CN109146686B - Transaction data cross matching method, credit granting method and system thereof - Google Patents
Transaction data cross matching method, credit granting method and system thereof Download PDFInfo
- Publication number
- CN109146686B CN109146686B CN201810904648.XA CN201810904648A CN109146686B CN 109146686 B CN109146686 B CN 109146686B CN 201810904648 A CN201810904648 A CN 201810904648A CN 109146686 B CN109146686 B CN 109146686B
- Authority
- CN
- China
- Prior art keywords
- matching
- data
- cross
- enterprise
- trading partner
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention relates to the field of finance, and discloses a transaction data cross matching method, a credit granting method and a system thereof. In the invention, the cross matching method of the transaction data comprises the following steps: configuring a cross matching model and a data item required by cross validation of the model; extracting data items from a first data system and a second data system according to the data items required for cross validation of the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise; performing cross matching on the transaction data of the extracted data items according to the cross matching model; and outputting the result of the cross matching. Through a data model deployed at an enterprise end, transaction data of the enterprise and corresponding data of upstream and downstream trading partners are collected to perform multi-level transaction data cross validation, and enterprise trade authenticity is objectively quantified from different levels.
Description
Technical Field
The invention relates to the field of finance, in particular to a transaction data cross matching technology.
Background
In order to verify the authenticity of enterprise transaction data, in the prior art, most financial institutions send out manual work aiming at each verification request, data verification is carried out from a document level or a summary level, and multiple manual interventions are needed in the process, such as manual detail collection or summary data, manual data processing (such as desensitization), manual verification data and the like.
The prior art has the problems that sensitive data of enterprises are easy to leak, data verification cannot be completed in an automatic standardized mode, and a large amount of manual data verification of financial institutions is easy to generate.
Therefore, there is a need for a cross-matching method for transaction data that can effectively solve the above problems.
Disclosure of Invention
The invention aims to provide a transaction data cross matching method, a credit granting method and a system thereof.
In order to solve the above problems, the present application discloses a transaction data cross-matching method, which includes the following steps:
configuring a cross matching model and a data item required by cross validation of the model;
extracting data items from a first data system and a second data system according to the data items required for cross validation of the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise;
performing cross matching on the transaction data of the extracted data items according to the cross matching model;
and outputting the result of the cross matching.
The application also discloses a credit granting method, which comprises the following steps:
the system of the financial institution sends the cross matching model and the data items required by the cross validation of the model to the third-party system;
the third-party system executes the transaction data cross matching method, sends the cross matching result to the system of the financial institution, and shields the original transaction data for the system of the financial institution;
and the financial institution system gives credit according to the cross matching result.
The application also discloses a transaction data cross-matching system, including:
the configuration unit is used for configuring a cross matching model and data items required by cross validation of the model;
the data extraction unit is used for extracting data items from a first data system and a second data system according to the data items required to be cross-verified by the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise;
the cross matching unit is used for performing cross matching on the transaction data of the extracted data items according to the cross matching model;
and the result output unit is used for outputting the cross matching result.
Compared with the prior art, the implementation mode of the invention has the main differences and the effects that:
through the data model deployed at the enterprise end, the transaction data of the enterprise and the corresponding data of upstream and downstream trading partners are collected for cross validation, and the enterprise trade authenticity is objectively quantified.
Further, the authenticity of the enterprise transaction data is verified from different levels through multi-level transaction data cross-matching.
Furthermore, by completing cross matching of transaction data at the enterprise end, the possibility of leakage of enterprise sensitive data can be effectively reduced, and the security of enterprise data is enhanced.
Furthermore, by configuring the preset values of the matching modes of the upstream and downstream trade objects of different enterprises, the method can effectively adapt to the data opening policies adopted by different trade objects, reduces the matching obstruction caused by the difference of the data opening policies, and enables the matching range to be wider and more practical.
Further, data is collected and verified in an automatic standardized manner, thereby protecting against human error or human modification that may result from manually processing the data.
Furthermore, data are collected and checked in an automatic standardized mode, the defect that a large amount of manual data checking is needed is overcome, and meanwhile data checking efficiency and accuracy are improved.
Drawings
FIG. 1 is a schematic flow chart of a cross-matching method for transaction data according to a first embodiment of the present invention;
fig. 2 is a flowchart illustrating a credit granting method according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a cross-matching system for transaction data according to a third embodiment of the present invention;
fig. 4 is a flow chart illustrating a transaction data cross-matching method according to an embodiment of the invention.
