CN114549170A

CN114549170A - Data processing method, medium, equipment and system for joint loan

Info

Publication number: CN114549170A
Application number: CN202210118915.7A
Authority: CN
Inventors: 高玉鹏
Original assignee: Beijing Yusys Technologies Group Co ltd
Current assignee: Beijing Yusys Technologies Group Co ltd
Priority date: 2021-11-19
Filing date: 2021-11-19
Publication date: 2022-05-27
Anticipated expiration: 2041-11-19
Also published as: CN113807956A; CN114549170B; CN113807956B

Abstract

The invention provides a data processing method, medium, equipment and system for joint loan, wherein the method comprises the following steps: acquiring multiple transaction data of the same transaction; carrying out data distribution processing on a plurality of transaction data to determine a plurality of normal transaction data; integrating and assembling a plurality of normal transaction data to obtain full service chain monitoring information; generating a plurality of index data related to the letter sequence according to the full-service chain monitoring information; receiving a message application message; acquiring a corresponding credit application result according to the credit application information; acquiring a plurality of index data related to the credit ordering; calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence; sorting the ginseng credit lines in an ascending order according to the credit values of the ginseng credit lines; and sending the credit application transactions to the credit participating lines one by one according to the determined sequence after sorting. The method can meet the service and technical data required by the routing calculation and ensure the existing service requirement and the future service expansion.

Description

Data processing method, medium, equipment and system for joint loan

This application is a divisional application of the original patent application having application number "202111372983.8", filed on 19/11/2021, which is incorporated herein by reference.

Technical Field

The invention relates to the field of financial business data processing, in particular to a data processing method, medium, equipment and system for joint loan.

Background

A joint loan is a loan made by two or more banks together for a project or business. In the joint loan mode, the original one sponsor is changed into a plurality of sponsors.

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: in the prior art, transaction data is scattered and the data quality is worried, so that expected and reliable cooperative funding parties cannot be selected from multiple dimensions.

Disclosure of Invention

In view of the above, an object of the embodiments of the present invention is to provide a data processing method, medium, device and system for syndication lending, so as to solve the above technical problems.

In a first aspect, an embodiment of the present invention provides a data processing method for syndicated credits, where the method includes the following steps:

acquiring multiple transaction data of the same transaction;

carrying out data distribution processing on the multiple transaction data to determine multiple normal transaction data;

integrating and assembling multiple normal transaction data from the same transaction to the end of the service processing from the service request to obtain the monitoring information of the whole service chain;

generating a plurality of index data for the routing rule to use according to the full service chain monitoring information;

acquiring a routing rule corresponding to a head-pulling row;

according to the routing rule, acquiring a plurality of credit-participating row check index data corresponding to the routing rule from the index data used by the routing rule;

filtering the participatory loan rows which are not suitable for the lead rows according to the routing rule and the plurality of participatory loan row check index data, and determining available participatory loan rows;

and sending a credit application transaction to the available credit-participating bank.

In some possible embodiments, the data splitting processing is performed on the multiple transaction data, and after determining multiple normal transaction data, the method further includes:

carrying out data distribution processing on the transaction data to determine abnormal transaction data;

if the occurrence frequency of abnormal transaction data reaches the preset frequency within the appointed time, marking the transaction as fusing;

sending a fusing notification, wherein the fusing notification is used for indicating that the transaction is fused.

In some possible embodiments, the integrating and assembling multiple normal transaction data from the initiation of the same transaction from the service request to the completion of the service processing to obtain the full service chain monitoring information specifically includes:

taking the unique transaction ID as a unique basis for judging whether each piece of transaction data exists in the message queue, and if not, triggering to execute analysis processing; if yes, skipping the transaction data;

if the transaction data needs to be analyzed and processed, marking the transaction data, comprising the following steps:

if the state of the transaction data is a first final state, the first final state is that the receiving party returns the processing result of the transaction, and the transaction starting time and the transaction ending time both exist, setting a first mark for the transaction data;

if the state of the transaction data is an initial state, the initial state is that the receiving party returns the transaction as a processing state, the transaction starting time exists, and the transaction ending time does not exist, a second mark is set for the transaction data;

if the state of the transaction data is a second final state, the second final state is that the receiving party returns the processing result of the transaction, the transaction ending time exists, and the transaction starting time does not exist, a second mark is set for the transaction data;

acquiring transaction data with a second mark, and merging the transaction state, the transaction starting time, the transaction ending time, the lead line code, the loan line code, the client certificate type, the client certificate number, the application number and the credit number of all the transaction data with the second mark;

and if the state of the merged data is the first final state, determining that the merged data is the full service chain monitoring information.

In some possible embodiments, before the obtaining the routing rule corresponding to the head-of-line, the method further includes:

acquiring configuration information of a routing switch participating in a loan line;

filtering out the participation lines of which the routing switches are in the closed state in the routing switch configuration information;

acquiring project configuration information of a lead line;

acquiring an asset admission condition from the project configuration information of the lead row;

and filtering out the participating lines which do not accord with the asset admission condition according to the asset admission condition.

In some possible embodiments, the filtering out the lending lines unsuitable for the lead line according to the routing rule and the lending line verification index data specifically includes:

filtering the participatory loan rows which are not suitable for the lead row according to the comparison result of the plurality of participatory loan row verification index data and the routing rule;

the loan column check index data includes any plurality of the following: abnormal fusing, approval and time efficiency of credit granting, whether a rejected record exists in the same client in about X days, the credit granting passing rate of the last X months and the credit granting rejection rate of the last X months; wherein X is a positive integer.

In some possible embodiments, after generating a plurality of metric data for use by the routing rule according to the all-service chain monitoring information, the method further includes:

generating index data related to credit granting sequencing according to the full service chain monitoring information, wherein the index data comprises any more than one of the following data: response rate, payment success rate, capital cost and approval time efficiency of credit granting;

after filtering out the lending lines unsuitable for the lead line according to the routing rule and the plurality of lending line verification index data, the method further comprises the following steps:

acquiring index data related to credit ordering;

determining the credit granting value of each credit participating bank according to any more of response rate, payment success rate, capital cost and credit granting approval timeliness;

and determining the priority of the available reference and credit lines according to the credit granting value of each reference and credit line.

In some possible embodiments, determining the credit granting score of each participating and lending bank according to any more of response rate, loan success rate, capital cost and time of credit granting approval includes:

and calculating the credit granting value of each credit line according to the following formula:

credit score of the ginseng-credit row is (first weight (ranking number after the first weight (1+ a) is sorted according to the ascending order of min (capital cost)) + second weight (ranking number after the second weight (1+ b) is sorted according to the ascending order of min (response rate)) + third weight (ranking number after the second weight (1+ c) is sorted according to the ascending order of max (paying success rate)) + fourth weight (ranking number after the second weight (1+ d min (approval time))/4); the sum of the first weight, the second weight, the third weight and the fourth weight is 1;

in some possible embodiments, the sending of the credit application transaction to the available lending bank specifically includes:

when the credit distribution mode in the product configuration information of the lead agency is to search a plurality of co-partners, sending credit application transactions to all available credit participation agencies at the same time, and receiving all returned results;

when the credit distribution mode in the product configuration information of the lead rows is to search united parties from home to home, performing ascending ordering according to credit scores of available credit rows, sending credit application transactions one by one, and if the current credit row returns a credit failure result, sending the credit application transaction to the next credit row; and if the current credit-participating bank returns a successful credit-granting result, no credit-granting application transaction is sent to the subsequent credit-participating bank.

