. If not, the object is skipped and the next object in the at least one object is traversed.

The appointed server can acquire account information and operation data respectively corresponding to at least one object fed back by each financial institution, and can compare every two financial institutions of the at least two financial institutions to acquire a target object with account intersection in the every two financial institutions.

In the embodiment of the disclosure, the target object with account information of any two financial institutions can be acquired from the designated server, so that intersection analysis, isolation intersection analysis and exception analysis processes of account information of the financial institutions at the designated server are realized, and accurate analysis of data is realized.

In one possible design, obtaining the target object with account information existing at any two financial institutions from the designated server may include:

acquiring object encryption information from a designated server; the object encryption information is obtained by encrypting a target object with account information respectively existing in any two financial institutions by a designated server;

and decrypting the object encrypted information to obtain the target object with account information existing in any two financial institutions.

The designated server may be a local server designated by the electronic device, for example, the designated local server may be a common server, a cloud server, and the like, and a specific type of the server is not limited in this disclosure.

In one possible design, the designated server may be the same device as the electronic device. The user can carry out account intersection calculation on every two financial institutions through the electronic equipment to obtain an intersection calculation result.

The appointed server obtains the intersection calculation result to determine that the target object of the account information exists in the two financial institutions, and the operation data and the account information corresponding to the target object in the two financial institutions can be encrypted to obtain object encryption information. The object encryption information may include account information of the target object and operation data respectively corresponding to the two financial institutions.

And decrypting the object encrypted information to obtain the account information and the operation data of the target object respectively stored in the two corresponding financial institutions when the target object with the account information in both financial institutions is obtained.

In the embodiment of the present disclosure, when the target object is obtained from the designated server, the object encryption information may be obtained from the designated server, and the object encryption information may be obtained by performing encryption processing on the target object for which account information exists in any two financial institutions, by the designated server. And decrypting the object encryption information to obtain the target object with account information existing in any two financial institutions. The information is encrypted and decrypted in the information transmission process with the appointed server, so that the safe transmission of the information can be realized, and the safety of the object information is improved.

As another embodiment, obtaining the transaction data of the target object corresponding to at least two financial institutions respectively comprises:

if the target object with the account information in any two financial institutions is determined, the operation data stored in all the financial institutions with the account information in the target object are obtained, so that the operation data corresponding to the target object in at least two financial institutions are obtained.

Alternatively, the target object may be obtained by the specified server performing intersection calculation between two financial institutions to at least two financial institutions. In determining the target object, the designation server may transmit the operation data of the target object in the corresponding two financial institutions to the electronic device.

Alternatively, the target object may exist in more than two financial institution groups with the account intersection, and the account information and the operation data fed back by each financial institution group may be acquired. The operation data fed back by the multiple groups of financial institutions can be subjected to duplicate removal processing, and the corresponding business data of the corresponding financial institutions are obtained. For example, if account information of an a-object exists in both the first financial institution and the second financial institution, it may be determined that the a-object exists in the first financial institution and the second financial institution as the target object. If the account information of the object a exists in both the second financial institution and the third financial institution, it may be determined that the object a exists in both the second financial institution and the third financial institution as the target object. At this time, the designated server may transmit first operated data of the a object at the first financial institution and second operated data at the second financial institution, respectively, to the electronic device; second operational data of the a-object at a second financial institution may also be transmitted to the electronic device, and third operational data at a third financial institution may be transmitted to the electronic device. The electronic device can receive the first operation data, the second operation data and the third operation data at the time, and can perform deduplication on the two second operation data to obtain the first operation data, the second operation data and the third operation data which are respectively corresponding to the three financial institutions as the object A.

The business data of the target object corresponding to at least two financial institutions may be business data stored in account information of all the existing target objects.

In the embodiment of the disclosure, when the target object is determined, the operation data stored in all the financial institutions where the account information exists in the target object may be obtained to obtain the operation data corresponding to the target object in at least two financial institutions, and the operation data stored in all the financial institutions may be obtained in a full-scale manner to analyze all the operation data of the target object as a whole, so as to obtain a more comprehensive and accurate analysis result.