Detailed Description
In the following description, numerous technical details are set forth in order to provide a better understanding of the present application. However, it will be understood by those skilled in the art that the technical solutions claimed in the present application can be implemented without these technical details and with various changes and modifications based on the following embodiments.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The first embodiment of the invention relates to a transaction data cross matching method. Fig. 1 is a flow chart diagram of the transaction data cross-matching method.
Specifically, as shown in fig. 1, the transaction data cross-matching method includes the following steps:
in step 101, a cross-matching model and the data items required for cross-validation of the model are configured.
Thereafter, step 102 is entered, and data items are extracted from a first data system and a second data system according to data items required for cross-validation of the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise.
It should be noted that the cross matching model is configured at the client, and the collection of the transaction data of the enterprise itself and the cross data of the upstream and downstream trading partners is also performed at the client, so that the possibility of leakage of the sensitive data of the enterprise can be reduced, and the security of the data of the enterprise can be enhanced.
This flow ends thereafter.
Further, specifically, in step 103, the following sub-steps are also included:
a matching mode pre-value pre for each trading partner of the enterprise is deployedi：
Wherein i is a trading partner identification number,
or 4 of the number of the first and second groups,
the method respectively corresponds to four-layer matching modes of strict document matching, fuzzy document matching, monthly summary matching and summary matching, wherein a value of 0 indicates that the trading partner i is not applicable to the j-layer matching mode, and a value of 1 indicates that the trading partner i is applicable to the j-layer matching mode;
predicting according to matching mode of each trading partneriDetermining the matching mode applicable to each trading partner; by "prei0001 as an example, it means that the trading partner i only applies the summary matching method;
and extracting corresponding data items according to the required fields corresponding to the matching mode according to the matching mode applicable to each trading partner, wherein when the matching mode is the bill level, the extracted data items are accurate to the bill level, and when the matching mode is the summary level, the extracted data items are accurate to the summary level, and the data items extracted from each trading partner and the corresponding data items extracted from the enterprise are subjected to cross matching.
Still further, preferably, before the step of cross-matching the data items extracted from each trading partner with the corresponding data items extracted from the enterprise, an initialization process is further required for each trading partner, the initialization process comprising the steps of:
initializing each parameter and index value, mainly involving the following values:
M0-match cumulative amount, 0;
T0e, wherein E is the total amount with the trading partner counted according to the enterprise's own ERP data;
A0＝E*D00, -trade match amount.
Next, the four-layer matching method is described in detail:
a first layer: strict document matching, wherein the strict document matching refers to that the date and the order number of the transaction order of the enterprise are matched with the date and the order number of the transaction order corresponding to the trading partner at the same time;
in a strict document matching mode, the document amount is added into the matching accumulated amount M1Middle and trade total T1Keeping the document unchanged, and then calculating a next document;
if the date or the document number do not match, the next document is directly calculated.
A second layer: fuzzy document matching, wherein the fuzzy document matching means that the interval between the date of the self transaction order of the enterprise and the date of the corresponding transaction order of the trading partner is within a first preset value;
in the present embodiment, preferably, the first preset value is 3 days.
In other embodiments, the first preset value can be adjusted according to specific situations.
In the fuzzy bill matching mode, the bill sum is added into the matching accumulated sum M2Middle and trade total T2Keeping the document unchanged, and then calculating a next document;
and if the date interval is not within the preset value, directly calculating the next bill.
And a third layer: the monthly summary matching refers to the monthly summary data of the enterprise and the monthly summary data of the trading partners being matched in a monthly manner;
accumulating the sum into a matching cumulative sum M in the monthly summary matching mode3Middle and trade total T3Keeping the standard value unchanged, and calculating the next month;
if the monthly summarized data do not match, the money amounts of the two parties are judged:
if the monthly summary amount of the enterprise is large, the trade partnerThe monthly summary amount is added into the matching cumulative amount M3Middle and trade total T3Keeping the standard value unchanged, and calculating the next month;
if the monthly sum of the enterprise is smaller, the monthly sum of the enterprise is accumulated into the matching cumulative sum M3Middle and trade total T3The difference between the two totals (the corporate monthly total and the trading partner monthly total) must be added and the next month calculated.
A fourth layer: summarizing abstract matching, wherein the summarizing abstract matching refers to monthly matching of enterprise summarizing abstract data and trading partner summarizing abstract data;
in the summary matching mode, the enterprise summary amount is added to a matching cumulative amount M4Middle and trade total T4Keeping the standard value unchanged, and calculating the next month;
if the summary summaries do not match, the next month is directly calculated.