In a second aspect, a data processing method for syndicated credits is provided, the method comprising the steps of:

acquiring multiple transaction data of the same transaction;

generating a plurality of index data related to the credit sequence according to the full service chain monitoring information; wherein the plurality of index data related to the credit utilization sequence comprise any plurality of the following data: response rate, cash deposit success rate, capital cost and time efficiency of approval by letter;

receiving a message application information;

acquiring a corresponding credit application result according to the credit application information;

acquiring a plurality of index data related to the credit ordering;

calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence;

sorting the ginseng and credit lines in an ascending order according to the credit value of each ginseng and credit line;

and sending the credit application transactions to the credit participating lines one by one according to the determined sequence after sorting.

In some possible embodiments, calculating the credit consumption value of each credit-participating line according to the index data related to the credit consumption ranking specifically includes:

and calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence and a preset weight.

Specifically, the credit line credit value is determined according to the following formula:

the credit score of the ginseng row is ═ 4 (first weight (rank number after sorting by min (capital cost))/4 (first weight (rank number after sorting by min (capital cost)) and second weight (rank number after sorting by 1+ b) and by min (response rate))/third weight (rank number after sorting by 1+ c and by max (cash placement success rate))/fourth weight (rank number after sorting by 1+ d min (credit approval time))/4;

wherein a sum of the first weight, the second weight, the third weight, and the fourth weight is 1.

In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements any one of the data processing methods for syndication credits described above.

In a fourth aspect, an embodiment of the present invention provides a computer device, including:

one or more processors;

storage means for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the data processing methods for syndication credits described above.

In a fifth aspect, there is provided a data processing system for syndicated credits, the system comprising:

the first server is used for acquiring multiple transaction data of the same transaction; carrying out data distribution processing on the multiple transaction data to determine multiple normal transaction data; integrating and assembling multiple normal transaction data from the same transaction to the end of the service processing from the service request to obtain the monitoring information of the whole service chain; generating a plurality of index data associated with routing rules according to the full service chain monitoring information;

the second server is used for acquiring a routing rule corresponding to the head-pulling line; according to the routing rule, acquiring a plurality of participating credit line verification index data corresponding to the routing rule from the index data associated with the routing rule; filtering the participatory loan rows which are not suitable for the lead row according to the routing rule and the plurality of participatory loan row verification index data, and determining available participatory loan rows; and sending a credit application transaction to the available credit-participating bank.

In a sixth aspect, there is provided a data processing system for syndicated credits, the system comprising:

the first server is used for acquiring multiple transaction data of the same transaction; carrying out data distribution processing on the multiple transaction data to determine multiple normal transaction data; integrating and assembling multiple normal transaction data from the same transaction to the end of the service processing from the service request to obtain the monitoring information of the whole service chain; generating a plurality of index data related to the credit sequence according to the full service chain monitoring information; wherein the plurality of index data related to the credit utilization sequence comprise any plurality of the following data: response rate, cash deposit success rate, capital cost and time efficiency of credit approval;

the second server is used for receiving the information of the user information application; acquiring a corresponding credit application result according to the credit application information; acquiring a plurality of index data related to the credit ordering; calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence; sorting the ginseng credit lines in an ascending order according to the credit values of the ginseng credit lines; and sending the credit application transactions to the credit participating lines one by one according to the determined sequence after sorting.

In a seventh aspect, a data processing system for syndicated credits is provided, the system comprising:

the first server is used for acquiring multiple transaction data of the same transaction; carrying out data distribution processing on the multiple transaction data to determine multiple normal transaction data; integrating and assembling multiple normal transaction data of the same transaction from the service request to the service processing end to obtain full-service chain monitoring information; generating a plurality of index data according to the full service chain monitoring information; wherein the plurality of index data includes: the multiple participating and lending lines verify the index data and the multiple index data related to the credit using sequence;

the second server is used for receiving the credit application information and acquiring a routing rule corresponding to the lead line according to the credit application information; according to the routing rule, acquiring a plurality of participating credit line verification index data corresponding to the routing rule from the plurality of index data; filtering the participatory loan rows which are not suitable for the lead row according to the routing rule and the plurality of participatory loan row verification index data, and determining available participatory loan rows; sending a credit application transaction to the available credit-participating bank; and receiving the information of the information application; acquiring a corresponding credit application result according to the credit application information; acquiring a plurality of index data related to the credit ordering; calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence; sorting the ginseng credit lines in an ascending order according to the credit values of the ginseng credit lines; and sending the credit application transactions to the credit participating lines one by one according to the determined sequence after sorting.

The technical scheme has the following beneficial effects:

the technical scheme defines the main requirements of service data monitoring, can meet the service and technical data required by route calculation, and ensures the existing service requirements and possible future service expansion;

the technical scheme can realize multi-dimensional rule intelligent routing management.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of a data processing method for syndicated credits according to an embodiment of the present invention;

FIG. 2 is a flow chart of another data processing method for syndicated credits according to an embodiment of the present invention;

FIG. 3 is a general architecture diagram of a data processing method for syndicated credits according to an embodiment of the invention;

FIG. 4 is a flow diagram of a rule chaining process for a trust service according to an embodiment of the present invention;

FIG. 5 is a flow chart of a regular chain process applicable to trusted services according to an embodiment of the present invention;

FIG. 6 is a functional block diagram of a computer-readable storage medium of an embodiment of the present invention;

FIG. 7 is a functional block diagram of a computer device of an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The inventor of the present application finds in research that at least one of the following defects exists in the prior art: (1) the prior art does not control the cost in the selection of the capital channel. (2) The reliability is not controllable when the prior art selects the funder. (3) The tie-down party and the cooperative fund party are in close binding relationship, and if the tie-down party and the cooperative fund party need to cooperate, the butt joint is needed again, so that the overall cost and the period are increased. (4) Transaction data is scattered and data quality is worrisome, resulting in an inability to select desired partner funders from multiple dimensions.

To overcome at least one of the above-mentioned drawbacks, a primary object of the embodiments of the present invention is to clarify the main requirements of service data monitoring, meet service and technical data required for routing computation, and ensure the existing service requirements and possible future service expansion.

In order to ensure the close binding relationship between the lead line and the participating credit line in the combined credit business scene, different rules such as capital cost, reliability, processing timeliness and the like need to be formulated, and in order to enable the rules to better meet the current and future expectations, different processing and statistics need to be carried out on each transaction generated by the two parties according to the rules, and finally a data basis is provided for the multi-dimensional rule intelligent routing management.

Example one

Fig. 1 is a flowchart of a data processing method for syndicated credits according to an embodiment of the present invention. The method comprises the following steps:

s110, acquiring multiple transaction data of the same transaction;

s120, carrying out data distribution processing on the multiple transaction data to determine multiple normal transaction data;

in some embodiments, this step performs data offloading processing according to the return state of the transaction result encapsulation object. Specifically, the object tradresultdto is encapsulated according to the transaction result encapsulated by the transaction data in a unified manner, the transaction with the return state of non-Error is a normal transaction (data), and the transaction with the return state of Error is an abnormal transaction (data).

S130, integrating and assembling multiple normal transaction data of the same transaction from the initiation of the service request to the completion of the service processing to obtain full-service chain monitoring information;

s140, generating a plurality of index data used by the routing rule or associated with the routing rule according to the full service chain monitoring information;

s150, obtaining a routing rule corresponding to the head-pulling row;

s160, according to the routing rule, acquiring a plurality of credit participation row check index data corresponding to the routing rule from the index data used by the routing rule;

s170, filtering out the participatory loan rows which are not suitable for leading rows according to the routing rule and the verification index data of the participatory loan rows, and determining the available participatory loan rows;

and S180, sending a credit application transaction to the available credit-participating bank.

In some embodiments, the data splitting processing is performed on the multiple transaction data in step S120, and after determining the multiple normal transaction data, the method may further include:

if the occurrence frequency of the abnormal transaction data reaches the preset frequency within the appointed time, the transaction is marked as fusing;

and sending a fusing notice, wherein the fusing notice is used for indicating that the transaction is fused.