As shown in fig. 3, a flow chart of another embodiment of a data analysis method provided in the embodiment of the present disclosure may include the following steps:

301: determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object respectively.

302: and acquiring the target object with account information in any two financial institutions so as to acquire the operation data of the target object corresponding to at least two financial institutions.

303: a target object level of the target object is determined.

304: determining at least one abnormal analysis parameter matched with a target object level from the object management strategies according to the multi-level object management strategies; the object management policy includes at least one anomaly analysis parameter corresponding to each of the plurality of object levels.

305: and determining analysis data corresponding to the target object in at least one abnormal analysis parameter according to the operation data corresponding to the target object in at least two financial institutions.

306: and determining the abnormal analysis result of the target object by utilizing the analysis data corresponding to the at least one abnormal analysis parameter respectively.

307: and outputting the abnormal analysis result of the target object.

Alternatively, different anomaly analysis strategies may be set according to different object levels. The object management policy may be composed of at least one anomaly analysis policy corresponding to each object level. The anomaly analysis policy for any one object level may include at least one anomaly analysis parameter corresponding to the object level, and each anomaly analysis parameter may set a corresponding anomaly threshold.

Determining at least one anomaly analysis parameter matching a target object level from the object management policies according to the multi-level object management policies may include: and determining an anomaly analysis strategy matched with the target object level according to the anomaly analysis strategies corresponding to the object levels respectively so as to obtain at least one anomaly analysis parameter corresponding to the anomaly analysis strategy.

The anomaly analysis parameter may refer to an anomaly indicator used when anomaly analysis is performed on the operation data, and may include, for example: the cash flow analysis parameters can specifically analyze the flow direction and the quantity of the cash flow so as to judge whether cash flow abnormity occurs. The anomaly analysis parameters may also include operational parameters characterizing the operational state of the enterprise, and the like. In the embodiment of the present disclosure, the specific type of the abnormal analysis parameter is not limited too much, and any parameter that can characterize the operation and management of the enterprise can be used as the abnormal analysis parameter.

In the embodiment of the disclosure, when performing anomaly analysis on a target object, a target object level of the target object may be determined, and at least one anomaly analysis parameter matched with the target object level is determined from object management policies according to multi-level object management policies, so as to determine analysis data corresponding to the target object at the at least one anomaly analysis parameter according to business data corresponding to the target object at least two financial institutions, respectively. And the anomaly analysis parameters are analyzed according to the anomaly analysis parameters, the operation data of the target object is converted into corresponding parameter data, the quantification of the anomaly analysis parameters is realized, the anomaly analysis result of the target object is determined according to the analysis data respectively corresponding to at least one anomaly analysis parameter, the anomaly quantification analysis of the target object is realized, and the accurate anomaly analysis result is obtained.

In one possible design, determining an abnormal analysis result of the target object by using analysis data corresponding to at least one abnormal analysis parameter respectively includes:

determining the parameter states of the target object corresponding to the at least one abnormal analysis parameter by utilizing the analysis data corresponding to the at least one abnormal analysis parameter respectively; the parameter state comprises an abnormal state or a normal state;

and if the number of the abnormal analysis parameters in the abnormal state in the parameter states respectively corresponding to the at least one abnormal analysis parameter is larger than the preset number, determining that the abnormal analysis result of the target data is the existence of the operating abnormality.

Optionally, determining, by using analysis data corresponding to each of the at least one abnormal analysis parameter, a parameter state of the target object corresponding to each of the at least one abnormal analysis parameter may include: and carrying out abnormity judgment on analysis data corresponding to any abnormal analysis parameter so as to obtain a parameter state corresponding to the abnormal analysis parameter. And confirming the parameter state of the target object in the abnormal analysis parameters according to the analysis data of each abnormal analysis parameter, so that an accurate analysis result can be obtained.