How do the summary summaries match? The method for judging the summary matching comprises the following steps:
respectively calculating summary abstracts of enterprise self and trade partner according to month
The initial values are all 0;
for a business, calculating according to data related to trading partner i in its own document
For the trading partner i, calculating according to the data related to the corresponding enterprise in the purchase-sale-stock data
In the formula, the documents are all documents in the month; the bill amount is the bill amount; the time interval is the bill interval date, namely the interval date of the bill and the last bill closest in time, and the calculation mode of the interval date is refined to the day in principle;
if it is not
Matching the summary summaries, otherwise, mismatching, wherein e is a second preset value;
in the present embodiment, e is preferably 7.8.
In other embodiments, e may take other values, but is not limited thereto.
It should be noted that each trading partner can simultaneously apply to multiple matching modes, and the priority order of the four-layer matching mode is as follows:
strict document matching, fuzzy document matching, monthly summary matching and summary matching
The step of cross-matching the extracted data items according to the cross-matching model further comprises the sub-steps of:
calculating the trade matching degree of each layer matching and the trade matching total of each layer matching according to the matching accumulated sum of each layer matching and the trade total of each layer matching, specifically:
1. and generating the trading matching degree (D) and the trading matching amount (A) of each layer of the single trading partner according to the following formulas:
a first layer: the matching of the strict documents is strict,
a second layer: the fuzzy document is matched with the fuzzy document,
and a third layer: the matching is summarized according to the month,
a fourth layer: the summary and the abstract are matched with each other,
A4＝E*D4；
AH＝max{A1,A2,A3,A4}，
wherein A isHMaximum trade match amount, D, representing the trading partnerHRepresenting the maximum trade matching degree of the trading partner.
ATGet A1,A2,A3,A4The first value of (a) that is not zero,
wherein A isTExpress the premium trade match amount, D, of the trading partnerTIndicating the excellent trade matching degree of the trading partner.
Namely, in the priority aspect:
strict document matching, fuzzy document matching, monthly summary matching, summary matching
2. After the matching numerical value calculation aiming at the single trade partner is completed, A is calculated1,A2,A3,A4,AH,ATAdding the values of E to the total parameter
ETIn (1), the initial value of each total parameter is zero;
generating a matching degree according to the following formula:
The second embodiment of the present invention relates to a credit granting method. Fig. 2 is a flow chart of the credit granting method.
Specifically, as shown in fig. 2, the credit granting method includes the following steps:
in step 201, the financial institution's system sends the cross-matching model and the data items for which the model needs cross-validation to a third-party system.
Then, step 202 is entered, and the third-party system executes the above-mentioned transaction data cross-matching method, and sends the cross-matching result to the system of the financial institution, so as to shield the original transaction data from the system of the financial institution.
Thereafter, step 203 is entered, and the financial institution system performs credit granting according to the cross-matching result.
This flow ends thereafter.
This embodiment is a method embodiment corresponding to the first embodiment, and may be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment.
A third embodiment of the invention is directed to a transaction data cross-matching system. Fig. 3 is a schematic structural diagram of the transaction data cross-matching system.
Specifically, as shown in fig. 3, the transaction data cross-matching system includes:
the configuration unit is used for configuring a cross matching model and data items required by cross validation of the model;
the data extraction unit is used for extracting data items from a first data system and a second data system according to the data items required to be cross-verified by the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise;
the cross matching unit is used for performing cross matching on the transaction data of the extracted data items according to the cross matching model;
and the result output unit is used for outputting the cross matching result.
This embodiment is a system embodiment corresponding to the first embodiment, and may be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and are not described herein again in order to reduce repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first embodiment.
One specific embodiment of the present application is explained below.
In order to verify the authenticity of enterprise transaction data, the prior art has the problems that enterprise sensitive data leak, data verification cannot be completed in an automatic standardized mode, and a large amount of manual data verification of financial institutions generates. By configuring a data model preposed at the enterprise end, the trade matching degree and the trade matching amount are output to the financial institution after the enterprise end calculates, and the financial institution uses the data to assist in completing credit granting. The problems are effectively solved.
By means of the model preposition, the leakage of sensitive data of an enterprise can be avoided; meanwhile, data items related to credit investigation indexes needed by the model can be rapidly configured, and corresponding data items are rapidly extracted from the enterprise ERP/purchase, sales and inventory system.
The output trade matching degree can assist financial institutions to judge the trade authenticity of enterprises by quantitative data; and the financial institution is assisted in automatic credit investigation and credit granting.