Specifically, data distribution processing is carried out on transaction data, and abnormal transaction data are determined; if the occurrence frequency of the abnormal transaction data reaches the preset frequency within the appointed time, the transaction is marked as fusing; sending a fusing notification to a gateway; or sending a fusing notice to a related party except the gateway; wherein the fusing notification is to indicate that the transaction is fused.

In some embodiments, the step S130 performs integration and assembly processing on multiple normal transaction data of the same transaction from the initiation of the service request to the completion of the service processing to obtain the full service chain monitoring information, which may specifically include:

In some embodiments, before the step S150 obtains the routing rule corresponding to the head-up line, the following steps may be further included:

filtering out the participation lines of the routing switch in the closed state in the routing switch configuration information;

acquiring project configuration information of a lead line;

In some embodiments, the filtering out the lending lines unsuitable for the lead line in step S170 according to the routing rule and the verification index data of the lending lines may specifically include:

filtering the participatory loan rows which are not suitable for leading rows according to the comparison result of the plurality of participatory loan row verification index data and one or more routing rules;

the loan column verification index data includes any plurality of the following: abnormal fusing, approval and time efficiency of credit granting, whether a rejected record exists in the same client in about X days, the credit granting passing rate of the last X months and the credit granting rejection rate of the last X months; wherein X is a positive integer.

The calculation formula of abnormal fusing is as follows:

if X Error state transactions occur within Y time, recording as abnormal fusing;

recovering the transaction after Z minutes of the last abnormal fusing;

wherein, X is the fusing monitoring number of strokes (positive integer), Y is the fusing time interval (positive integer, unit minute), and Z is the fusing failure time (positive integer, unit minute).

The response rate calculation formula is as follows:

the response rate is sum (normal state monitoring data transaction start time-normal state monitoring data transaction end time)/sum (monitoring data).

The credit passing rate of about X months, with leading and loan behavior dimensions, collects the monitoring data to generate final state data which can be directly used by the routing platform through the following formula, and writes the final state data into the elastic search:

and (4) the credit passing rate of the near X months is the number of credit passing of the near X months/the total number of credit passing of the near X months.

The credit rejection rate of the last X months is obtained by summarizing the monitoring data through the following formula to generate final state data which can be directly used by a routing platform according to the dimensions of leading lines and participating credits, and the final state data is written into an elastic search:

and (4) the near X month credit refusal rate is the number of the near X month credit refusals/the total number of the near X months credit.

The calculation method for whether the same client has rejected records in about X days is as follows:

and recording the rejected records of the same client in about X days by taking a lead line, a participation line, a certificate type and a certificate number as grouping conditions, and recording the transaction date when the rejected records are rejected.

The following illustrates a process of generating abnormal fusing index data according to the full service chain monitoring information:

sample data table for Error status:

line of Shen-lending	Sample data	Transaction time
			Line 1 for Shen-lending	Sample data 1	2021-11-09 10:00
Line 1 for Shen-lending	Sample data 2	2021-11-09 10:01
			Line 1 for Shen-lending	Sample data 3	2021-11-09 10:05
Line 1 for Shen-lending	Sample data 4	2021-11-09 10:08
			Line 1 for Shen-lending	Sample data	5	2021-11-09 12:01
Shen lending line 2	Sample data 6	2021-11-09 12:03

Acquiring a transaction result encapsulation object TradeResultDto from sample data, counting time from the first sample data which is not abnormally fused and counted and has a return state of Error by using credit participation behavior statistics dimensionality, if sample data of X Error states appears in total within Y minutes, determining that abnormal fusing is needed by the credit participation line, and recording the credit participation line into redis; and if the sample data of the Error state does not reach the X pen in Y minutes, counting again when the sample data of the Error state appears next time.

And (4) counting the time from the last time of abnormal fusing, and deleting the participating-in-credit line from redis after Z minutes, wherein the fusing is recovered.

Example (c): the number of fusing monitoring strokes X is 3, the fusing time interval Y is 10, and the fusing failure time Z is 30.

The first step is as follows: the first Error state sample data 1 of the reference loan row 1 occurs at a time of 2021-11-0910: 00, and the time is taken as a reference, and within Y minutes, namely 10 minutes, the total of 4 transactions are sample data 1, sample data 2, sample data 3 and sample data 4 respectively.

The second step is that: and if the Error state data of the reference line 1 in Y time is 4 in total and is greater than the configured fusing monitoring number of 3, recording that the reference line 1 is abnormally fused in redis.

The third step: and counting down for Z minutes by using the TTL (time to live) mechanism of the redis based on the occurrence time 2021-11-0910: 08 of the sample data 4, namely automatically deleting the abnormally fused reference line 1 from the redis after the Z minutes.

The fourth step: sample number 5 of reference row 1 produced Error status data at 2021-11-0912: 01, no new Error status data was produced for the next Y minutes, i.e., 10 minutes, and no abnormal fusing was recorded.

The fifth step: the sample data 6 in the reference row 2 generates Error status data in 2021-11-0912: 03, and no new Error status data is generated in the subsequent Y minutes, i.e. 10 minutes, so that abnormal fusing is not recorded.

In some embodiments, after generating a plurality of metric data for use by the routing rule according to the all-service chain monitoring information, the method further includes:

specifically, the statistical data of the capital cost is from the configuration items, and the capital cost is counted by the dimension of the lead line and the participator line, as an example, the following:

item 1, the lead line is lead line 1, the reference credit line is reference credit line 1, and the capital cost is 58%;

item 2, the lead line is lead line 1, the reference credit line is reference credit line 2, and the capital cost is 46%;

item 3, with lead row 2 and refer row 1, has a capital cost of 56%.

Specifically, in one example, the deposit success rate is a statistic of the total data, and is not a deposit success rate in the near X days, and the deposit success rate is calculated according to the following formula:

the payout success rate is sum (number of successful payout strokes)/sum (number of payout strokes).

Specifically, the generation process of the credit approval aging index data is as follows:

collecting the monitoring data by using the leading line and the loan participation behavior dimension through the following formula to generate final state data which can be directly used by a routing platform, and writing the final state data into an elastic search:

the time limit of the near X day credit approval is sum (end time of each credit transaction on near X days-start time of each credit transaction on near X days)/sum (number of credits of near X day credit transactions).

Here, the credit transaction refers to the assembled credit integrated data.

After filtering out the lending lines unsuitable for the lead line according to the routing rule and the plurality of lending line verification index data, the method may further include:

acquiring index data related to credit ordering;

credit granting score of the ginseng row is (first weight (rank number after the first weight is sorted by min (capital cost)) in an ascending order) + second weight (rank number after the second weight is sorted by 1+ b in an ascending order by min (response rate)) + third weight (rank number after the first weight is sorted by 1+ c in an ascending order by max (payment success rate)) + fourth weight (rank number after the first weight is sorted by 1+ d min (credit granting approval time))/4; the sum of the first weight, the second weight, the third weight and the fourth weight is 1;

the minimum capital cost is represented by min (capital cost), the minimum response rate is represented by min (response rate), the maximum release success rate is represented by max (release success rate), and the minimum credit approval time is represented by min (credit approval time). The sort number is a natural number obtained by ascending, the ascending sort number value of the 1 st bit after sorting is 1, the ascending sort number of the 2 nd bit is 2, the ascending sort number of the 3 rd bit is 3, the ascending sort number of the 4 th bit is 4, the ascending sort number of the 5 th bit is 5, and so on. The sorting numbers sorted in ascending order of min (capital cost) are shown as: and all the participating and lending lines obtain respective minimum capital cost and obtain respective serial numbers according to ascending order.

