CN110532131A - A kind of processing method of cross check, intelligent questionnaire data processing method - Google Patents
A kind of processing method of cross check, intelligent questionnaire data processing method Download PDFInfo
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Abstract
The present invention relates to processing data information technical fields, processing method, intelligent questionnaire data processing method more particularly to a kind of cross check, the processing method of one of cross check, wherein, it include: to choose any one of target data in the state of establishing the incidence relation having between each target data and data source to form verification object；Data are verified according to incidence relation reading and the associated data source of verification object, and according to the data source basis of formation；Arrangement processing is carried out to form a rank results to the basis verification data, the verification object；Determine that the target data is true in the state that the rank results match a predetermined threshold.
Description
Technical field
The present invention relates to processing data information technical fields, processing method, intelligence more particularly to a kind of cross check
Questionnaire data processing method.
Background technique
The starting point of the small micro- finance of China Businessization is in 2005, undertaken derived from World Bank's initiation, State Development Bank,
German IPC company provides [the small wechat credit item mesh of State Development Bank] that skill is helped.The project will be commercialized small wechat for the first time and borrow
Air control technology introduces China, supports 18 municipal commercial banks in total under the project and Rural Commercial Bank carries out small wechat
Loan business and the small micro- credit technique of IPC is propagated, leader Taizhou bank, concessionaire bank, Chongqing bank for example including the field,
Kweiyang bank, Anhui Maanshan agriculture firm etc..From 2008, a large amount of municipal commercial bank, Rural Commercial Bank, rural area letter
The small wechat of IPC is introduced and propagated with association, little Dai company and borrows air control technology, which becomes each financial institution and carry out small wechat
The core air control technology of loan business.
But existing IPC technology has the following deficiencies, and is mainly reflected in, the data validity that questionnaire obtains
To be improved, the data obtained in questionnaire are the oral account data or lonely data of client, give an oral account data or lonely data
In the case where the corresponding evidence of no third party offer gives secondary proof, the common true and false of the data can not be verified directly, and one
Denier client provide false data after, based on the false data be formed by credit analysis, credit examination & approval exist it is biggish
Risk, once loan origination, then there are greater risks for this loan repayment ability.
Summary of the invention
In view of the above-mentioned problems, the present invention provides the processing method and intelligent questionnaire data processing method of a kind of cross check.
On the one hand, the present invention provides a kind of processing method of cross check, wherein includes:
In the state of establishing the incidence relation having between each target data and data source, any one of mesh is chosen
Data are marked to form verification object；
According to incidence relation reading and the associated data source of verification object, and according to the data source shape
Data are verified at basis；
Arrangement processing is carried out to form a rank results to the basis verification data, the verification object；
Determine that the target data is true in the state that the rank results match a predetermined threshold.
Preferably, the processing method of above-mentioned a kind of cross check, wherein to basis the verification data, the verification
Object carries out arrangement processing and is specifically included with forming a rank results:
The basis verification data, the verification object are calculated, arrange processing to form an arrangement data；
Discrete calculation processing is done to form a dispersion degree index to the arrangement data；
The rank results are formed according to the dispersion degree index.
Preferably, the processing method of above-mentioned a kind of cross check, wherein further include:
Determine that the target data is false in the state that the rank results mismatch the predetermined threshold.
On the other hand, the present invention provides a kind of intelligent questionnaire data processing method again, wherein including,
Establish the matching relationship between fisrt feature data and output data；
Receive the externally input and matched first kind target data of current output data, according to the first kind number of targets
According to one fisrt feature data of formation；
According to next output data of the fisrt feature reading data and the fisrt feature Data Matching.
Preferably, above-mentioned a kind of intelligent questionnaire data processing method, in which: further include,
The first kind target data is read, one first verification data output is formed according to the first kind target data.