In the embodiment of the present disclosure, when the abnormal analysis result of the target object is determined by using the analysis data corresponding to at least one abnormal analysis parameter, the parameter state of the abnormal analysis parameter may be confirmed by using the analysis data of each abnormal analysis parameter. By accurately determining the parameter state of each abnormal analysis parameter, the abnormal analysis parameters can be accurately determined when in the abnormal state when the number of the abnormal analysis parameters in the parameter state respectively corresponding to at least one abnormal analysis parameter is larger than the preset number, and the state abnormality determination precision is improved.

In some embodiments, determining at least one anomaly analysis parameter from the object management policies that matches the target object level based on the multiple levels of object management policies comprises:

determining at least one abnormal analysis parameter matched with the target object level and an abnormal threshold corresponding to each abnormal analysis parameter from the object management strategies according to the multi-level object management strategies;

determining the parameter states of the target object corresponding to the at least one abnormal analysis parameter by using the analysis data corresponding to the at least one abnormal analysis parameter, respectively, including:

if the analysis data of the abnormal analysis parameters meet the abnormal threshold corresponding to the abnormal analysis parameters, determining that the target object is in an abnormal state in the abnormal analysis parameters;

if the analysis data of the abnormal analysis parameters do not meet the abnormal threshold corresponding to the abnormal analysis parameters, determining that the target object is in a normal state in the abnormal analysis parameters;

and obtaining the parameter states corresponding to the at least one abnormal analysis parameter respectively.

Optionally, the analyzing process of the target object at any abnormal analysis parameter may specifically be to compare the analysis data of the target object corresponding to the abnormal analysis parameter with an abnormal threshold corresponding to the abnormal analysis parameter, determine that the target object is in an abnormal state at the abnormal analysis parameter if the abnormal threshold is met, and determine that the target object is in a normal state at the abnormal analysis parameter if the abnormal threshold is not met.

In the embodiment of the disclosure, at least one anomaly analysis parameter corresponding to the target object and an anomaly threshold of each anomaly analysis parameter can be specifically determined, so that accurate acquisition of an anomaly judgment condition is realized. And then, the obtained abnormal analysis parameters and the corresponding abnormal threshold are utilized to accurately judge the abnormal state of the analysis data respectively corresponding to at least one abnormal analysis parameter, so that an accurate parameter abnormal judgment result is obtained, and the abnormal judgment accuracy is improved.

As shown in fig. 4, a flowchart of another embodiment of an anomaly analysis method provided in the embodiment of the present disclosure may include the following steps:

401: determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object respectively.

402: the method comprises the steps of obtaining at least one object set by a user and generating an object analysis request corresponding to the at least one object.

403: transmitting object analysis requests to at least one financial institution, respectively; the object analysis request instructs the financial institution to authorize the object analysis on the object.

404: and acquiring object analysis authorization of at least one object respectively by at least one financial institution.

405: and acquiring the target object with account information in any two financial institutions so as to acquire the operation data of the target object corresponding to at least two financial institutions.

406: performing anomaly analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an anomaly analysis result; the abnormal analysis result comprises the existence of the operational abnormity or the nonexistence of the operational abnormity of the target object.

407: and outputting the abnormal analysis result of the target object.

Alternatively, the object analysis request may be an authorization request of at least one object. The object analysis request is sent to at least one financial institution. Each financial institution that receives the object analysis request may perform analysis authorization on at least one object to ensure the validity of the object analysis by the electronic device.

After the financial institution authorizes the object analysis of the at least one object, the account information and the operation data of the at least one object can be transmitted to a server designated by the electronic device.

In the embodiment of the disclosure, after at least two financial institutions are determined, at least one object set by a user may be further acquired, and object analysis requests corresponding to the at least one object are generated, so as to send the object analysis requests to the at least one financial institution respectively. The financial institution may perform analysis authorization on the at least one object using the object analysis request to obtain object analysis authorization of the at least one object by the at least one financial institution, respectively, after the authorization. After authorization, anomaly analysis is performed on at least one object. By authorizing at least one financial institution to use, the safety management of the financial institution to at least one object can be ensured, and the management safety of the financial institution is effectively improved.

In one possible design, further comprising:

determining a data analysis request when data analysis is performed on at least one object according to a multi-level object management strategy;

respectively sending a data analysis request to at least one financial institution, wherein the data analysis request is used for indicating the financial institution to carry out data analysis authorization for operation data analysis confirmation on at least one object;

and acquiring data analysis authorization for performing operation data analysis on at least one object by at least one financial institution.

In the embodiment of the present disclosure, when analyzing the data of the object, in order to ensure the security of the analysis, the data analysis request of at least one object may be respectively sent to at least one financial institution to ensure the data analysis, that is, the analysis of the business data is authorized, so as to ensure the security of the data analysis of at least one object.

As a possible implementation manner, outputting the abnormality analysis result of the target object may include:

and sending the abnormal analysis result of the target object to at least two financial institutions of which the account information exists in the target object.

At least two financial institutions have all financial institutions for which account information exists for the target object.

In the embodiment of the disclosure, when the abnormal analysis result is obtained, the abnormal analysis result of the target object can be fed back to the financial institution with the account information, so that when the financial institution receives the abnormal analysis result of the target object, the abnormal analysis result is accurately prompted, and the financial risk of the target object is avoided.

As shown in fig. 5, a flow chart of another embodiment of a data analysis method provided in the embodiment of the present disclosure may include the following steps:

501: receiving account information which is respectively sent by at least two financial institutions and respectively corresponds to at least one object; the account information corresponding to the at least one object indicates the electronic device to be fed back by the at least one financial institution.

The data analysis method in the embodiment of the disclosure may be used for performing intersection analysis on every two financial institutions executed by the designated server to obtain a target object with account intersection in any two financial institutions.

The description related to the designated server may refer to the schemes described in the above embodiments.

502: performing account information intersection analysis on every two financial institutions of the at least two financial institutions to obtain a target object with account information in any two financial institutions;

503: and sending the target object with account information in any two financial institutions to the electronic equipment so that the electronic equipment can obtain the operation data of the target object in at least two financial institutions respectively, and performing exception analysis processing on the target object to obtain an exception analysis result.

Alternatively, the sending of the target object with account information existing in any two financial institutions to the electronic device may include: and sending account information and operation data of the target object respectively corresponding to the two financial institutions to the electronic equipment. Both financial institutions are financial institutions in which account information of the target object exists.

In this embodiment of the disclosure, the designation server may receive account information respectively corresponding to at least one object sent by at least two financial institutions, where the account information respectively corresponding to the at least one object indicates, for the electronic device, that the at least one financial institution is fed back to obtain. The appointed server can obtain account information corresponding to at least one object respectively so as to accurately analyze data at the appointed server, isolate isolation of account intersection analysis and abnormal analysis, enable the appointed server to accurately analyze the electronic equipment, reduce analysis pressure of the electronic equipment and improve analysis efficiency.

As one embodiment, the sending a target object for which account information exists in any two financial institutions to the electronic device includes:

encrypting target objects with account information respectively existing in any two financial institutions to obtain object encryption information;

transmitting object encryption information to the electronic device; the object encryption instructs the electronic device to decrypt the object encryption information to obtain a target object having account information at any two financial institutions.

Alternatively, the Algorithm of the encryption processing and the decryption processing may be preset according to the actual encryption requirement, for example, an Algorithm such as a Message-Digest Algorithm (MD 5) may be used to perform the encryption processing and the decryption processing. The encryption processing algorithm and the decryption processing algorithm may correspond.

In the embodiment of the present disclosure, encryption processing is performed on a target object of account information respectively existing in any two financial institutions, so as to obtain object encryption information. The account information is accurately encrypted, data loss in the data transmission process is avoided, and the safety of information transmission is improved.

In one possible design, the analysis of the intersection of account information for two or more of the at least two financial institutions includes:

and performing account information intersection analysis on every two financial institutions in the at least two financial institutions by adopting a differential privacy algorithm.