And determining the priority of the available reference and credit lines according to the credit value of each reference and credit line. The lower the score, the higher the priority.

In an optional embodiment, values of the parameter a, the parameter b, the parameter c, and the parameter d are equal to 0.1, and values of the parameter a, the parameter b, the parameter c, and the parameter d may not be equal to 0.1 to 0.5 in other embodiments.

In other alternative embodiments, the above formula may be replaced by only using any one, two or three index data of response rate, loan success rate, capital cost and time of approval of credit, and accordingly, the weight is one weight, two weights or three weights.

Examples include, but are not limited to: calculating the credit value of each credit line according to any one of the following formulas:

credit score of the ginseng-credited row is (first weight (ranking number after sorting by min (capital cost) in ascending order + second weight (ranking number after sorting by min (response rate)) + third weight (ranking number after sorting by max (payment success rate)) and 3; the sum of the first weight, the second weight and the third weight is 1; or,

credit score of the ginseng credit row is (first weight (rank number after the first weight is sorted by min (capital cost)) and plus second weight (1+ b) is sorted by min (response rate)) and plus fourth weight (rank number after the fourth weight is sorted by 1+ d min (credit approval time))/3; the sum of the first weight, the second weight and the fourth weight is 1; or,

the credit score of the ginseng credit line is second weight (rank number after 1+ b is sorted according to the ascending order of min (response rate)) + third weight (rank number after 1+ c is sorted according to the ascending order of max (cash deposit success rate)) + fourth weight (rank number after 1+ d is sorted according to the ascending order of min (credit approval time))/3; the sum of the second weight, the third weight and the fourth weight is 1;

credit granting score of the ginseng credit line is (first weight (rank number after ascending sorting according to min (capital cost))/3, wherein the rank number is the third weight (1+ c rank number after ascending sorting according to max (cash release success rate))/3; the sum of the first weight, the third weight and the fourth weight is 1;

the credit score of the ginseng credit line is (the ranking number after the first weight (1+ a) is sorted according to the ascending order of min (capital cost))) + the second weight (1+ b) is sorted according to the ascending order of min (response rate))/2, and the sum of the first weight and the second weight is 1;

the credit score of the ginseng row is (the ranking number after the first weight (1+ a) is sorted according to the ascending order of min (capital cost))/2; the sum of the first weight and the third weight is 1.

The credit score of the ginseng credit line is the first weight (1+ a) ranking number sorted by min (capital cost) ascending order); the first weight value is 1.

In some embodiments, the step S180 of sending the credit application transaction to the available lending bank may specifically include:

The beneficial technical effects of the technical scheme include:

the technical scheme of the embodiment of the invention displays the complete information of the currently issued transaction application for the user in the form of integrated data, avoids screening expected data from a plurality of transaction information, and reduces the complexity of data use.

According to the technical scheme of the embodiment of the invention, the client information, the transaction initiation time, the transaction ending time and the current transaction conclusion returned by the partner in the current partner transaction can be displayed in the integrated data, so that the data intuitiveness is increased.

The embodiment of the invention can realize the self-defined credit distribution mode of the lead line and can be changed at any time according to the market situation.

The method for searching the multiple co-partners can be used for simultaneously connecting the multiple co-partners, and the lead party automatically selects the co-partners for subsequent cooperation according to the credit granting result returned by the multiple co-partners.

The embodiment of the invention only connects one union party each time in a mode of finding the union party one by one, and when one union party passes the examination and approval, the lead party defaults to cooperate with the union party.

Example two

Fig. 2 is a flowchart of a data processing method for syndicated credits according to an embodiment of the present invention. As shown in fig. 2, the method comprises the steps of:

s201, acquiring multiple transaction data of the same transaction;

s202, carrying out data distribution processing on multiple transaction data, and determining multiple normal transaction data;

s203, integrating and assembling multiple normal transaction data of the same transaction from the initiation of the service request to the completion of the service processing to obtain full-service chain monitoring information;

s204, generating a plurality of index data related to the message sequence according to the full service chain monitoring information; wherein, the plurality of index data related to the credit ordering comprise any plurality of the following: response rate, cash deposit success rate, capital cost and time efficiency of credit approval;

s205, receiving the information of the user information application;

s206, acquiring a corresponding credit application result according to the credit application information;

s207, acquiring a plurality of index data related to the credit ordering;

s208, calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence;

s209, sorting the ginseng and lending lines in an ascending order according to the credit values of the ginseng and lending lines;

and S210, sending the credit application transactions to the loan participation rows one by one according to the determined sequence after sorting.

In some embodiments, step S203 performs integration and assembly processing on multiple normal transaction data of the same transaction from the initiation of the service request to the completion of the service processing to obtain the full service chain monitoring information, which specifically includes:

In some embodiments, in step S208, calculating the credit consumption value of each credit-participating line according to the index data related to the credit consumption ranking may specifically include:

calculating the credit value of each credit line according to the index data related to the credit sequence and the following formula:

wherein the sum of the first weight, the second weight, the third weight and the fourth weight is 1.

In order to reduce the difference in the scores generated by the ascending ranking numbers, the smallest possible number is used, the number 0.1 is selected, and other numbers may be used as long as they are as small as possible. The weight 1, the weight 2, the weight 3, and the weight 4 may be the same or different, and the sum of the 4 weights is equal to 1.

In some embodiments, the credit approval aging is based on the dimension of lead action + loan action, the monitoring data is summarized by the following formula to generate the final state data that can be directly used by the routing platform, and the final state data is written into the Elasticsearch:

the credit approval time is sum (credit transaction end time-credit transaction start time)/sum (credit monitoring data number).

The beneficial technical effects of the technical scheme include:

in the embodiment of the invention, the lead line can adjust the overall rule judgment at any time by modifying the weight according to the policy of the lead line.

In the embodiment of the invention, the configuration of each lead bank is independent and does not influence each other, and different combinations are allowed to be selected from capital cost, response rate, loan-release success rate and credit approval time effectiveness besides different weight configuration values, so as to generate a new credit value calculation formula for credit participation.

EXAMPLE III

Fig. 3 is an overall architecture diagram of a data processing method for syndicated credits according to an embodiment of the present invention. The method is executed by a combined loan platform, and the combined loan platform comprises a plurality of services: service intelligent matching and distribution service, service monitoring service, internal gateway service, external gateway service, etc. The services can be respectively deployed on independent servers or all deployed on one server, or an internal gateway service, an external gateway service and a business intelligence matching and distribution service are deployed on one server together. The gateway provides interior gateway services and exterior gateway services functions. In some embodiments, transaction data of the gateway is derived from a service intelligent matching and distribution service, in the embodiment, a service monitoring service executes a monitoring method to generate routing index data for the service intelligent matching and distribution service to use, the service intelligent matching and distribution service executes an intelligent routing method in the using process to generate sample data for the monitoring method, and the two are combined to form a closed loop for data use and data generation, so that a combined loan service is realized; in other embodiments, the closed loop may not be formed, and the gateway may collect the transaction data by itself. As shown in fig. 3, the processing method of the combined loan platform includes the following steps:

and S1, the gateway sends each transaction datum to the business monitoring service, and the business monitoring service receives each transaction datum sent by the gateway and writes each transaction datum into the message queue.

And S2, executing data distribution processing on each transaction data in the message queue.

Specifically, a great deal of transaction data is generated in the process of processing upstream and downstream services by the joint loan platform, if all transaction data are processed by one downstream service, great pressure is applied to the downstream service, in order to reduce the pressure of the downstream service monitoring service on sample data acquisition and processing, data distribution is performed from a data source, and a large data stream is divided into small tributary data according to the data state. The data distribution processing logic may include the following specific steps:

s21, packaging the object TradeResultDto based on the transaction result packaged by the transaction unification package, returning the transaction with Error state, marking as abnormal transaction data, entering the abnormal transaction data processing flow, which comprises the following step S3. Wherein each transaction corresponds to a transaction result encapsulated object instance.