The trade matching amount is output, so that financial institutions can be assisted to judge the trade authenticity of enterprises through quantitative data; and the financial institution is assisted in automatic credit investigation and credit granting.
The cross matching of the transaction data refers to the cross verification of the corresponding data of the upstream and downstream trading partners of the enterprise and the self purchasing and selling data of the enterprise, so that the enterprise trade authenticity is objectively quantified.
The specific flow of the method and the system for cross matching of the enterprise transaction data with the preposed model is shown in fig. 4, and the method comprises the following steps:
step 1, determining a brand and a version of an ERP/purchase, sale and stock system used by an enterprise;
step 2, configuring data items required by cross validation of the model;
step 3, configuring data interfaces of upstream and downstream trading partners of the enterprise;
step 4, extracting corresponding data items in the enterprise ERP/purchase, sales and inventory system;
step 5, extracting corresponding data items of transaction data of upstream and downstream trading partners of the enterprise;
step 6, cross matching transaction data;
step 7, generating different levels of trade matching degrees and trade matching amount;
step 8, outputting the relevant cross matching result to the financial institution;
and 9, the financial institution completes credit granting based on the cross matching result.
Separately calculating the trade matching degree and the trade matching amount according to the upstream and downstream:
firstly, determining the brand and the version of the ERP/purchase-sale-stock system used by the enterprise in a manual or automatic mode (step 1) and providing support for the configuration of the data items in the step 2.
Secondly, mapping the address (position) of the required data item in the system according to the system brand and the version confirmed in the step 1, and providing support for subsequent data extraction.
And thirdly, configuring data interfaces of the upstream and downstream trading partners of the enterprise (step 3), including but not limited to the following contents:
determining the number of trade partners through self transaction data of an enterprise, and numbering (counting from 1, calculating separately upstream and downstream, and dividing into n and m);
deploying a match mode pre-value (pre) of each trading partner (i)i)：
Wherein i is a trading partner identification number, and the definition rule of the identification number is as follows:
i is composed of up and down identifiers and ordinal numbers, u represents the trading partner as upstream, d represents the trading partner as downstream, and r represents the serial number of the upstream and downstream trading partners, which represents the r-th trading partner and is a non-negative integer. 0 ≦ r ≦ n for upstream, default case i ═ u0, representing the 0 th upstream trading partner; for downstream 0 ≦ r ≦ m, default i ≦ d0, representing the 0 th downstream trading partner. Taking upstream as an example, assuming that the enterprise has 1500 upstream trading partners, the ordinal number r of the 100 th upstream trading partner is 100, and the value "i" is u100, wherein the ordinal number r of the 1500 th upstream trading partner is 1500, and the value "i" is u 1500.
Wherein
The method respectively corresponds to four layers of matching modes of strict document matching (a), fuzzy document matching (b), monthly summary matching (c) and summary matching (d). By default pre of each trading partner iiIs "0000", "0" value indicates that the matching method is not applied, and "1" value indicates that the matching method is applied. By "preiFor example, 0001 "indicates that the trading partner i only applies the summary matching method.
The multi-level transaction data are cross-matched, the transaction data are matched in a multi-level mode, and the transaction matching is more comprehensively shown; and displaying the goodness of fit of the corresponding data from different degrees, and fitting different situations and requirements of enterprises.
And fourthly, extracting corresponding data items in the ERP/purchase-sale-storage system of the enterprise according to the step 2 and the step 3, and providing original detail data, summarized data or summary data and parameters of the enterprise for subsequent cross matching.
And fifthly, extracting corresponding cross data of the upstream and downstream trading partners of the enterprise according to the step 2 and the step 3, and providing original detail data, summarized data or summary data and parameters of the trading partners of the enterprise for subsequent cross matching.
Sixthly, the flow of the transaction data cross matching (the step 6) is as follows:
letting the ordinal number r in the identification number of the trading partner be a numerical value of 0, and executing the following procedures for each trading partner in sequence:
i, initializing (A);
pre-value (pre) according to matching mode of each trade partneri) And (B) performing multilayer matching.
A. The initialization process includes, but is not limited to, the following:
take upstream as an example (similar downstream processing):
initializing each parameter and index value, mainly involving the following values:
·M0-match cumulative amount, 0;
·T0e, wherein E is the total amount with the trading partner counted according to the enterprise's own ERP data;
·
-trade match degree;
·A0＝E*D00, -trade match amount.
