CN118365119A - Credit data processing method based on credit bureau alliance - Google Patents
Credit data processing method based on credit bureau alliance Download PDFInfo
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Abstract
The invention discloses a credit investigation data processing method based on credit investigation organization alliance, which relates to the field of data processing and comprises the following steps: setting a credit alliance platform, verifying the validity of a credit organization using the platform, setting a data transmission channel, and transmitting the acquired credit data through the data transmission channel; establishing a credit data pool, and carrying out duplication removal processing and integrity analysis on the credit data in the credit data pool; presetting a credit investigation period, acquiring credit investigation data in the credit investigation period, processing the credit investigation data, acquiring first risk data of the credit investigation period, counting the first risk data in the credit investigation period, and acquiring a change trend and corresponding second risk data of the credit investigation period; analyzing and processing the first risk data and the second risk data to obtain comprehensive risk data, packaging the obtained data information into credit investigation data packets, and outputting risk prediction results; the invention improves the accuracy of credit investigation data processing to a certain extent.
Description
Technical Field
The invention relates to the field of data processing, in particular to a credit investigation data processing method based on credit investigation organization alliance.
Background
Credit investigation means that a specialized and independent third party organization establishes a credit file for a person or an enterprise, collects and objectively records credit information of the person or the enterprise according to law, and provides credit information service according to law, thereby helping personnel judge whether the corresponding person or enterprise is reliable or not; with the development of society, more and more fields will be applied to personal credit, so credit is closely related to our daily life, and the data processing method of credit is also important.
A credit investigation data processing method and electronic equipment with the publication number of CN110765484B disclose a credit investigation data processing method and electronic equipment, the method comprises the following steps: after receiving a credit inquiry request, analyzing the credit inquiry request to generate a corresponding analysis result, and acquiring a corresponding service scene according to the analysis result; invoking a corresponding decision configuration rule according to the service scene, and acquiring credit investigation data corresponding to the credit investigation request according to the decision configuration rule, wherein the step of acquiring the credit investigation data from at least one preset credit investigation server through a preset interface; and generating corresponding customized data according to the customized requirements from the acquired credit investigation data so as to respond to the credit investigation inquiry request. The method can formulate the query strategy according to the actual condition of the credit inquiry request, and can perform adaptive data interaction with the preset credit inquiry server, so that the processing efficiency is improved, and meanwhile, the stability and the order of the whole system are ensured.
A method and a device for realizing credit investigation data processing with the publication number of CN108230137B disclose a method and a system for realizing credit investigation data processing, comprising the following steps: acquiring transaction data of a merchant, and performing anomaly judgment on the acquired transaction data according to a preset anomaly judgment strategy; when judging that the transaction data is abnormal, acquiring transaction related information related to the abnormal transaction data; and determining whether to adjust the merchant credit according to the acquired transaction data and transaction association information associated with the transaction data, and adjusting the merchant credit when determining to adjust the merchant credit. According to the embodiment of the invention, whether the transaction of the merchant is abnormal or not is judged through the transaction data and the transaction related information, and the abnormal analysis is used as a basis for adjustment of the credit of the merchant, so that the counterfeit of the transaction data of the merchant is avoided, and the accuracy of the credit information is improved.
At present, in the processing process of the credit information data of people, only the credit information data of individual institutions similar to banks is insufficient, but the credit information data of the banks is not circulated with the credit information data of other credit information institutions, so that the island of the credit information data is caused, and the accuracy in the processing process of the credit information data is reduced, therefore, how to increase the data quantity of the credit information data of users and the accuracy in the processing process of the credit information data are problems which need to be solved, and the credit information data processing method based on the credit information institution alliance is provided.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a credit data processing method based on a credit organization alliance.