S22, packaging the object TradeResultDto based on the transaction result packaged by the transaction unification, returning the transaction with the state of non-Error, marking as normal transaction data, entering the normal transaction data processing flow, which comprises the following step S4.

S3, executing abnormal transaction data processing flow;

and S31, for the sample marked as 'abnormal data' in the processing, if the occurrence number of the abnormal transaction data reaches the preset number in the appointed time, marking the transaction as fused. The abnormal transaction data includes data that is not successfully transmitted, which includes but is not limited to any of the following cases:

the transaction fails because the transaction is not normally delivered due to network jitter and network disconnection of the joint loan platform.

Due to the abnormal service of the outbound gateway of the combined loan platform, the transaction cannot be normally sent, and the transaction fails.

The transaction link is incomplete because the recipient fails to answer the transaction within the specified time.

The transaction link is incomplete because the recipient actively blows the transaction.

And S32, sending a fusing notice to the gateway. Specifically, this step may include notifying the affiliated lending platform that the type of transaction between the lead and participating lenders is fused, and then the transaction execution is not triggered. In particular to the fusing of the transaction interface.

And S33, sending a fusing notice to the relevant parties except the gateway. Specifically, the step may send the fusing notification in a manner including, but not limited to, short message, email, or voice communication, so as to notify the relevant personnel that the specified transaction type of a certain lead and the participating lending line is fused.

S4, executing normal transaction data processing flow, which includes executing integration and assembly processing to normal transaction.

In the step, the business data integration and assembly processing is carried out on the samples marked as the normal transaction data. In this embodiment, the credit application, credit approval, loan transaction and other transactions participating in the lending bank are all asynchronous transactions, and in this scenario, the service monitoring service will initiate the same transaction from the service request to the end of the service processing to assemble and integrate the service data, and combine multiple requests into a complete transaction data. The specific processing logic is as follows:

s41: in order to prevent data duplicate processing, the unique transaction ID is used as a unique basis for determining whether the transaction data exists in a message queue, such as a Remote Dictionary Server (rdis), and if the transaction data does not exist, the parsing processing in the subsequent step S42 is triggered to be executed; if so, skipping the transaction data. The parsing process may tag each piece of data.

S42: if the transaction data needs to be analyzed and processed, marking the transaction data, wherein the specific processing process comprises the following steps:

a. if the data state is the final state, that is, the receiving party explicitly returns the processing result of the transaction, including: the processing is successful and the processing is failed. And the transaction start time, the transaction end time exist, then the data is marked as "unique";

b. if the data state is an initial state, namely the receiver returns that the transaction is a processing state, the transaction starting time exists, and the transaction ending time does not exist, the data is marked as 'combination';

c. if the data state is the final state, that is, the receiving party explicitly returns the processing result of the transaction, including: the process is successful, the process is failed, and the transaction end time exists and the transaction start time does not exist, then the data is marked "combined".

S43: all data marked as 'combination' are acquired and grouped in fields of jlsLeadeTradeId and jlsUnitetandeId, non-final state data (namely initial state data) are covered based on final state data, and the transaction state, the transaction starting time, the transaction ending time, the leading line code, the loan line code, the client certificate type, the client certificate number, the application number and the letter number of the 'combination' state data are all subjected to merging processing according to the rule that a plurality of pieces of final state data are covered according to the event processing time sequence. If the combined data state is a final state and the transaction start time and the transaction end time are complete, determining the data state as full service chain monitoring information, calculating the total transaction time consumption, and writing the data state into an elastic search first index: monitor _ biz _ trans _ detail _ collect _ index; and if any condition is not met, continuing to wait for the data in the 'combined' state until the data becomes the all-service chain monitoring information. The Elasticsearch refers to a distributed, free-source search and analysis engine, applicable to all types of data including text, numeric, geospatial, structured and unstructured data, and the like. And the service monitoring service writes the collected sample data, the integrated monitoring data and the integrated index data into an elastic search.

Specifically, jlsLeaderTradeId: the lead bank generates a unique transaction ID, and each time the lead bank initiates a transaction, the combined loan platform generates a unique ID. jlsUnitetandeid: and the joint loan platform generates a unique ID every time a transaction is initiated for the loan participation row. These two fields are unique IDs generated from transactions within the federated lending platform, which have advantages including:

(1) the internal generation of the transaction ID can completely avoid the influence of external factors, so that the transaction ID is repeated.

(2) Resources can be saved, and if the unique transaction is represented by using a mode of combining a plurality of fields in the transaction, the whole data length is longer than the length of the transaction ID generated internally, and additional resources are occupied.

(3) Because only one field represents a unique transaction ID, the identification is straightforward in the scenario where one lead corresponds to multiple participating lines.

Specifically, the rule that the plurality of pieces of final state data are covered according to the event processing time sequence comprises the following steps: and covering the plurality of final state data in sequence according to the ascending order of the event processing time, namely covering the data rule with early processing time by the data with late processing time.

For example: the processing time of the multi-stroke final state data is respectively as follows:

final state data 1: 2021-10-2515: 32;

final state data 2: 2021-10-2515: 33;

final state data 3: 2021-10-2516:00.

Then, when merging, the final state data 2 overwrites the final state data 1, and the final state data 3 overwrites the final state data 2.

Since different final state data cannot guarantee that all fields are ready, an overriding, rather than an alternative, manner of operation is employed.

S44: acquiring data marked as 'unique', calculating 'transaction total time consumption', and writing into an elastic search first index: monitor _ biz _ trans _ detail _ collect _ index.

S45: calculating the transaction time consumption for each transaction datum, and recording the transaction time consumption to an elastic search second index:

monitor _ sys _ trans _ detail _ collect _ index. The calculation is time-consuming for a single transaction, belongs to a technical field, and provides a monitoring basis for later-stage system operation and maintenance. The two calculations of steps S44 and S45 can be operated together in the process of single transaction parsing and data integration.

The correlation calculation formula is as follows:

the total transaction time is transaction end time-transaction start time;

transaction elapsed time-response time-request time.

And S5, with the continuous generation of the full-service chain monitoring information, the service monitoring service periodically captures the full-service chain monitoring information and generates index data required by the intelligent service matching/distributing service according to the full-service chain monitoring information.

Sample data: the original transaction data generated by each transaction is called sample data after being collected by the service monitoring service. The sample data content in the text is equal to transaction data, and the calling method is different only because the scene is different.

Monitoring data: after integration and assembly, a plurality of sample data are combined into data of a service complete transaction.

Index data: and summarizing the monitoring data to generate final state data which can be directly used by the routing platform according to the rule of the routing platform.

Specifically, the related calculation formula of the index data statistics is as follows:

the credit granting rejection rate in the near X months is the credit granting rejection quantity in the near X months/the credit granting total quantity in the near X months;

the credit passing rate in the near X months is equal to the credit passing amount in the near X months/the total credit passing amount in the near X months;

if the same client is rejected in X days, taking a lead line, a participation line, a certificate type and a certificate number as grouping conditions, and recording the transaction date when the same client is rejected; the certificate refers to the certificate of the loan applicant.