B. The multilayer matching process is as follows:
according to the data interface configuration of the upstream and downstream trading partners of the enterprise (step 3 above), the following judgments are sequentially carried out for each trading partner and the corresponding flow is entered:
for the ith trading partner, according to
The type of match for which it is applicable is determined.
a. Is strict document matching applicable? (
Adapted to strict document matching, otherwise not adapted)
b. Is fuzzy document matching applicable? (
Adapted to fuzzy document matching, otherwise not adapted)
c. Is a monthly summary match applicable? (
Adapted to gather together monthly, otherwise not adapted)
d. Is summary matching applicable? (
Adapted summary matching, otherwise not adapted)
a. And once the parameters b, c and d indicate that the matching type is suitable, calling a specific module in sequence to calculate.
The matching in the following modules refers to comparison and verification between the data of the enterprise and the corresponding data of the trading partner.
Ms-matching the accumulated amount, and taking 1, 2, 3, 4 respectively according to different modules s, wherein the initial values are M0；
Ts-trade total, according to different s respectively taking 1, 2, 3 and 4, its initial value is T0；
Module a (strict document matching):
comparing the exact order number according to the date of the transaction order:
if the date matches the document number at the same time, the document amount is added up to the matching cumulative amount M1Middle and trade total T1Keeping the document unchanged, and calculating the next document;
if the date or the document number do not match, the next document is calculated.
Module b (fuzzy document matching):
comparing the exact order number according to the date of the transaction order:
if the single number date interval is within 3 days (can)Adjusted according to specific conditions), the bill amount is added into the matched accumulated amount M2Middle and trade total T2Keeping the document unchanged, and calculating the next document;
if the date or the document number do not match, the next document is calculated.
Module c (monthly summary match):
matching according to monthly summarized data:
if the monthly summary data matches, the summary amount is accumulated into the matching cumulative amount M3Middle and trade total T3Keeping the original shape; (monthly summarized data are monthly sum of money, equal to each other, are considered as matching)
If the monthly summary data does not match, then determine the amount of money between parties:
calculate the next month.
Module d (summary matching):
matching according to the summary data in months:
if the summaries match, the business summary amount is accumulated into a matching cumulative amount M4Middle and trade total T4Keeping the original shape;
if the summary summaries do not match, calculate the next month;
calculate the next month.
A synthesis module:
1. and generating each layer of trade matching degree (D) and trade matching amount (A) according to the following formulas:
·
A1＝E*D1；
·
A2＝E*D2；
·
A3＝E*D3；
·
A4＝E*D4；
·AH＝max{A1,A2,A3,A4}，
·ATget A1,A2,A3,A4The first value of (a) that is not zero,
namely, in the priority aspect:
strict document matching, fuzzy document matching, monthly summary matching and summary matching
2. After the matching numerical value calculation aiming at the single trade partner is completed, A is calculated1,A2,A3,A4,AH,ATAdding the values of E to the total parameter
ETIn (3), the initial value of each total parameter is zero;
·
respectively:
generating the degree of match according to the following formula:
let ordinal number r in the trading partner identification number be r +1, and perform the next-family trading partner data matching.
Summary matching may be calculated as follows:
1. respectively calculating summary abstracts of enterprise self and trade partner according to month
The initial values are all 0.
For the enterprise, the calculation is based on the data related to the trading partner (i) in its own documents
For trading partner (i), calculations are made based on data in its purchase, sales and inventory data relating to the corresponding business
In the above formula:
I. the documents are all related documents in the month;
II, the bill amount is the corresponding amount of the bill;
and III, the time interval is the bill interval date, namely the interval date between a certain bill and the last latest bill, and the calculation mode is refined to the day-by-day mode in principle.
a. The documents in the same day are not classified, namely the document interval in the same day is 0. At the moment, the sum of the bills in the same day is accumulated to be used as the sum of the bills, and the interval date between the sum of the bills and the last latest bill is calculated. For example, if there are two documents in succession on a certain day, the interval between the two documents is 0.
b. And for the processing of the bill with the earliest date, because the last comparison date does not exist, uniformly adopting the time interval corresponding to the bill amount of the first day to take 1.
The following table is given as an example:
assuming that the summary is calculated from 17 years and 9 months, and a total of 5 documents for 9 months, the summary for 9 months is:
5000*1+(4000+3000+2500)*10+1000*3＝103000
2. if it is not
The summary digests match, otherwise they are not. By default e takes 7.8.
Desensitizing the trade data through summary abstract matching to generate summary abstract for matching, so that sensitive data are prevented from leaking; desensitization data matching may improve trading partner compliance.