Preferably, above-mentioned a kind of intelligent questionnaire data processing method, in which: further include,
Read with described first matched each output data of verification data and with output data described in each
The first kind target data matched verifies data with basis of formation；
Arrangement processing is done to form a rank results to the basis verification data；
Determine in the state that the rank results match a predetermined threshold and the quasi- objective result matched described the
A kind of target data is true.
Preferably, above-mentioned a kind of intelligent questionnaire data processing method, in which: further include,
Determine in the state that the rank results match a predetermined threshold and the quasi- objective result matched described the
A kind of target data is false.
Compared with prior art, the beneficial effect of the disclosure is:
In above-described embodiment, when being verified to a target data, read and the verification object according to incidence relation
The associated data source, and data are verified according to the data source basis of formation, pass through at least one basis verification data pair
Target data is verified, specifically, the data source of a target data have it is multiple, by multiple data sources to a certain data
It is verified, the reliability of data source can be improved, while multiple data sources directly do not verify target data, but
Difference degree or dispersion degree between data are calculated by the method for calculation processing, goes to discriminate by difference degree or dispersion degree
The authenticity of other data itself.The true of single lonely data is improved in the case where no third party evidence by the above method
Reality and reliability, it is intended to provide authentic and valid data for other links of credit, reduce the risk of the processes such as credit analysis.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of the processing method of cross check in the embodiment of the present invention；
Fig. 2 is a kind of flow diagram of the processing method of cross check in the embodiment of the present invention；
Fig. 3 is a kind of flow diagram of intelligent questionnaire data processing method in the embodiment of the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure.
Embodiment one
On the one hand, the present invention provides a kind of processing method of cross check, wherein include: as shown in Figure 1,
Step S110, it in the state that Yu Jianli has the incidence relation between each target data and data source, chooses any
One target data is to form verification object.Verification object selects to be arbitrary target data, but in practical application
In, verify to each target data that its workload is relatively large, therefore can choose comparatively important data and give
With verification, the present embodiment can verify each target data, only can also give school time to part of data, about
The selection of inspection data is not particularly limited herein.
Wherein, target data can establish incidence relation between at least one data source.The target data can be regarded as
Master data needed for credit analytic process, for example, in credit analytic process, such as enterprise customer A is intended to apply 5,000,000 letter
Amount is borrowed, this loan limit is based on, judges whether enterprise customer A has the ability for repaying the loan limit, it is assumed that is done business with year
Profit is as reference, and the questionnaire data middle age operating profit part that user provides is 10,000,000 RMB, then 10,000,000 people
Coin is then regarded as target data, or can be described as the master data of credit analysis；Data source can for user it is practical it is providing with
The matched answer of questionnaire topic includes at least for example, the year operating profit data for enterprise are obtained about the data
Three problems, questionnaire topic one are " the average operating profit of enterprise monthly is how many ", and the answer that client provides is " 1,000,000 ",
So being somebody's turn to do " monthly average operating profit 1,000,000 " can be used as data source one；Topic two is that " the daily average operating profit of enterprise is more
It is few ", the answer that client provides is " 300,000 ", then being somebody's turn to do " per day operating profit 300,000 " can be used as data source two；Topic three is
" the average operating profit of enterprise quarterly is how many ", the answer that client provides are " 2,600,000 ", then should " season operating profit
2600000 " it can be used as data source three.
Step S120, according to incidence relation reading and the associated data source of verification object, and according to institute
State data source basis of formation verification data；For example, when there are three data sources in the incidence relation of verified target data
When, then three data sources provide data can basis of formation verify data, continue using " 10,000,000 RMB " as verify object,
The setting of target data corresponding to the verification object is there are three the data source that is associated, in above-mentioned case, " the moon battalion of data source one
Industry profit 1,000,000 ", data source two " day operating profit 30,000 ", data source three " season operating profit 2,600,000 " are then directly interpreted as
Basis verification data, can also be to data source one " moon operating profit 1,000,000 ", data source two " day operating profit 30,000 ", data source three
The data that " season operating profit 2,600,000 " provides calculate and basis of formation verification data, specifically: according to the one " moon of data source
The basis verification data one of operating profit 1,000,000 " formation " annual revenue 12000000 "；Data source two " day operating profit 30,000 "
Form the basis verification data two of " annual revenue 10950000 "；Data source three " season operating profit 2,600,000 " formation " Nian Yingye
Take in 10,400,000 " basis verification data three, be not particularly limited herein.