Example data:

data of	Lead the head to walk	Line of Shen-lending	Certificate type	Certificate number	Day of trade
						Monitoring data 1	Lead line 1	Line 1 for Shen-lending	Identity card	Number 1	2021-10-08
Monitoring data 2	Lead line 1	Shen lending line 2	Identity card	Number 1	2021-08-03
						Monitoring data 3	Lead row 1	Line 1 for Shen-lending	Identity card	Number 2	2021-10-01
Monitoring data 4	Lead line 1	Line 1 for Shen-lending	Identity card	Number 1	2021-09-01

As can be seen from the above example data: (1) the monitoring data 1 and the monitoring data 4 are the same client; (2) the monitoring data 2 is identical to the monitoring data 1 and the monitoring data 4 in the header line, the certificate type and the certificate number, but is different from the reference line, and is regarded as a non-identical client in use. (3) Monitoring data 3, as above, is also considered to be a non-identical client when used.

The money releasing success rate in the near X days is equal to the money releasing success quantity in the near X days/the money releasing total quantity in the near X days;

and the time limit of the loan approval is sum (total time consumed by each loan approval transaction)/the total number of the loan approval on nearly X days.

And S6, the service intelligent matching and distributing service screens out all the participated loan rows suitable for the lead row by matching with the participated loan row sample data processed by the service monitoring service according to the routing rule configured by the lead row. The specific treatment process comprises the following steps:

s61: and the business intelligent matching and distribution service acquires different parameter configurations configured by the user according to the leading line to which the transaction belongs. Various configuration data are stored in Mysql and redis, the step can preferentially obtain the configuration data from the redis, and if the parameter configuration does not exist in the redis, the parameter configuration is obtained from the Mysql. Relevant examples are as follows:

configuration 1: example lead row product configuration information

Configuration 2: example lead line item configuration information

In the example table corresponding to the configuration 2, the admission condition 3 is followed by an ellipsis to indicate that more admission condition configurations are supported. How many admission conditions are configured, how many admission conditions need to be satisfied, for example: 1 admission condition is configured in the item 1, and then the admission condition 1 is met; the project 2 is configured with a plurality of admission conditions, and then the admission conditions 1, 2 and 3 need to be satisfied.

Configuration 3: lead row routing configuration information example

Configuration 4: example of configuration information for a loan routing switch

Line of Shen-lending	Route switch
		Line 1 for Shen-lending	Opening device
Shen lending line 2	Switch (C)
		Shen lending line 3	Closing device

S62: and acquiring index data counted according to the rules, and calculating to obtain all the participating loan rows which finally meet the requirements of the lead rows based on the routing rules. The following description will be made in detail by taking two examples of the credit application and the credit approval as examples.

As shown in fig. 4, service 1 is a regular chain process applicable to the trust application service:

step S411 (i.e., step 1 of service 1): receiving a credit application, and checking the configuration 1: and (4) whether the configuration items of the product configuration before the head of the product are complete or not is judged, and if the configuration items are lacked, default configuration is used.

Step S412 (i.e., step 2 of service 1): get (query) the lead row configuration 2: all item information of the lead line item configuration "includes: and (4) participating in loan rows and asset admission conditions related to the project.

Step S413 (i.e., step 3 of service 1): according to "configuration 4: and (4) participating in route switch configuration of route switch configuration ', and filtering out all participating in route configured as ' off ' in the last step.

Step S414 (i.e., step 4 of service 1): and (3) filtering out all participating lines which do not accord with the asset admission conditions in the previous step according to the asset admission conditions obtained in the step (2). The above asset admission condition refers to configuration 2, and the data is obtained at step S512.

Step S415 (i.e., step 5 of service 1): according to the leading line to which the credit application belongs, acquiring a configuration 3: and leading the head to configure the configured routing rule.

Step S416 (i.e., step 6 of service 1): according to the routing rule obtained in step 5, obtaining (querying) statistical index data of the corresponding rule from the service monitoring service, including but not limited to any more of the following:

index data 1: abnormal fusing; the abnormal fusing means: and after transaction errors occur for many times due to network problems, unavailable service and the like, the initiator actively suspends the transaction measures for controlling risks. And recording the abnormally fused loan participation line into the redis, and directly filtering the abnormally fused loan participation line as long as the loan participation line is found in the redis during filtering, so that the abnormally fused loan participation line does not participate in subsequent transactions.

Index data 2: approval and aging are carried out through credit granting;

index data 3: whether the same client has rejected records in about X days;

index data 4: the credit passing rate of about X months;

index data 5: a credit rejection rate of approximately X months.

Step S417 (i.e., step 7 of service 1): and (4) executing the routing rules acquired in the step (5) one by one, filling the statistical data (index data) acquired in the step (6) into each routing rule, and filtering all participating rows which do not accord with the routing rules in the step (4).

For example:

routing rule 1: (ii) a last 1 month credit passage > 80%;

routing rule 2: the same customer was rejected within nearly 30 days;

routing rule 3: abnormal fusing;

routing rule 4: the rejection rate of credit was < 10% in the last 1 month;

suppose that the current service application date is: 11/9/2021

An example table of the participating lending line rule data is as follows:

the execution sequence is as follows:

first, routing rule 1 is executed: and if the credit granting pass rate of the credit line 1 in about 1 month is less than 80%, filtering, and keeping the credit line 2, the credit line 3 and the credit line 4 to participate in the next filtering execution.

Second, routing rule 2 is executed: the line 3 rejects the client within 30 days, then filters out, leaving the line 2, 4 to participate in the next filtering execution.

Third, routing rule 3 is executed: the reserved reference and credit lines are not fused, and the reference and credit line 2 and the reference and credit line 4 participate in the next filtering execution.

Step four, executing a routing rule 4: the credit rejection rate of the reserved ginseng credit line in the near 1 month is all less than 10 percent, and the ginseng credit line 2 and the ginseng credit line 4 are reserved.

Step S418 (i.e., step 8 of service 1): and obtaining index data related to credit ordering from the business monitoring service, calculating credit scores of all the credit lines according to the following formula, and then sequencing the credit lines in an ascending order according to the credit scores of the credit lines.

The credit score of ginseng (rank number after the weight 1 (1+ 0.1) is sorted according to the ascending order of min (capital cost)) + the weight 2 (rank number after the weight 1+0.1 is sorted according to the ascending order of min (response rate)) + the weight 3 (rank number after the weight 1+0.1 is sorted according to the ascending order of max (deposit success rate)) + the weight 4 (rank number after 1+0.1 min (credit approval time))/4).

Weight 1+ weight 2+ weight 3+ weight 4 is 1.

In an alternative embodiment, the credit granting score of the credit granting line may be replaced by only adopting any one, any two or any three of the capital cost, the response rate, the payment success rate and the time of credit granting approval, and accordingly, the weight amount may be changed to one, two or three, any one weight value should be 1, any two weight sum values should be 1, and any three weight sum values should be 1.

The following is an example of calculating the credit score of the credit line using any one of the index data:

the credit score of ginseng credit line is a ranking number with the weight of 1 (1+ 0.1) sorted in ascending order of min (capital cost);

the credit score of ginseng (weight 2) (rank number after sorting by min (response rate)) in ascending order;

the credit score of ginseng credit line is 3 (the rank number after sorting according to max (success rate of money put) ascending order 1+ 0.1);

the credit score of ginseng was 4 (1+0.1 min (credit approval time) sorted in ascending order).

The following is an example of calculating the credit-for-participating score using any two index data combinations:

the ginseng credit score ═ ((weight 1 × (1+0.1 × (rank ordered by min (capital cost)) in ascending order) + (weight 2 × (1+0.1 × (rank ordered by min (response rate)) in ascending order))/2);

the credit score of ginseng (weight 1 (rank ordered by min (capital cost)) and (weight 3 (rank ordered by max (deposit success rate)) in ascending order of 1+ 0.1))/2;

the credit score of ginseng (weight 1 (rank ordered by min (capital cost)) in ascending order) + (weight 4 (rank ordered by 1+0.1 min (credit approval time)) in ascending order))/2;

the credit score of ginseng (weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 3 × (1+0.1 × (rank ordered by max (deposit success rate)))/2;

the credit score of ginseng (weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 4 × (rank ordered by 1+0.1 × (credit approval time)))/2;

the credit score of ginseng was ═ ((weight 3 × (rank ordered by max (deposit success rate)) + (weight 4 × (rank ordered by 1+0.1 × (credit approval time) in ascending order))/2).

The following is an example of calculating the credit-credit score using any combination of three index data:

the ginseng credit line credit score ═ ((weight 1 × (1+0.1 × (rank ordered by min (capital cost)) plus (weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 3 × (rank ordered by 1+ 0.1) by max (cash deposit success rate)))/3);

the credit score of ginseng was ═ ((weight 1 × (1+0.1 × + rank ordered by min (capital cost)) + weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 4 × (1+0.1 × (rank ordered by time of approval for credit))/3);

the credit score of ginseng was ═ ((weight 1 × (1+0.1 × (rank ordered by min (capital cost)) plus (weight 3 × (1+0.1 × (rank ordered by max (cash deposit success rate)) + (weight 4 × (1+0.1 × (rank ordered by credit approval) time))/3);

the credit score of ginseng was ═ ((weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 3 × (rank ordered by 1+0.1 × (max (deposit success rate))) + (weight 4 × (rank ordered by 1+0.1 × (credit approval time))/3).

The time efficiency of the approval of the trust is verified by using the formula: the total transaction time is the transaction end time-transaction start time. The term "credit approval time" used herein refers to the total time consumed for the transaction of the credit approval.

The index data involved are as follows: index data 1: a response rate; index data 2: a success rate of money put; index data 3: capital cost; index data 4: and (5) approval and time efficiency are granted.

Step S419 (i.e., step 9 of service 1): according to "configuration 1: and (3) sending a credit application transaction to the available credit lines (all the obtained credit lines are available credit lines) according to the credit line sequencing order in the step (8) in a credit distribution mode in the lead line product configuration, wherein the sending rule is as follows:

searching a plurality of co-partners: and simultaneously sending credit application transactions to all available credit-participating lines in the step 8 and receiving all returned results.

Finding a union party family by family: and 8, sorting the credit awarding values in an ascending order according to the credit participating lines, and sending credit application transactions one by one. If the credit-participating bank returns the credit-granting failure result, the credit-granting application transaction is sent to the next credit-participating bank; if the credit-participating bank returns a credit-granting success result, the credit-granting application transaction is not sent to the subsequent credit-participating bank.

Step S420 (i.e., step 10 of service 1): and if no available participating and lending lines exist after filtering in the step 8, informing the lead lines of no adaptive participating and lending lines.

As shown in fig. 5, the service 2 is a regular chain process applicable to the credit-in service, and the service 2 is the credit-in service, and the credit-in service does not need to be filtered any more, and the credit-in service is directly used, which may include the following steps:

step S511 (i.e., step 1 of service 2): and the service intelligent matching and distribution service in the combined loan platform checks whether the credit application sent by the lead line meets the transaction requirements, and executes the subsequent steps after the check is passed. Specifically, the checking process may include: only the fields of the incoming transaction request are checked for readiness. There are many fields, including but not limited to: a credit application number, a lead party code, a contract number, a debit number, and the like.

Step S512 (i.e., step 2 of service 2): and the business intelligent matching and distribution service in the joint credit platform associates (inquires) the corresponding credit application result according to the credit application information. The credit application result comes from the credit application transaction of the service 1, and the credit application number is sent in the credit application transaction through the service 2 to be in one-to-one association. The credit application result is irrelevant to the overall intelligent matching distribution of the service and the monitoring of the service data, and is only used for sending the message to the credit participating bank.

Step S513 (i.e., step 3 of service 2): and intelligently matching the business in the joint loan platform with index data related to the credit ordering obtained by the distribution service from the business monitoring service, calculating the credit value of each credit line according to the following formula, and then sequencing the credit lines in an ascending order according to the credit value of each credit line.

The credit score for the ginseng row is (rank after weight 1 (rank after sorting by min (capital cost)) in ascending order) + weight 2 (rank after sorting by min (response rate) + weight 3 (rank after sorting by 1+0.1 max (cash release success rate)) in ascending order) + weight 4 (rank after sorting by 1+0.1 min (approval time by credit))/4.

Weight 1+ weight 2+ weight 3+ weight 4 is 1.

In an alternative embodiment, the credit-applying value of the participating credit line can be replaced by only adopting any one, any two or any three of the capital cost, the response rate, the payment success rate and the time of the credit approval, and accordingly the weight amount can be changed into one, two or three, any one weight value is 1, any two weight sum values are 1, and any three weight sum values are 1.

An example of calculating the credit-for-credit score value using any one of the index data is as follows:

the ranking numbers are ranked in ascending order of min (capital cost) by the credit line score value of 1 (1+ 0.1);

the credit line credit score value is weight 2 (rank number after sorting in the ascending order of min (response rate) 1+ 0.1);

the credit line credit score value is 3 (weight 1+ 0.1) and the ranking number is sorted according to max (cash deposit success rate);

the ginseng rows were ranked in ascending order with a weight of 4 (1+0.1 min (time to approval by letter)).

An example of calculating the credit worthiness value for the credit line using any two index data combinations is as follows:

the ginseng credit line credit value ═ ((weight 1 × (1+0.1 × + rank ordered in ascending order of min (capital cost)) + ((weight 2 × (1+0.1 × (rank ordered in ascending order of min (response)))/2);

the credit line credit score value ═ ((weight 1 × (1+0.1 × (rank ordered by min (capital cost)) in ascending order) + (weight 3 × (1+0.1 × (rank ordered by max (cash deposit success rate)) in ascending order))/2);

credit score for ginseng, ((weight 1 × (1+0.1 × + serial number sorted in ascending order of min (capital cost)) + and (weight 4 × (1+0.1 × (serial number sorted in ascending order of credit approval aging))/2);

the value of credit for the ginseng row ═((weight 2 × (1+0.1 × (rank ordered by min (response rate)) + (weight 3 × (1+0.1 × (rank ordered by max (deposit success rate)))/2;

the value of the credit line ═ ((weight 2 × (rank ordered by min (response rate)) + (weight 4 × (rank ordered by 1+0.1 × (time-dependent approval by credit)) in ascending order))/2;

the score value for the ginseng row ═ ((weight 3 × (rank ordered by max (deposit success rate)) + (weight 4 × (rank ordered by 1+0.1 × (time-dependent approval by letter)) 2).

An example of calculating the credit-for-line credit value using any three index data is as follows:

the value of the credit line credit ═((weight 1 × (1+0.1 × + row number sorted in the ascending order of min (capital cost)) + (weight 2 × (1+0.1 × (row number sorted in the ascending order of min (response)) + (weight 3 × (1+0.1 × (row number sorted in the ascending order of max (deposit success rate)))/3);

the ginseng credit row credit value ═ ((weight 1 × (1+0.1 × + row rank ordered in ascending order of min (capital cost)) + ((weight 2 × (1+0.1 × (row rank ordered in ascending order of min (response)) + (weight 4 × (1+0.1 × (row rank ordered in ascending order of time of approval))/3);

the credit score value for ginseng row ═ ((weight 1 × (1+0.1 × + rank ordered by min (capital cost)) plus (weight 3 × (1+0.1 × (rank ordered by max (cash deposit success rate)) + (weight 4 × (1+0.1 × (rank ordered by credit approval) in ascending order))/3;

the credit score for the ginseng row ═((weight 2 × (1+0.1 × (rank ordered in ascending order of min (response rate)) + (weight 3 × (rank ordered in ascending order of max (deposit success rate)) + (weight 4 × (rank ordered in ascending order of 1+0.1 × (approval by letter) time))/3.

The time efficiency of the credit approval is represented by the following formula: the total transaction time is the transaction end time-transaction start time. The term "time-to-use approval" is used herein to refer to the total time consumed for the transaction using the time-to-use approval.

The ranking-related metric data are as follows: index data 1: a response rate; index data 2: capital cost; index data 3: a success rate of money put; index data 4: and (5) approving the aging by using a letter.

Step S514 (i.e., step 4 of service 2): according to the loan participation lines sequenced in the step 313, the credit application transactions are sent one by one.

Example four

FIG. 6 is a functional block diagram of a computer-readable storage medium of an embodiment of the present invention. As shown in fig. 6, an embodiment of the present invention further provides a computer-readable storage medium 600, a computer program 610 is stored in the computer-readable storage medium 600, and when executed by a processor, the computer program 610 implements the steps of any one of the data processing methods for syndication credits in the above embodiments.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

EXAMPLE five

The embodiment of the present invention further provides a computer device, as shown in fig. 7, including one or more processors 701, a communication interface 702, a memory 703 and a communication bus 704, where the processors 701, the communication interface 702, and the memory 703 complete mutual communication through the communication bus 704.

A memory 703 for storing a computer program;

the processor 701 is configured to implement the following steps when executing the program stored in the memory 703:

acquiring multiple transaction data of the same transaction;

performing data distribution processing on the multiple transaction data to determine multiple normal transaction data;

generating a plurality of index data associated with routing rules according to the full service chain monitoring information;

acquiring a routing rule corresponding to a head-pulling row;

according to the routing rule, acquiring a plurality of participating credit line verification index data corresponding to the routing rule from the index data associated with the routing rule;

filtering the participatory loan rows which are not suitable for the lead row according to the routing rule and the plurality of participatory loan row verification index data, and determining available participatory loan rows;

In some embodiments, in the processing of the processor 701, performing data distribution processing on the multiple transaction data, and after determining multiple normal transaction data, further includes:

In some embodiments, in the processing of the processor 701, the integrating and assembling the multiple normal transaction data, which are initiated from the service request to the service processing end in the same transaction, to obtain the full service chain monitoring information specifically includes:

In some embodiments, before the obtaining the routing rule corresponding to the head-of-line, the processing of the processor 701 further includes:

acquiring configuration information of a routing switch participating in a lending line;

acquiring project configuration information of a lead line;

and filtering out participating credit lines which do not accord with the asset admission condition according to the asset admission condition.

In some embodiments, in the processing of the processor 701, the filtering out the participated credit lines which are not suitable for the lead line according to the routing rule and the plurality of participated credit line verification index data specifically includes:

In some embodiments, after generating a plurality of metric data for use by the routing rule according to the full service chain monitoring information, the processing of the processor 701 further includes:

acquiring index data related to credit ordering;

the credit score of the ginseng credit line is (the ranking number of a first weight (1+ a) sorted according to the ascending order of min (capital cost)) + (the ranking number of a second weight 2 (1+ b) sorted according to the ascending order of min (response rate)) + (the ranking number of a third weight (1+ c) sorted according to the ascending order of max (payment success rate)) + (the ranking number of a fourth weight) (1+ d min (time for approval for credit approval))/4; the sum of the first weight, the second weight, the third weight, and the fourth weight is 1;

In some embodiments, the sending of the credit application transaction to the available lending bank in the processing of the processor 701 specifically includes:

when the credit distribution mode in the lead bank product configuration information is to search for united parties one by one, performing ascending sequencing according to credit scores of available credit banks, sending credit application transactions one by one, and if the current credit bank returns a credit failure result, sending the credit application transactions to the next credit bank; and if the current credit-participating bank returns a successful credit-granting result, no credit-granting application transaction is sent to the subsequent credit-participating bank.

acquiring multiple transaction data of the same transaction;

generating a plurality of index data related to the credit sequence according to the full service chain monitoring information; wherein the plurality of index data related to the credit utilization sequence comprise any plurality of the following data: response rate, cash deposit success rate, capital cost and time efficiency of credit approval;

receiving a message application message;

acquiring a corresponding credit granting application result according to the credit using application information;

acquiring a plurality of index data related to the credit ordering;

sorting the ginseng credit lines in an ascending order according to the credit values of the ginseng credit lines;

if the state of the transaction data is a first final state, the first final state is that the receiving party returns the processing result of the transaction, and the transaction starting time and the transaction ending time exist, setting a first mark for the transaction data;

In some embodiments, in the processing of the processor 701, calculating, according to the index data related to the credit utilization sequence, a credit utilization value of each credit-participating row includes:

and calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence and the following formula:

Processor 701 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), etc.; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.

Memory 703 may include mass storage for data or instructions. By way of example, and not limitation, memory 303 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, magnetic tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. The memory 803 may include removable or non-removable (or fixed) media, where appropriate. In a particular embodiment, the memory 803 is a non-volatile solid-state memory. In certain embodiments, memory 803 comprises Read Only Memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.

The communication bus 704 includes hardware, software, or both for coupling the above-described components to each other. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. A bus may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Although the present application provides method steps as in an embodiment or a flowchart, more or fewer steps may be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A data processing method for syndicated credits, the method comprising the steps of:

acquiring multiple transaction data of the same transaction;

receiving a message application message;

acquiring a plurality of index data related to the credit ordering;

2. The method according to claim 1, wherein the integrating and assembling the multiple normal transaction data from the initiation of the same transaction from the service request to the completion of the service processing to obtain the full service chain monitoring information specifically comprises:

and if the state of the merged data is a first final state, determining that the merged data is the full service chain monitoring information.

3. The method according to claim 1 or 2, wherein the calculating of the credit consumption value of each participating line according to the index data related to the credit consumption sequence comprises:

4. The method according to claim 3, wherein the calculating the credit consumption value of each credit-participating line according to the index data related to the credit consumption sequence and a preset weight specifically comprises:

the credit value of each participating line is calculated according to the following formula:

the credit score of the ginseng row is (the ranking number of a first weight (1+ a) sorted by the ascending order of min (capital cost)) + the ranking number of a second weight (1+ b) sorted by the ascending order of min (response rate)) + the third weight (1+ c) sorted by the ascending order of max (cash placement success rate)) + the fourth weight (1+ d min (credit approval time))/4; the sum of the first weight, the second weight, the third weight and the fourth weight is 1;

wherein min (capital cost) represents the minimum capital cost, min (response rate) represents the minimum response rate, max (deposit success rate) represents the maximum deposit success rate, and min (time efficiency of approval by letter) represents the minimum time efficiency of approval by letter.

5. The method of claim 4, wherein a, b, c, d are between 0.1 and 0.5.

6. The method of claim 2, wherein the message queue is a remote dictionary service (redis).

7. The method according to claim 2, further comprising, before obtaining a corresponding credit application result according to the credit application information:

checking whether the credit application sent by the lead bank meets the transaction requirements; wherein, the inspection process includes: checking only whether fields of an incoming transaction request are ready, said fields comprising at least one of: the application number is awarded, the application number is used, the front-end-pulling party code, the contract number and the borrow number.

8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a data processing method for syndicated credits according to any one of claims 1 to 7.

9. A computer device, comprising:

one or more processors;

storage means for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the data processing method for syndicated credits according to any of claims 1 to 7.

10. A data processing system for syndicated credits, the system comprising:

the second server is used for receiving the information of the credit application; acquiring a corresponding credit application result according to the credit application information; acquiring a plurality of index data related to the credit ordering; calculating the credit utilization value of each credit participation line according to the index data related to the credit utilization sequence; sorting the ginseng credit lines in an ascending order according to the credit values of the ginseng credit lines; and sending the credit application transactions to the credit participating lines one by one according to the determined sequence after sorting.