In summary, through a data collection tool (data gateway) and a pre-model deployed at an enterprise end, transaction data of the enterprise and corresponding upstream and downstream cross data are collected, sensitive information is removed, and then trade matching degree (including upstream trade matching degree and downstream trade matching degree) and trade matching amount (including upstream trade matching amount and downstream trade matching amount) are automatically generated and submitted to a financial institution, so that the data collection tool and the pre-model serve as credit investigation data to assist the financial institution in completing a credit granting process.
The following problems are mainly solved:
1. verifying enterprise transaction data authenticity from different levels;
2. the possibility of leakage of sensitive data of the enterprise is reduced, and the security of the data of the enterprise is enhanced;
3. data are collected and verified in an automatic standardized mode, and human errors or human modification possibly caused by manual data processing are prevented in a cautious mode;
4. and the credit investigation and credit granting process efficiency is improved. The data checking efficiency and accuracy are improved while the defect that a large amount of manual data checking is needed is overcome.
Each method embodiment of the present invention can be implemented by software, hardware, firmware, or the like. Whether the present invention is implemented as software, hardware, or firmware, the instruction code may be stored in any type of computer-accessible memory (e.g., permanent or modifiable, volatile or non-volatile, solid or non-solid, fixed or removable media, etc.). Also, the Memory may be, for example, Programmable Array Logic (PAL), Random Access Memory (RAM), Programmable Read Only Memory (PROM), Read-Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic disk, an optical disk, a Digital Versatile Disk (DVD), or the like.
Each unit or module mentioned in each system embodiment of the present invention is a logical unit or module, and physically, a logical unit or module may be a physical unit or module, or a part of a physical unit or module, or may be implemented by a combination of multiple physical units or modules, where the physical implementation manner of the logical unit or module itself is not the most important, and the combination of functions implemented by the logical unit or module is the key to solve the technical problem provided by the present invention. Furthermore, in order to highlight the innovative part of the present invention, the above-mentioned embodiments of the device of the present invention do not introduce elements or modules which are not too closely related to solve the technical problems posed by the present invention, which does not indicate that there are no other elements or modules in the above-mentioned embodiments of the device.
It is to be noted that in the claims and the description of the present patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.
While the invention has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (9)
1. A transaction data cross-matching method is characterized by comprising the following steps:
configuring a cross matching model and a data item required by cross validation of the model;
extracting data items from a first data system and a second data system according to the data items required for cross validation of the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise;
performing cross matching on the transaction data of the extracted data items according to the cross matching model;
outputting the result of the cross matching;
the step of "cross-matching transaction data for the extracted data items according to the cross-matching model" comprises the sub-steps of:
deploying the pre-value pre of the matching mode of each trading partner of the enterprisei：
Wherein i is a trading partner identification number,
or 4 of the number of the first and second groups,
the method respectively corresponds to four-layer matching modes of strict document matching, fuzzy document matching, monthly summary matching and summary matching, wherein a value of 0 indicates that the trading partner i is not applicable to the j-layer matching mode, and a value of 1 indicates that the trading partner i is applicable to the j-layer matching mode; the strict document matching means that the date and the order number of the transaction order of the enterprise are matched with the date and the order number of the transaction order corresponding to the trading partner at the same time; the fuzzy document matching means that the date of the self transaction order of the enterprise and the date interval of the corresponding transaction order of the trading partner are within a first preset value; the monthly summary matching means that monthly summary data of an enterprise are matched with monthly summary data of a trading partner in a monthly manner; the summary abstract matching means that the summary abstract data of the enterprise is matched with the summary abstract data of the trading partner in months;
predicting according to matching mode of each trading partneriDetermining the matching mode applicable to each trading partner;
and extracting corresponding data items according to the required fields corresponding to the matching mode according to the matching mode applicable to each trading partner, wherein when the matching mode is the bill level, the extracted data items are accurate to the bill level, and when the matching mode is the summary level, the extracted data items are accurate to the summary level, and the data items extracted from each trading partner and the corresponding data items extracted from the enterprise are subjected to cross matching.
2. According to claimThe transaction data cross matching method of 1, characterized in that, in the strict document matching mode, the document amount is added into the matching accumulated amount M1Middle and trade total T1Keeping the document unchanged, and then calculating a next document;
if the date or the document number do not match, the next document is directly calculated.
3. The transaction data cross-matching method of claim 1, wherein in the fuzzy document matching mode, document amount is added to match cumulative amount M2Middle and trade total T2Keeping the document unchanged, and then calculating a next document;
and if the date interval is not within the preset value, directly calculating the next bill.