It should be understood that verification data in basis can be the data in addition to verifying object, or including verification
The selection of data including data, basis verification data is related with incidence relation setting, can be by number of targets in incidence relation setting
According to being also configured as with incidence relation, target data can not also be set to incidence relation, in the state that incidence relation is different,
The verification data that it is generated are also different, and two kinds of set-up modes of embodiment of the disclosure can be achieved, herein to incidence relation
Setting is not particularly limited.
Step S130, arrangement processing is carried out to form a rank results to the basis verification data, the verification object；
Specifically: as shown in Fig. 2,
Step S1301, to the basis verifies data, the verification object is calculated, it is in a row with shape to arrange processing
Column data；
Continue by taking above-mentioned case as an example, in above-mentioned case, with data source one " moon operating profit 1,000,000 ", two " day of data source
In the state that operating profit 30,000 ", data source three " season operating profit 2,600,000 " verify data directly as basis: basis verifies
Data include at least: data source one " moon operating profit 1,000,000 ", data source two " day operating profit 30,000 ", three " season of data source
Operating profit 2,600,000 "；First respectively to data source one " moon operating profit 1,000,000 ", data source two " day operating profit 30,000 ", number
Calculation processing is done according to source three " season operating profit 2,600,000 ", according to data source one " moon operating profit 1,000,000 " formation " Nian Yingye
Take in 12,000,000 " basis verification data one；The base of data source two " day operating profit 30,000 " formation " annual revenue 10950000 "
Plinth verifies data two；The basis verification data of data source three " season operating profit 2,600,000 " formation " annual revenue 10400000 "
Three；Arrangement processing is done based on " annual revenue 12000000 ", " annual revenue 10950000 ", " annual revenue 1040 " to be formed
One arrangement data " 10,000,000 ", " 10,950,000 ", " 10,400,000 ", " 12,000,000 ".
Step S1302, discrete calculation processing is done to form a dispersion degree index to the arrangement data；It is based respectively on
" 10,000,000 ", " 10,950,000 ", " 10,400,000 ", " 12,000,000 " stated are calculated to obtain a dispersion degree index, need to illustrate
, there are many forming methods of dispersion degree index, such as can be formed by coefficient of dispersion, can also pass through other methods shape
At herein the calculation method of dispersion degree index is not particularly limited.
Step S1303, the rank results are formed according to the dispersion degree index.
Step S140, Yu Suoshu rank results determine that the target data is true in the state of matching a predetermined threshold.
Predetermined threshold can be arranged according to concrete application, such as be arranged in a manner of percentage, be not particularly limited herein.
When the state of rank results matching predetermined threshold, it can determine that the basis verification data formed by multiple data sources
And/or the gap between verification data is smaller, when determining the verification data in the lesser situation of gap between multiple data
Validity is higher, then determines that the data are true.
Step S150, Yu Suoshu rank results determine that the target data is in the state of mismatching the predetermined threshold
It is false.
When the state of rank results mismatch predetermined threshold, it can determine that the basis verification data formed by multiple data sources
And/or the gap between verification data is larger, when determining the verification data in the biggish situation of gap between multiple data
Validity is lower, then determines that the data are false.
In above-described embodiment, when being verified to a target data, read and the verification object according to incidence relation
The associated data source, and data are verified according to the data source basis of formation, pass through at least one basis verification data pair
Target data is verified, specifically, the data source of a target data have it is multiple, by multiple data sources to a certain data
It is verified, the reliability of data source can be improved, while multiple data sources directly do not verify target data, but
Difference degree or dispersion degree between data are calculated by the method for calculation processing, goes to discriminate by difference degree or dispersion degree
The authenticity of other data itself.The true of single lonely data is improved in the case where no third party evidence by the above method
Reality and reliability.
Embodiment two
In existing credit authorization, asked by first making investigation questionnaire to applicant with the credit for the people that judges whether to accept applications
It asks.But existing questionnaire excessively standardizes, and cannot vary with each individual, i.e., different clients is led to after issuing credit request
Often need to answer identical problem, for example, client A personal credit to be applied, client A are still in singlehood, but according to mark
The process of quasi- questionnaire, it is still necessary to the information of inquiry spouse is carried out to client A, the human oriented design of this intelligent questionnaire just highlighted is not
It is enough.On the one hand this unified questionnaire reduces the investigation efficiency of customer manager, while also reducing the experience sense of user.
On the other hand, the present invention provides a kind of intelligent questionnaire data processing method again, wherein including, as shown in figure 3,
Step S210, the matching relationship between fisrt feature data and output data is established；For example, when auditor passes through
When mobile terminal first to file people shows questionnaire content, output data can be regarded as the investigation that mobile terminal is currently shown and ask
Topic.
Step S220, receive it is externally input with the matched first kind target data of current output data, according to described the
A kind of target data forms a fisrt feature data；First kind target data can be applicant based on the investigation problem currently shown
And the answer given, such as investigation problem is " whether you have spouse ", the answer of applicant is " not having spouse ", then " does not match
It is even " it is first kind target data, each investigation problem can be formed with first kind target data.Fisrt feature data are based on the
A kind of target data is formed, and the fisrt feature data formed in the state that first kind target data is " without spouse " can be
" unmarried ".
Step S230, according to next output number of the fisrt feature reading data and the fisrt feature Data Matching
According to.Such as in above-mentioned case, fisrt feature data are " unmarried ", then being not in such as " you in next investigation topic
Spouse is how many at the age " the problem of, but selection continues to show with the problem of fisrt feature Data Matching, such as first special
Data are levied as the problem of in the state of " unmarried ", next output data be should be with fisrt feature Data Matching.
In above-mentioned steps, by the matching relationship established between fisrt feature data and output data, mentioned according to applicant
Different output datas is selected for different first kind target datas, it is intended to improve the efficiency of questionnaire.
As further preferred embodiment, a kind of above-mentioned intelligent questionnaire data processing method, in which: further include,
Step S240, the first kind target data is read, one first verification is formed according to the first kind target data
Data output.
Step S250, read with described first verify matched each output data of data and with it is defeated described in each
The first kind target data of Data Matching verifies data with basis of formation out；
Step S260, arrangement processing is done to form a rank results to the basis verification data；
Step S270, Yu Suoshu rank results determine to match with the quasi- objective result in the state of matching a predetermined threshold
The first kind target data be true.
Step S280, Yu Suoshu rank results determine to match with the quasi- objective result in the state of matching a predetermined threshold
The first kind target data be false.
Above-mentioned steps S240 is identical as above-described embodiment one to its working principle of step S280, is not repeated herein.Its shape
At beneficial effect be: the data source of a target data has multiple, is verified by multiple data sources to a certain data,
The reliability of data source can be improved, while multiple data sources directly do not verify target data, but passes through calculating
The method of processing calculates difference degree or dispersion degree between data, goes to screen data certainly by difference degree or dispersion degree
The authenticity of body.By the above method, in the case where no third party evidence, improve single lonely data authenticity and can
By property.
Enumerate a kind of concrete application method of intelligent questionnaire data processing method: where the problem of the intelligence questionnaire extremely
It less include essential information, financial information, image data, assessment information, Soft Inform ation, gage information, supplemental information.
Wherein, essential information part is according to credit product entry criteria individual cultivation, such as: bank's loan micro- for intelligence
The entry criteria of product is that client individual is married, and the enterprise operation place using client as legal representative is located at the area this city XX model
In enclosing, the enterprise operation time limit must not be less than 12 months, and enterprise operation industry must not be the industries such as real estate.Then essential information part
The problem of include at least business management trade, enterprise operation time limit, the legal commissarial marital status of enterprise, enterprise operation place
Address, bank clerk obtain above- mentioned information with long-range communication way, can also the communication way of short distance obtain above-mentioned letter
Breath, and judged whether to be the client remaining investigation according to above- mentioned information.
After essential information part meets correlated condition, the investigation of financial information part can be carried out, financial information page is main
Balance sheet and profit and loss statement information including enterprise, balance sheet include at least currently all assets value and debt valence
Value, such as assets value include at least business assets (cash, receivable, stock, fixed assets etc.) and family assets (house property, vehicle
Etc.), value of being in debt is in debt (dealing with, advance, operational loan balance etc.) including business and family is in debt, and (housing loan, vehicle are borrowed
Deng).Profit and loss statement information includes at least the profitability of client, and core data is the income from sales of client, rate of gross profit, fixed cost
And family expense, to calculate the net profit of client.
Specifically, in the state of the matching relationship established between fisrt feature data and output data, receive external defeated
Enter with the matched first kind target data of current output data, according to the first kind target data formed a fisrt feature number
According to；Such as output data can be problem as shown in table 1 below:
| Detail | Title | Output data | First kind target data | Matching relationship |
| BS-KM1 | Cash and deposits with banks | Does is personal cash and deposits with banks remaining sum how many | x1 | X1=x2+x3+x4+x5 |
| Does is personal disposable cash how many | x2 | |||
| Do are personal Alipay and wechat remaining sum how many | x3 | |||
| Does is Private Banking's account balance how many | x4 | |||
| Does is personal fixed deposit how many | x5 | |||
| BS-KM2 | Accounts receivable | How many is the amount of money of normal accounts receivable | x1 | x1 |
| BS-KM3 | Advance money | Pay that the amount of money of businessman has in advance number | x1 | x1 |
| BS-KM4 | Stock | Does is current inventory level (by the amount of money) how many | x1 | x1 |
| BS-KM5 | Other current assets | How many is other current assets (such as bill receivable) | x1 | x1 |
| BS-KM6 | Fixed assets | The managerial present market net value of fixed assets is how many | x1 | x1 |
Table 1
According to next output data of the fisrt feature reading data and the fisrt feature Data Matching.On such as
In table 1, current output data is shown " personal cash and deposits with banks remaining sum is how many ", and first kind target data is " 0 "
" personal disposable cash is how many ", " personal Alipay and wechat remaining sum are how many ", " Private Banking under state, then do not shown
Account balance is how many ", " personal fixed deposit is how many ", but show " amount of money of normal accounts receivable how many ", again
Or current output data is shown " personal cash and deposits with banks remaining sum is how many ", first kind target data is " 1000 "
Under state, show next output data be " personal disposable cash is how many ", the first kind target data of the output data
In the state of " 1000 ", then show next output data be " amount of money of normal accounts receivable how many ".Pass through such side
Formula forms different display output based on different first kind target datas, it is intended to which the efficiency for improving investigation avoids that user is allowed to return
Inefficiency is answered, user experience is improved.
Continue the data collection of image data part, the data collection of image data part includes at least basic certificate
File is proved according to data, financial data, image data is corresponding with above-mentioned essential information and financial information.
The problem of entering evaluation stage after the completion of essential information, financial information and image data are collected, assess information purport
Information in financial information page is further being verified and value assessment, evaluation problem include at least common question and
Configurable problem, common question, which refers to, assesses crucial financial data, the wealth that must further verify each client
It is engaged in subject, such as: stock, receivable, house property, the income from sales in the sports such as vehicle assets and profit and loss statement.Another kind of can configure is asked
Topic refers to the special circumstances for each client, the financial data for needing further to verify.For example, according to objective in financial information page
The information that family provides carries out primary Calculation, judges which data needs further to verify according to the rule being previously written.Example
Such as: preset rules are, it is more than that 50% need to further verify that the individual event assets amount of money, which accounts for total assets amount of money specific gravity,.
To the appraisal procedure of crucial financial data, by taking the assessment of sales volume as an example, then the first verification data are then moon sale
Volume can be specially；
The finance first kind target data is read, it is defeated to form one first verification data according to the first kind target data
Out, read with described first matched each output data of verification data and with output data described in each matched the
A kind of target data verifies data with basis of formation；Basis verification data can be by the problem shape in " calculating moon sales volume method by week "
At basis verification data one, " by dull and rush season calculate moon sales volume " in problem formed basis verification data two, " press three days
The basis verification data three that problem in reckoning moon sales volume " is formed verify data one, the basic check number to the basis
Arrangement processing is done according to two, the basis verification data three to form a rank results；A predetermined threshold is matched in the rank results
Determine with the quasi- matched first kind target data of objective result to be true in the state of value.It is matched in the rank results
Determine with the quasi- matched first kind target data of objective result to be false in the state of one predetermined threshold.Based on above-mentioned step
Suddenly crucial financial data is assessed.
Soft Inform ation part, gage message part, supplemental information part are continued to complete after the completion of information to be assessed, wherein carrying on a shoulder pole
Whether subject matter insured information then needs the product of tendering guarantee and flexible configuration according to product element.Gage includes guarantor and guaranty two
Kind, acquisition information includes gage authenticity, and guarantor assures strength and collateral value.It is completed by seven above-mentioned steps
The collection work of entire data.
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (7)
1. a kind of processing method of cross check characterized by comprising
In the state of establishing the incidence relation having between each target data and data source, any one of number of targets is chosen
Verification object is formed accordingly；
Base is formed according to incidence relation reading and the associated data source of verification object, and according to the data source
Plinth verifies data；
Arrangement processing is carried out to form a rank results to the basis verification data, the verification object；
Determine that the target data is true in the state that the rank results match a predetermined threshold.
2. a kind of processing method of cross check according to claim 1, which is characterized in that the basic check number
Arrangement processing is carried out according to, the verification object to specifically include to form a rank results:
The basis verification data, the verification object are calculated, arrange processing to form an arrangement data；
Discrete calculation processing is done to form a dispersion degree index to the arrangement data；
The rank results are formed according to the dispersion degree index.
3. a kind of processing method of cross check according to claim 1, which is characterized in that further include:
Determine that the target data is false in the state that the rank results mismatch the predetermined threshold.
4. a kind of intelligence questionnaire data processing method, which is characterized in that including
Establish the matching relationship between fisrt feature data and output data；
Receive the externally input and matched first kind target data of current output data, according to the first kind target dataform
At a fisrt feature data；
According to next output data of the fisrt feature reading data and the fisrt feature Data Matching.
5. a kind of intelligent questionnaire data processing method according to claim 4, it is characterised in that: further include,
The first kind target data is read, one first verification data output is formed according to the first kind target data.
6. a kind of intelligent questionnaire data processing method according to claim 5, it is characterised in that: further include,
Reading is matched with each matched output data of the first verification data and with output data described in each
First kind target data verifies data with basis of formation；
Arrangement processing is done to form a rank results to the basis verification data；
Determine and the quasi- matched first kind of objective result in the state that the rank results match a predetermined threshold
Target data is true.
7. a kind of intelligent questionnaire data processing method according to claim 5, it is characterised in that: further include,
Determine and the quasi- matched first kind of objective result in the state that the rank results match a predetermined threshold
Target data is false.
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Cited By (1)
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| CN111814011A (en) * | 2020-06-02 | 2020-10-23 | 上海众言网络科技有限公司 | Method and device for controlling answer sheet quality in network survey |
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| CN111814011A (en) * | 2020-06-02 | 2020-10-23 | 上海众言网络科技有限公司 | Method and device for controlling answer sheet quality in network survey |
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Application publication date: 20191203 |