Optionally, a differential privacy algorithm (PSI protocol) may perform account intersection analysis on two financial institutions, and search for a target object with account information in both financial institutions. In the intersection analysis process, object information corresponding to the object in any one financial institution, including account information and financial data of the object, cannot be leaked, and the safety of the data is ensured.

In the embodiment of the disclosure, a differential privacy algorithm may be adopted to perform intersection analysis on account information of two financial institutions of the at least two financial institutions, so as to improve accuracy of the intersection analysis.

As shown in fig. 6, a flow chart of another embodiment of a data analysis apparatus provided in the present disclosure may include the following units:

the first determination unit 601: for determining at least two financial institutions; the financial institution stores account information and operation data corresponding to at least one object.

The first acquisition unit 602: the system comprises a target object, a plurality of financial institutions and a plurality of data processing units, wherein the target object is used for acquiring account information of any two financial institutions so as to acquire operation data corresponding to the target object in at least two financial institutions;

abnormality analysis unit 603: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for performing exception analysis processing on operation data of a target object respectively corresponding to at least two financial institutions to obtain exception analysis results; the abnormal analysis result comprises the existence of the operational abnormity or the nonexistence of the operational abnormity of the target object.

Result output unit 604: and the anomaly analysis module is used for outputting the anomaly analysis result of the target object.

As an embodiment, the first obtaining unit includes:

the system comprises a designated acquisition module, a storage module and a processing module, wherein the designated acquisition module is used for acquiring a target object with account information in any two financial institutions from a designated server; and the target object is obtained by performing account information intersection analysis on two financial institutions in the at least two financial institutions after the designated server obtains account information corresponding to at least one object respectively sent by the at least two financial institutions.

In one possible design, the designating acquisition module includes:

the encryption obtaining sub-module is used for obtaining object encryption information from a specified server; the object encryption information is obtained by encrypting a target object with account information respectively existing in any two financial institutions by a designated server;

and the information decryption submodule is used for decrypting the object encrypted information to obtain a target object with account information in any two financial institutions.

As still another embodiment, the first acquisition unit includes:

and the data acquisition module is used for acquiring the operation data stored in all the financial institutions with account information of the target object if the target object with the account information in any two financial institutions is determined, so as to acquire the operation data corresponding to the target object in at least two financial institutions.

In certain embodiments, an anomaly analysis unit, comprising:

a level determination module for determining a target object level of the target object;

the parameter determining module is used for determining at least one abnormal analysis parameter matched with the target object level from the object management strategies according to the multi-level object management strategies; the object management strategy comprises at least one abnormal analysis parameter corresponding to each object level;

the data conversion module is used for determining analysis data corresponding to the target object in at least one abnormal analysis parameter according to the business data corresponding to the target object in at least two financial institutions respectively;

and the anomaly judgment module is used for determining an anomaly analysis result of the target object by utilizing the analysis data respectively corresponding to the at least one anomaly analysis parameter.

In one possible design, the anomaly determination module includes:

the state determining submodule is used for determining the parameter state of the target object corresponding to at least one abnormal analysis parameter by utilizing the analysis data corresponding to the at least one abnormal analysis parameter; the parameter state comprises an abnormal state or a normal state;

and the result judgment submodule is used for determining that the abnormal analysis result of the target data is an operation abnormality if the number of the abnormal analysis parameters in the abnormal state in the parameter state respectively corresponding to the at least one abnormal analysis parameter is greater than the preset number.

In certain embodiments, the parameter determination module comprises:

the parameter determining submodule is used for determining at least one abnormal analysis parameter matched with the target object level and an abnormal threshold corresponding to each abnormal analysis parameter from the object management strategies according to the multi-level object management strategies;

the state determination submodule is specifically configured to:

In one possible design, further comprising:

the request acquisition unit is used for acquiring at least one object set by a user and generating an object analysis request corresponding to the at least one object;

a first transmitting unit for transmitting object analysis requests to at least one financial institution, respectively; the object analysis request instructs a financial institution to authorize object analysis of the object;

the first authorization unit is used for obtaining the object analysis authorization of at least one financial institution on at least one object respectively.