4. The transaction data cross-matching method of claim 1, wherein in the monthly summary matching mode, a summary amount is added to the matching cumulative amount M3Middle and trade total T3Keeping the standard value unchanged, and calculating the next month;
if the monthly summarized data do not match, the money amounts of the two parties are judged:
if the enterprise summarized amount per month is larger, the summarized amount per month of the trading partner is accumulated into the matched accumulated amount M3Middle and trade total T3Keeping the standard value unchanged, and calculating the next month;
if the monthly sum of the enterprise is smaller, the monthly sum of the enterprise is accumulated into the matching cumulative sum M3Middle and trade total T3The difference between the two amounts totaled is added and the next month is calculated.
5. The transaction data cross-matching method of claim 1, wherein the business summary amount is added to a matching cumulative amount M in the summary matching mode4Middle and trade total T4Keeping the standard value unchanged, and calculating the next month;
if the summary summaries do not match, the next month is directly calculated.
6. The transaction data cross-matching method of claim 5, wherein the summary matching determination method comprises the following steps:
respectively calculating summary abstracts of enterprise self and trade partner according to month
The initial values are all 0;
for a business, calculating according to data related to trading partner i in its own document
For the trading partner i, calculating according to the data related to the corresponding enterprise in the purchase-sale-stock data
In the formula, the documents are all documents in the month; the bill amount is the bill amount; the time interval is the bill interval date, namely the interval date between the bill and the last bill closest in time;
if it is not
The summarized summaries are matched, otherwise, the summaries are not matched, wherein e is a second preset value.
7. The transactional data cross-matching method according to any of claims 2-6, wherein said step of cross-matching the extracted data items according to the cross-matching model further comprises the sub-steps of:
and calculating the trade matching degree of each layer of matching and the trade matching total of each layer of matching according to the matching accumulated sum of each layer of matching and the trade matching total of each layer of matching.
8. A credit granting method is characterized by comprising the following steps:
the system of the financial institution sends the cross matching model and the data items required by the cross validation of the model to the third-party system;
the third party system performing the method of any one of claims 1-7, sending the cross-matching results to the financial institution's system, and masking the financial institution's system from the original transaction data;
and the financial institution system gives credit according to the cross matching result.
9. A transaction data cross-matching system, comprising:
the configuration unit is used for configuring a cross matching model and data items required by cross validation of the model;
the data extraction unit is used for extracting data items from a first data system and a second data system according to the data items required to be cross-verified by the model, wherein the first data system is a data system of an enterprise, and the second data system is a data system of an upstream or downstream trading partner of the enterprise;
the cross matching unit is used for performing cross matching on the transaction data of the extracted data items according to the cross matching model;
a result output unit for outputting the result of the cross matching;
the cross matching unit performs the steps of:
deploying the pre-value pre of the matching mode of each trading partner of the enterprisei：
Wherein i is a trading partner identification number,
or 4 of the number of the first and second groups,
the method respectively corresponds to four-layer matching modes of strict document matching, fuzzy document matching, monthly summary matching and summary matching, wherein a value of 0 indicates that the trading partner i is not applicable to the j-layer matching mode, and a value of 1 indicates that the trading partner i is applicable to the j-layer matching mode; the strict document matching means that the date and the order number of the transaction order of the enterprise are matched with the date and the order number of the transaction order corresponding to the trading partner at the same time; the fuzzy document matching means that the date of the self transaction order of the enterprise and the date interval of the corresponding transaction order of the trading partner are within a first preset value; the monthly summary matching means that monthly summary data of an enterprise are matched with monthly summary data of a trading partner in a monthly manner; the summary abstract matching means that the summary abstract data of the enterprise is matched with the summary abstract data of the trading partner in months;
predicting according to matching mode of each trading partneriDetermining the matching mode applicable to each trading partner;