The aim of the invention can be achieved by the following technical scheme: a credit information data processing method based on a credit information organization alliance comprises the following steps:
Step S1: setting a credit alliance platform, verifying legality of a credit organization using the platform, setting a data transmission channel for the credit organization passing verification, uploading corresponding credit data by the credit organization through the data transmission channel according to the platform demand, and marking the input credit data;
step S2: establishing a credit data pool, and performing duplication removal processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel;
step S3: analyzing and processing credit data in a credit data pool, presetting a credit period, acquiring first risk data corresponding to each credit data in the credit period, carrying out statistical analysis on the acquired first risk data, acquiring a variation trend of the first risk data, and acquiring second risk data according to the variation trend of the first risk data;
Step S4: and analyzing and processing the obtained first risk data and second risk data, obtaining comprehensive risk data corresponding to the identity information of the credit investigation person, packaging the obtained data information into a credit investigation data packet, and outputting a risk prediction result.
Further, the process of setting the credit alliance platform, verifying the validity of the credit organization using the platform, and setting the data transmission channel for the credit organization passing the verification comprises the following steps:
Setting a credit alliance platform which comprises a credit alliance authentication window, and uploading a credit alliance application by a credit alliance organization using the credit alliance platform according to enterprise information of the credit alliance; the credit alliance application comprises registration information, qualification authentication information, record information of a supervision organization and compliance examination information; and auditing the obtained credit alliance application, if the audit passes, granting the credit organization permission to enter the credit alliance platform, setting a data transmission channel for the credit organization, and marking the data transmission channel according to the credit organization.
Further, the process of uploading the corresponding credit investigation data by the credit investigation mechanism through the data transmission channel according to the platform requirement and marking the input credit investigation data comprises the following steps:
The credit investigation alliance platform is internally provided with an instruction receiving window which is used for receiving credit investigation acquisition instructions sent by staff in the platform, wherein the credit investigation acquisition instructions comprise credit investigation personnel identity information; the acquired credit acquisition instructions are verified, if verification is successful, the credit acquisition instructions are sent to all credit mechanisms in the credit alliance platform, corresponding credit data are acquired by each credit mechanism, and the credit data are marked, wherein the acquisition time, the credit investigation identity information and the credit mechanisms are included; and uploading the collected credit investigation data to a credit investigation alliance platform through a data transmission channel.
Further, the process of establishing the credit data pool and performing de-duplication processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel includes:
A credit data pool is built in a credit alliance platform, a central credit data pool and a peripheral credit data pool are arranged in the credit data pool, and the central credit data pool is used for storing collected credit data subjected to duplicate removal processing; the peripheral credit data pool is used for temporarily storing collected credit data which is not subjected to reprocessing; acquiring credit investigation personnel identity information, marking the credit investigation personnel identity information as a key acquisition characteristic, and acquiring a credit investigation data set corresponding to the credit investigation personnel identity information in a central credit investigation data pool through an SQL compiling query algorithm according to the key acquisition characteristic; acquiring credit information marked as credit investigation person identity information in a peripheral credit information data pool, comparing the credit information with credit information contained in a corresponding credit information data set in a central credit information data pool one by one, sequentially acquiring hash values of the corresponding credit information data through a hash algorithm, if the hash values are inconsistent, marking the credit information as non-repeated data, transferring the credit information from the peripheral credit information data pool to the central credit information pool, updating a credit information set corresponding to the credit investigation person identity information, adding the credit information into the credit information set, sequentially repeating comparison operation until the credit information corresponding to the credit investigation person identity information does not exist in the peripheral credit information pool, and finishing duplication removal of the credit information.
Further, a fixed data set element occupation of the credit information data is arranged in the credit information alliance platform, the data set element occupation corresponds to the credit information data of different types contained in each credit information period one by one, the credit information data in the credit information data set is matched with each data set element occupation, and if the data set element occupation arranged in the platform is successfully matched, the credit information data set is complete; if the occupation of each data set element set in the platform is not successfully matched, the credit investigation data set is incomplete, a repeated acquisition instruction is generated and sent to each credit investigation organization in the credit investigation alliance platform, and the repeated acquisition instruction is acquired again by the credit investigation organization.
Further, the process of analyzing the credit data in the credit data pool, presetting a credit period, and obtaining first risk data corresponding to each credit data in the credit period includes:
Acquiring historical credit information data corresponding to different types of credit information data in the credit information alliance platform, generating a historical credit information data set of the corresponding types of credit information data, analyzing and training the historical credit information data set through a natural language algorithm according to the historical credit information data set, and constructing a key feature extraction model; presetting credit investigation periods, and acquiring credit investigation data corresponding to the identity information of a corresponding credit investigation person in each credit investigation period; inputting the obtained credit investigation data into a key feature extraction model of a corresponding type, outputting key feature data of the credit investigation data of the corresponding type, analyzing and processing the obtained key feature data, and obtaining first risk data in a credit investigation period;
The risk level interval is preset, wherein the risk level interval comprises a low risk interval, a medium risk interval and a high risk interval respectively, the obtained first risk data are subjected to comparison analysis on the corresponding risk level interval respectively, the risk level of the risk level interval is obtained according to the risk level interval to which the first risk data belong in the credit investigation period, and a first risk coefficient is set according to the risk level of the risk level interval.
Further, the process of performing statistical analysis according to the obtained first risk data to obtain a variation trend thereof and obtaining second risk data thereof according to the variation trend thereof includes:
Acquiring first risk data in each credit period, setting each credit period as a unit time, constructing a first risk image according to the credit period corresponding to the identity information of a credit investigation person and the corresponding first risk data in the credit period, dividing continuous unit time according to the change trend of the first risk data in the first risk image, setting the continuous unit time as a continuous unit set when the change trend of adjacent unit time in the first risk image is consistent, marking the initial unit time as the lower limit of the continuous unit set, and marking the termination unit time as the upper limit of the continuous unit set; acquiring the variation trend of each first risk data in each continuous unit set; the change trend is obtained according to the difference value of the lower limit and the upper limit in the continuous unit set in the corresponding unit time; and acquiring the change trend of each continuous unit set, and recording the change trend as second risk data.
Further, the process of analyzing the obtained first risk data and second risk data to obtain comprehensive risk data corresponding to the identity information of the corresponding credit investigation person, packaging the obtained data information into a credit investigation data packet, and outputting a risk prediction result includes:
Acquiring first risk data, first risk coefficients, second risk data and corresponding credit investigation periods and continuous unit sets thereof, acquiring the number of the continuous unit sets and the number of the credit investigation periods in the continuous unit sets corresponding to the credit investigation person identity information, analyzing and processing the acquired data information, acquiring an average value of the sum of the first risk data and the first risk coefficient products in each credit investigation period and an average value of the second risk data in each continuous unit set, wherein a second risk interval is preset, the second risk coefficient is acquired according to the second risk interval to which the average value belongs, and the sum of the products of the second risk data and the second risk coefficients in each continuous unit set is acquired; and analyzing and processing the obtained sum of the products and the average value of the sum of the first risk data and the first risk coefficient products to obtain comprehensive risk data, packaging data information generated in the platform into a credit investigation data packet, and outputting a risk prediction result.
Compared with the prior art, the invention has the beneficial effects that:
1. By setting a credit data pool, the credit data corresponding to the identity information of each credit investigation person obtained by using the platform credit institution is subjected to comparison analysis, and duplicate removal processing and integrity analysis are performed, so that the accuracy in the credit data processing process is improved to a certain extent;
2. By setting the credit investigation period, the credit investigation data in the credit investigation period is analyzed and processed to obtain the first risk data, and the analysis and processing are carried out according to the variation trend of the obtained first risk data to obtain the second risk data, so that the comprehensive risk data is obtained according to the first risk data and the second risk data, and the credit investigation is analyzed and processed according to the credit investigation data and the variation trend, so that the scientificity of the user in the credit investigation analysis process is improved.
Drawings
FIG. 1 is a schematic diagram of a credit data processing method based on a credit agency alliance in an embodiment of the application.
Detailed Description
As shown in fig. 1, the credit data processing method based on the credit agency alliance comprises the following steps:
Step S1: setting a credit alliance platform, verifying legality of a credit organization using the platform, setting a data transmission channel for the credit organization passing verification, uploading corresponding credit data by the credit organization through the data transmission channel according to the platform demand, and marking the input credit data;
step S2: establishing a credit data pool, and performing duplication removal processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel;
step S3: analyzing and processing credit data in a credit data pool, presetting a credit period, acquiring first risk data corresponding to each credit data in the credit period, carrying out statistical analysis on the acquired first risk data, acquiring a variation trend of the first risk data, and acquiring second risk data according to the variation trend of the first risk data;
Step S4: and analyzing and processing the obtained first risk data and second risk data, obtaining comprehensive risk data corresponding to the identity information of the credit investigation person, packaging the obtained data information into a credit investigation data packet, and outputting a risk prediction result.
The method for setting the credit alliance platform, verifying the validity of the credit organization using the platform, setting a data transmission channel for the credit organization passing the verification, uploading corresponding credit data by the credit organization through the data transmission channel according to the platform demand, and marking the input credit data comprises the following steps:
Setting a credit alliance platform, wherein a credit alliance authentication window is set for a credit alliance, and a credit organization needing to use the credit alliance platform uploads a credit alliance application according to enterprise information, wherein the credit alliance application comprises registration information, qualification authentication information, supervision organization record information and compliance examination information; auditing the obtained credit investigation alliance application, wherein a corresponding professional supervision team is arranged in the credit investigation alliance platform, the obtained credit investigation alliance application is sent to the professional supervision team, and the professional supervision team performs auditing; if the verification passes, the authority of the credit bureau for entering the credit alliance platform is granted, a data transmission channel is set for the credit bureau, and the data transmission channel is marked according to the credit bureau; if the verification is not passed, the authority of the credit alliance to enter the credit alliance platform is not granted;
It should be further noted that, in the implementation process, the information included in the credit alliance application is: the registration information is the registration name, legal representative and contact way of the credit investigation organization, and the qualification authentication information is the authentication passing through the national financial management organization or the related authorities so as to ensure the certification information meeting the legal requirements and standards of the credit investigation organization; the supervision and management organization record information is supervision report and annual report information obtained by periodically checking and supervising the credit bureau by the supervision and management organization; the compliance censoring information is a report issued by a related regulatory agency or organization, and is information which proves that the compliance censoring information meets the compliance requirement.
The credit investigation alliance platform is internally provided with an instruction receiving window which is used for receiving credit investigation acquisition instructions sent by staff in the platform, wherein the credit investigation acquisition instructions comprise credit investigation person identity information, credit investigation person identity evidence and application books authorized by the credit investigation person; performing verification processing on the acquired credit collection instruction, and if verification is successful, sending the credit collection instruction to all credit institutions in the credit alliance platform, and collecting corresponding credit data by each credit institution;
the credit alliance platform is internally provided with a credit data pool, the data transmission channel is in communication connection with a credit organization and a credit data pool which correspond to the marks, the credit organization is used for collecting credit data of corresponding credit investigation persons according to credit collection instructions, the collected credit data are marked, the marks comprise credit data obtaining time and credit organization names, and the credit data are transmitted to the credit data pool in the credit alliance platform through the data transmission channel;
It should be further noted that, in the specific implementation process, a multi-channel list cooperating with the credit organization is provided in the credit organization in the credit alliance platform, where the multi-channel list includes each credit data source channel authenticated by the regulatory department, and corresponding credit data in the multi-channel list is sequentially obtained through a database query technology, where the credit data includes but is not limited to public data, payment data and financial data; the advertising data includes legal litigation, decision making, bankruptcy records, etc. of individuals or institutions; the financial data comprises credit lending behaviors of individuals or institutions, including loan application, loan amount, loan deadline, repayment records and the like, and credit card service conditions of individuals, such as credit line, bill repayment conditions, overdue records and the like; the payment records comprise payment behaviors of individuals or institutions, such as mobile phone payment, hydropower payment conditions and the like.
The process for establishing the credit data pool and carrying out de-duplication processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel comprises the following steps:
The central credit data pool is used for storing collected credit data subjected to duplication removal processing; the peripheral credit data pool is used for temporarily storing collected credit data which is not subjected to reprocessing; acquiring credit investigation person identity information in a credit collection instruction generated in a credit alliance platform, recording the acquired credit investigation person identity information as a key collection feature, acquiring a credit investigation data set corresponding to the credit investigation person identity information in a central credit investigation data pool through an SQL compiling query algorithm according to the key collection feature, and acquiring the credit investigation data set;
The peripheral credit data pool stores the credit data acquired by the credit organization in the credit alliance platform, acquires the credit data marked as the identity information of the credit investigation person, compares the credit data with the credit data contained in the credit data set consistent with the corresponding credit investigation person identity information in the central credit data pool one by one, sequentially acquires the hash value of the corresponding credit data through a hash algorithm, judges whether the credit data are consistent, marks the credit data as repeated data if the credit data are consistent, deletes the credit data, marks the credit data as non-repeated data if the credit data are inconsistent, transfers the credit data from the peripheral credit data pool to the central credit data pool, updates the credit data set corresponding to the credit investigation person identity information, and adds the credit data into the credit data set; the operation is repeated in sequence until the peripheral credit investigation data pool has no credit investigation data corresponding to the corresponding credit investigation personnel identification information, and the operation is stopped;
It should be further noted that, in the specific implementation process, the hash value of the credit information data is obtained through a hash algorithm, and the steps are as follows: preprocessing the obtained credit investigation data, converting the credit investigation data into data, filling the data into multiples of 512 in length, and adding data length; setting a hash initial value and recording the hash initial value as HC; dividing data into 512-bit data blocks, performing iterative processing on the obtained 512 data blocks to generate 128-bit output, and connecting output results of each data block to obtain a final hash value.
Acquiring a credit investigation data set corresponding to the credit investigation person identity information in a central credit investigation data pool; the method comprises the steps that a fixed data set element occupation of credit data is arranged in a credit alliance platform, the data set element occupation corresponds to credit data of different types contained in each credit period one by one, credit data in the credit data set are matched with each data set element occupation, if matching is successful, each data set element occupation arranged in the platform is successfully matched, the credit data set is complete, if matching is unsuccessful, or each data set element occupation arranged in the platform is unsuccessful, the credit data set is incomplete, a repeated acquisition instruction is generated and sent to each credit organization in the credit alliance platform, and the repeated acquisition instruction is acquired again by each credit organization, so that the steps are repeated;
it should be further noted that, in the specific implementation process, the credit investigation is performed on the credit investigation data in the credit investigation data set through the occupation of the data set element, and whether the deletion or repetition exists is judged, so that the accuracy in the data processing process is improved.
The process of analyzing the credit data in the credit data pool, presetting a credit period, acquiring first risk data corresponding to each credit data in the credit period, carrying out statistical analysis on the acquired first risk data, acquiring a change trend, and acquiring second risk data according to the change trend comprises the following steps:
acquiring historical credit information data corresponding to different types of credit information data in the credit information alliance platform, generating a historical credit information data set of the corresponding types of credit information data, dividing the acquired historical credit information data set into a training set and a verification set, analyzing and processing the training set based on a natural language algorithm, constructing a key feature extraction model, inputting the verification set into the constructed key feature extraction model, outputting a result, verifying the obtained result, acquiring a verification passing rate, presetting a verification passing rate threshold, comparing and analyzing the verification passing rate and the verification passing rate threshold, and outputting the key feature extraction model when the verification passing rate is larger than or equal to the verification passing rate threshold;
Presetting credit investigation periods, and acquiring credit investigation data corresponding to the identity information of a corresponding credit investigation person in each credit investigation period; inputting the obtained credit investigation data into a key feature extraction model of a corresponding type, outputting key feature data of the credit investigation data of the corresponding type, analyzing and processing the obtained key feature data, and obtaining first risk data in a credit investigation period;
The process for analyzing and processing the obtained key characteristic data comprises the following steps: acquiring the type of credit information data, and marking key characteristic data corresponding to the credit information data as ZX ij; wherein i=1, 2 … … n are respectively different credit types, j=1, 2, 3 … … m, and are set according to corresponding unit nodes in the credit period; the first risk data is noted as FY ij; presetting a credit function and an initial coefficient, and respectively marking the credit function and the initial coefficient as F ij and CS ij; wherein,
Presetting a risk level interval, comprising a low risk interval, a medium risk interval and a high risk interval, respectively carrying out comparative analysis on the obtained first risk data to the corresponding risk level interval, obtaining the risk level of the first risk data according to the risk level interval to which the first risk data belongs in a credit investigation period, and setting a first risk coefficient according to the risk level;
Acquiring first risk data in each credit period, setting each credit period as a unit time, carrying out data visualization analysis according to the credit period corresponding to the credit investigation person identity information and the first risk data corresponding to the credit period, constructing a first risk image, dividing continuous unit time according to the change trend of the first risk data in the first risk image, setting the continuous unit time as a continuous unit set when the change trend of adjacent unit time in the first risk image is consistent, marking the initial unit time as the lower limit of the continuous unit set, and marking the termination unit time as the upper limit of the continuous unit set; acquiring the variation trend of each first risk data in each continuous unit set; the change trend is obtained according to the difference value of the lower limit and the upper limit in the continuous unit set in the corresponding unit time; analyzing and processing the first risk data in the first risk image to acquire the variation trend of each continuous unit set, and recording the variation trend as second risk data;
It should be further noted that, in the implementation process, the second risk data may be positive number, negative number and 0, which are obtained according to the trend of the variation of the first risk data in the continuous unit set, and the obtained second risk data are marked according to the corresponding continuous unit set;
The process of analyzing and processing the obtained first risk data and second risk data, obtaining comprehensive risk data corresponding to the identity information of the corresponding credit investigation person, packaging the obtained data information into a credit investigation data packet, and outputting a risk prediction result comprises the following steps:
Acquiring first risk data, second risk data and corresponding credit investigation periods and continuous unit sets thereof, acquiring the number of the credit investigation periods in the continuous unit sets, and marking the number as G; acquiring a first risk coefficient according to the risk grade of the first risk data in the credit investigation period, and marking the first risk coefficient as YX; wherein the first risk data corresponds to FY; recording corresponding second risk data in each continuous unit set as FE; setting the number of continuous unit sets corresponding to the identity information of the credit investigation person as L; the comprehensive risk data is obtained according to the obtained first risk data and second risk data;
It should be further noted that, in the implementation process, the average value of the second risk data in each continuous unit set is obtained and recorded as EJ; Presetting a second risk interval of second risk data, dividing the second risk interval into a credit optimization interval, a credit stable interval and a credit degradation interval respectively, comparing and analyzing the second risk data with the second risk interval to obtain a second risk interval to which the second risk data belongs, setting a second risk coefficient according to the second risk interval, marking the second risk coefficient as EX, and obtaining the sum of products of the second risk data and the second risk coefficient in each continuous unit set; acquiring comprehensive risk data by using the sum of the obtained products and the average value set by the first risk data;
the comprehensive risk data is marked as FZ; wherein the method comprises the steps of
Packaging credit investigation data, first risk data, second risk coefficients and corresponding first risk images corresponding to the identity information of the credit investigation person into a credit investigation data packet, and setting the credit investigation data packet as a risk prediction result;
The credit alliance platform is internally provided with a credit assessment model, the credit assessment model is obtained by analyzing and training historical credit data packages in the platform, staff in the credit alliance platform randomly selects corresponding credit data packages and inputs the corresponding credit data packages into the credit assessment model, an assessment result is output, the comprehensive risk data obtained by the assessment result and the comprehensive risk data in the credit data packages are compared and analyzed, if the comprehensive risk data are consistent, the assessment is passed, and if the comprehensive risk data are inconsistent, the credit data packages are reprocessed.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The credit data processing method based on the credit organization alliance is characterized by comprising the following steps:
Step S1: setting a credit alliance platform, verifying legality of a credit organization using the platform, setting a data transmission channel for the credit organization passing verification, uploading corresponding credit data by the credit organization through the data transmission channel according to the platform demand, and marking the input credit data;
Step S2: establishing a credit data pool, and performing duplication removal processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel; presetting a credit investigation data integrity rule, and acquiring the integrity of credit investigation data corresponding to the credit investigation person;
step S3: analyzing and processing credit data in a credit data pool, presetting a credit period, acquiring first risk data corresponding to each credit data in the credit period, carrying out statistical analysis on the acquired first risk data, acquiring a variation trend of the first risk data, and acquiring second risk data according to the variation trend of the first risk data;
Step S4: and analyzing and processing the obtained first risk data and second risk data, obtaining comprehensive risk data corresponding to the identity information of the credit investigation person, packaging the obtained data information into a credit investigation data packet, and outputting a risk prediction result.
2. The credit data processing method based on credit agency alliance according to claim 1, wherein the process of setting a credit agency platform, verifying the validity of the credit agency using the platform, and setting a data transmission channel for the credit agency passing the verification comprises:
Setting a credit alliance platform which comprises a credit alliance authentication window, and uploading a credit alliance application by a credit alliance organization using the credit alliance platform according to enterprise information of the credit alliance; the credit alliance application comprises registration information, qualification authentication information, record information of a supervision organization and compliance examination information; and auditing the obtained credit alliance application, if the audit passes, granting the credit organization permission to enter the credit alliance platform, setting a data transmission channel for the credit organization, and marking the data transmission channel according to the credit organization.
3. The credit data processing method based on the credit agency alliance according to claim 2, wherein the process of uploading corresponding credit data through a data transmission channel and marking the input credit data by the credit agency according to the platform requirement comprises the following steps:
The credit investigation alliance platform is internally provided with an instruction receiving window which is used for receiving credit investigation acquisition instructions sent by staff in the platform, wherein the credit investigation acquisition instructions comprise credit investigation personnel identity information; the acquired credit acquisition instructions are verified, if verification is successful, the credit acquisition instructions are sent to all credit mechanisms in the credit alliance platform, corresponding credit data are acquired by each credit mechanism, and the credit data are marked, wherein the acquisition time, the credit investigation identity information and the credit mechanisms are included; and uploading the collected credit investigation data to a credit investigation alliance platform through a data transmission channel.
4. The credit data processing method based on the credit agency alliance according to claim 3, wherein the process of establishing the credit data pool and performing deduplication processing on the credit data in the credit data pool according to the mark of the credit data uploaded by the data transmission channel comprises the following steps:
A credit data pool is built in a credit alliance platform, a central credit data pool and a peripheral credit data pool are arranged in the credit data pool, and the central credit data pool is used for storing collected credit data subjected to duplicate removal processing; the peripheral credit data pool is used for temporarily storing collected credit data which is not subjected to reprocessing; acquiring credit investigation personnel identity information, marking the credit investigation personnel identity information as a key acquisition characteristic, and acquiring a credit investigation data set corresponding to the credit investigation personnel identity information in a central credit investigation data pool through an SQL compiling query algorithm according to the key acquisition characteristic; acquiring credit information marked as credit investigation person identity information in a peripheral credit information data pool, comparing the credit information with credit information contained in a corresponding credit information data set in a central credit information data pool one by one, sequentially acquiring hash values of the corresponding credit information data through a hash algorithm, if the hash values are inconsistent, marking the credit information as non-repeated data, transferring the credit information from the peripheral credit information data pool to the central credit information pool, updating a credit information set corresponding to the credit investigation person identity information, adding the credit information into the credit information set, sequentially repeating comparison operation until the credit information corresponding to the credit investigation person identity information does not exist in the peripheral credit information pool, and finishing duplication removal of the credit information.
5. The credit data processing method based on credit organization alliance according to claim 4, wherein a fixed data set element occupation of the credit data is arranged in the credit alliance platform, the data set element occupation corresponds to the credit data of different types contained in each credit period one by one, the credit data in the credit data set is matched with each data set element occupation, and if the data set element occupation arranged in the platform is successfully matched, the credit data set is complete; if the occupation of each data set element set in the platform is not successfully matched, the credit investigation data set is incomplete, a repeated acquisition instruction is generated and sent to each credit investigation organization in the credit investigation alliance platform, and the repeated acquisition instruction is acquired again by the credit investigation organization.
6. The credit data processing method based on credit organization alliance according to claim 5, wherein the process of analyzing and processing the credit data in the credit data pool, presetting a credit period, and obtaining first risk data corresponding to each credit data in the credit period includes:
Acquiring historical credit information data corresponding to different types of credit information data in the credit information alliance platform, generating a historical credit information data set of the corresponding types of credit information data, analyzing and training the historical credit information data set through a natural language algorithm according to the historical credit information data set, and constructing a key feature extraction model; presetting credit investigation periods, and acquiring credit investigation data corresponding to the identity information of a corresponding credit investigation person in each credit investigation period; inputting the obtained credit investigation data into a key feature extraction model of a corresponding type, outputting key feature data of the credit investigation data of the corresponding type, analyzing and processing the obtained key feature data, and obtaining first risk data in a credit investigation period;
The risk level interval is preset, wherein the risk level interval comprises a low risk interval, a medium risk interval and a high risk interval respectively, the obtained first risk data are subjected to comparison analysis on the corresponding risk level interval respectively, the risk level of the risk level interval is obtained according to the risk level interval to which the first risk data belong in the credit investigation period, and a first risk coefficient is set according to the risk level of the risk level interval.
7. The credit data processing method based on credit agency federation according to claim 6, wherein the process of performing statistical analysis based on the obtained first risk data to obtain a variation trend thereof and obtaining second risk data thereof based on the variation trend thereof comprises:
Acquiring first risk data in each credit period, setting each credit period as a unit time, constructing a first risk image according to the credit period corresponding to the identity information of a credit investigation person and the corresponding first risk data in the credit period, dividing continuous unit time according to the change trend of the first risk data in the first risk image, setting the continuous unit time as a continuous unit set when the change trend of adjacent unit time in the first risk image is consistent, marking the initial unit time as the lower limit of the continuous unit set, and marking the termination unit time as the upper limit of the continuous unit set; acquiring the variation trend of each first risk data in each continuous unit set; the change trend is obtained according to the difference value of the lower limit and the upper limit in the continuous unit set in the corresponding unit time; and acquiring the change trend of each continuous unit set, and recording the change trend as second risk data.
8. The credit data processing method based on credit agency alliance according to claim 7, wherein the process of analyzing the obtained first risk data and second risk data to obtain comprehensive risk data corresponding to the identity information of the corresponding credit investigator, and packaging the obtained data information into a credit data packet, and outputting the risk prediction result includes:
Acquiring first risk data, first risk coefficients, second risk data and corresponding credit investigation periods and continuous unit sets thereof, acquiring the number of the continuous unit sets and the number of the credit investigation periods in the continuous unit sets corresponding to the credit investigation person identity information, analyzing and processing the acquired data information, acquiring an average value of the sum of the first risk data and the first risk coefficient products in each credit investigation period and an average value of the second risk data in each continuous unit set, wherein a second risk interval is preset, the second risk coefficient is acquired according to the second risk interval to which the average value belongs, and the sum of the products of the second risk data and the second risk coefficients in each continuous unit set is acquired; and analyzing and processing the obtained sum of the products and the average value of the sum of the first risk data and the first risk coefficient products to obtain comprehensive risk data, packaging data information generated in the platform into a credit investigation data packet, and outputting a risk prediction result.
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