In certain embodiments, further comprising:

the request generating unit is used for determining a data analysis request when at least one object is subjected to data analysis according to the multi-level object management strategy;

the second sending unit is used for respectively sending data analysis requests to at least one financial institution, and the data analysis requests are used for instructing the financial institution to carry out data analysis authorization of operation data analysis confirmation on at least one object;

and the second authorization unit is used for acquiring data analysis authorization for performing operation data analysis on at least one object by at least one financial institution.

In one possible design, the result output unit includes:

and the result sending module is used for sending the abnormal analysis result of the target object to at least two financial institutions with account information of the target object.

The data analysis apparatus shown in fig. 6 may execute the data analysis method in the embodiments shown in fig. 1, and for specific steps executed by each unit, module or sub-module in the apparatus, reference may be made to the detailed description of the method, which is not repeated herein.

As shown in fig. 7, a schematic structural diagram of another embodiment of a data analysis apparatus provided in the embodiment of the present disclosure, the data analysis apparatus may include the following units:

object receiving unit 701: the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving account information which is respectively sent by at least two financial institutions and corresponds to at least one object; account information corresponding to the at least one object indicates that the electronic equipment feeds back by the at least one financial institution;

intersection analysis unit 702: the system comprises a plurality of financial institutions, a target object and a plurality of data processing units, wherein the target object is used for performing account information intersection analysis on every two financial institutions of at least two financial institutions to obtain a target object with account information in any two financial institutions;

object transmission unit 703: the system comprises a target object sending module, an electronic device and a processing module, wherein the target object is used for sending a target object with account information of any two financial institutions to the electronic device so that the electronic device can obtain operation data of the target object corresponding to at least two financial institutions respectively, and the target object is subjected to abnormal analysis processing to obtain an abnormal analysis result.

In one possible design, the object sending unit includes:

the information encryption module is used for encrypting target objects with account information respectively existing in any two financial institutions to obtain object encryption information;

the information sending module is used for sending the object encryption information to the electronic equipment; the object encryption instructs the electronic device to decrypt the object encryption information to obtain a target object having account information at any two financial institutions.

In some embodiments, the intersection analysis unit comprises:

and the score checking and analyzing module is used for performing account information intersection analysis on every two financial institutions in the at least two financial institutions by adopting a differential privacy algorithm.

The data analysis apparatus shown in fig. 7 may perform a data analysis method, and for specific steps performed by each unit, module or sub-module in the apparatus, reference may be made to the detailed description of the method, which is not repeated herein.

It should be noted that the account information and the operation data in this embodiment are not information and data for a specific user, and cannot reflect personal information of a specific user. In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the execution of the computer program by the at least one processor causing the electronic device to perform the solution provided by any of the embodiments described above.

FIG. 8 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the apparatus 800 includes a computing unit 801 which can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, computing units running various machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the data analysis method. For example, in some embodiments, the data analysis method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When loaded into RAM 803 and executed by computing unit 801, a computer program may perform one or more of the steps of the data analysis method described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the data analysis method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of data analysis, comprising:

and outputting the abnormity analysis result of the target object.

2. The method of claim 1, wherein obtaining the target object for which account information exists at any two financial institutions comprises:

acquiring a target object with account information existing in any two financial institutions from a designated server; and the target object is obtained by analyzing the intersection of the account information of every two financial institutions in at least two financial institutions after the designated server obtains the account information which is respectively sent by at least two financial institutions and respectively corresponds to at least one object.

3. The method of claim 2, wherein obtaining the target object for which account information exists at any two financial institutions from the designated server comprises:

acquiring object encryption information from a designated server; the object encryption information is obtained by encrypting the target object with account information respectively existing in any two financial institutions by the appointed server;

and decrypting the object encryption information to obtain the target object with account information in any two financial institutions.

4. The method according to any one of claims 1 to 3, wherein the obtaining of the business data corresponding to the target object at least two financial institutions comprises:

5. The method according to any one of claims 1 to 4, wherein the performing anomaly analysis processing on the operation data of the target object respectively corresponding to at least two financial institutions to obtain an anomaly analysis result comprises:

determining a target object level of the target object;

determining at least one abnormal analysis parameter matched with the target object level from the object management strategies according to the object management strategies of multiple levels; the object management strategy comprises at least one abnormal analysis parameter corresponding to each of a plurality of object levels;

according to the operation data of the target object respectively corresponding to at least two financial institutions, determining the analysis data of the target object respectively corresponding to at least one abnormal analysis parameter;

and determining the abnormal analysis result of the target object by utilizing the analysis data corresponding to at least one abnormal analysis parameter respectively.

6. The method according to claim 5, wherein the determining the abnormal analysis result of the target object by using the analysis data corresponding to the at least one abnormal analysis parameter respectively comprises:

determining the parameter state of the target object corresponding to at least one abnormal analysis parameter by utilizing the analysis data corresponding to at least one abnormal analysis parameter; the parameter state comprises an abnormal state or a normal state;

and if the number of the abnormal analysis parameters in the abnormal state in the parameter states respectively corresponding to at least one abnormal analysis parameter is larger than the preset number, determining that the abnormal analysis result of the target data is the operation abnormality.

7. The method of claim 6, wherein determining at least one anomaly analysis parameter from the object management policies that matches the target object level according to the multi-level object management policies comprises:

determining at least one abnormal analysis parameter matched with the target object level and an abnormal threshold corresponding to each abnormal analysis parameter from the object management policies according to multi-level object management policies;

the determining, by using analysis data corresponding to at least one of the abnormal analysis parameters, a parameter state of the target object corresponding to the at least one of the abnormal analysis parameters includes:

if the analysis data of the abnormal analysis parameters does not meet the abnormal threshold corresponding to the abnormal analysis parameters, determining that the target object is in a normal state in the abnormal analysis parameters;

and obtaining the parameter state corresponding to at least one abnormal analysis parameter respectively.

8. The method of any one of claims 1-7, further comprising:

acquiring at least one object set by a user, and generating an object analysis request corresponding to the at least one object;

transmitting the object analysis requests to at least one of the financial institutions, respectively; the object analysis request instructs the financial institution to authorize object analysis of the object;

and acquiring object analysis authorization of at least one financial institution to at least one object respectively.

9. The method of claims 1-8, further comprising:

determining a data analysis request when data analysis is carried out on at least one object according to a multi-level object management strategy;

respectively sending the data analysis request to at least one financial institution, wherein the data analysis request is used for indicating the financial institution to carry out data analysis authorization of operation data analysis confirmation on at least one object;

10. The method according to claims 1-9, wherein the outputting the result of the anomaly analysis of the target object comprises:

and sending the abnormal analysis result of the target object to at least two financial institutions of which account information exists in the target object.

11. A method of data analysis, comprising:

receiving account information respectively corresponding to at least one object respectively sent by at least two financial institutions; account information corresponding to the at least one object respectively indicates that the electronic equipment feeds back by the at least one financial institution;

and sending the target object with account information in any two financial institutions to the electronic equipment so that the electronic equipment can obtain the operation data of the target object corresponding to at least two financial institutions respectively, and performing exception analysis processing on the target object to obtain an exception analysis result.

12. The method of claim 11, wherein sending the target object with account information for any two financial institutions to the electronic device comprises:

sending the object encryption information to the electronic equipment; and the object encryption indicates the electronic equipment to decrypt the object encryption information, so that the target object with account information existing in any two financial institutions is obtained.

13. The method of claim 11 or 12, wherein the performing an account information intersection analysis on two of the at least two financial institutions comprises:

14. A data analysis apparatus, comprising:

15. A data analysis apparatus, comprising:

the object sending unit is used for sending a target object with account information of any two financial institutions to the electronic equipment so that the electronic equipment can obtain the operation data of the target object corresponding to at least two financial institutions respectively, and performing exception analysis processing on the target object to obtain an exception analysis result.

16. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

17. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 11-13.

18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.

19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 11-13.

20. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 10.

21. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 11-13.