and extracting corresponding data items according to the required fields corresponding to the matching mode according to the matching mode applicable to each trading partner, wherein when the matching mode is the bill level, the extracted data items are accurate to the bill level, and when the matching mode is the summary level, the extracted data items are accurate to the summary level, and the data items extracted from each trading partner and the corresponding data items extracted from the enterprise are subjected to cross matching.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810904648.XA CN109146686B (en) | 2018-08-09 | 2018-08-09 | Transaction data cross matching method, credit granting method and system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810904648.XA CN109146686B (en) | 2018-08-09 | 2018-08-09 | Transaction data cross matching method, credit granting method and system thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109146686A CN109146686A (en) | 2019-01-04 |
| CN109146686B true CN109146686B (en) | 2021-10-26 |
Family
ID=64792464
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810904648.XA Active CN109146686B (en) | 2018-08-09 | 2018-08-09 | Transaction data cross matching method, credit granting method and system thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109146686B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112927063A (en) * | 2021-02-23 | 2021-06-08 | 四川享宇金信金融科技有限公司 | Financial information cross-checking method |
| CN114049505B (en) * | 2021-10-11 | 2022-08-23 | 数采小博科技发展有限公司 | Method, device, equipment and medium for matching and identifying commodities |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403372A (en) * | 2017-07-31 | 2017-11-28 | 北京欣欣苹果网络科技有限公司 | Internet finance data processing platform |
| CN107798529A (en) * | 2017-03-28 | 2018-03-13 | 平安壹钱包电子商务有限公司 | transaction data monitoring method and device |
| CN108074180A (en) * | 2017-12-28 | 2018-05-25 | 广州富融信息技术有限公司 | A kind of recent financial development air control system and its construction method based on block chain technology |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7349557B2 (en) * | 1998-06-19 | 2008-03-25 | Solidus Networks, Inc. | Electronic transaction verification system |
-
2018
- 2018-08-09 CN CN201810904648.XA patent/CN109146686B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798529A (en) * | 2017-03-28 | 2018-03-13 | 平安壹钱包电子商务有限公司 | transaction data monitoring method and device |
| CN107403372A (en) * | 2017-07-31 | 2017-11-28 | 北京欣欣苹果网络科技有限公司 | Internet finance data processing platform |
| CN108074180A (en) * | 2017-12-28 | 2018-05-25 | 广州富融信息技术有限公司 | A kind of recent financial development air control system and its construction method based on block chain technology |
Non-Patent Citations (2)
| Title |
|---|
| Blockchains as a solution for traceability and transparency;André Jeppsson,Oskar Olsson;《lup.lub.lu.se》;20171215;全文 * |
| 基于区块链的科技金融大数据开放共享体系研究;王洁，魏生，戴科冕;《实践与经验》;20180805;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109146686A (en) | 2019-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11170376B2 (en) | Informational and analytical system and method for ensuring the level of trust, control and secure interaction of counterparties when using electronic currencies and contracts | |
| US8429050B2 (en) | Method for detecting ineligibility of a beneficiary and system | |
| US20090177664A9 (en) | System and Method for Regulatory Rules Repository Generation and Maintenance | |
| US20110106567A1 (en) | System and method for intelligently tracking and managing claim based calculations | |
| US8108225B2 (en) | Method, system, and software for analysis of a billing process | |
| WO2018223766A1 (en) | Method, storage medium, terminal apparatus, and device for calculating commission | |
| CN110458691B (en) | Pre-loan risk monitoring method and device | |
| CN109146686B (en) | Transaction data cross matching method, credit granting method and system thereof | |
| US10366351B2 (en) | Information standardization and verification | |
| US20140316824A1 (en) | Inventory and pricing method and computer program product | |
| US8566204B2 (en) | Method for detecting ineligibility of a beneficiary and system | |
| US20070255651A1 (en) | Batch processing of financial transactions | |
| US7698212B1 (en) | Online settlement statement and funding control system and method | |
| US20100125513A1 (en) | Reconciling Financial Transactions | |
| US20210056618A1 (en) | Transferring transactions between financial institutions | |
| Rutledge et al. | Elements of structured finance | |
| CN116128458B (en) | Intelligent automatic auditing system for hospital expense card account reporting | |
| Parente et al. | Assessment of predictive modeling for identifying fraud within the Medicare program | |
| US8032454B2 (en) | Import declaration/foreign supplier invoice payment reconciliation process | |
| US20120191471A1 (en) | Method, system, and software for analysis of a billing process | |
| US11966970B2 (en) | Method and system for performing income analysis from source documents | |
| US20170076401A1 (en) | Payment system with automated payroll deduction | |
| AU2019100421A4 (en) | New system and method of conducting real estate sales transactions online, including conveyancing, updating land registries and paying government taxes, utilising blockchain technology. This is done by recording property sales transactions on the blockchain and enable peer-to-peer title transfers and completing the settlement and conveyancing process on a private blockchain. | |
| US20200151814A1 (en) | Methods and Systems for Scoring Healthcare Debt Obligations | |
| CA3090657A1 (en) | Transferring transactions between financial institutions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |