CN112287237B - Transaction data analysis method and device for third-party transaction platform and terminal - Google Patents
Transaction data analysis method and device for third-party transaction platform and terminal Download PDFInfo
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- CN112287237B CN112287237B CN202011549909.4A CN202011549909A CN112287237B CN 112287237 B CN112287237 B CN 112287237B CN 202011549909 A CN202011549909 A CN 202011549909A CN 112287237 B CN112287237 B CN 112287237B
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Abstract
A transaction data analysis method, a device and a terminal for a third-party transaction platform are provided, wherein the method comprises the following steps: acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records; determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data; screening the repayment flow records belonging to repayment operation for the transaction flow records of each credit user under each credit merchant; and analyzing at least one transaction running record after the payment running record with the state as failure to determine whether the payment running record with the state as failure is overdue. Through the technical scheme of the invention, the third-party transaction platform can accurately judge whether the credit repayment of the cardholder is overdue or not by analyzing the transaction flow records of the cardholder and the credit merchant.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a transaction data analysis method, a transaction data analysis device and a transaction data analysis terminal for a third-party transaction platform.
Background
In recent years, with the gradual change of user consumption habits, more and more merchants open credit services, and the more common credit merchants (i.e., fund lenders) include internet consumption finance companies, small credit loan companies and the like. The current credit application is increasingly convenient and fast, the credit business volume is also continuously increased, and meanwhile, the business risk of overdue repayment of credit users (namely, fund borrowers) is also continuously increased.
Currently, most credit merchants and credit users do not typically conduct transactions directly, but rather complete transactions, settlements, etc. via third party transaction platforms such as unions. However, since the third-party transaction platform is not a credit merchant and cannot acquire specific credit information of the credit user, such as account balance, repayment date, repayment form, and the like, the third-party transaction platform can only see the consumption bill in the card of the credit user, that is, the third-party transaction platform can only see the transaction flow records of the credit user under the credit merchant. Therefore, the third party transaction platform cannot judge whether the credit user is overdue just by judging whether the credit user deposits the required repayment amount into the account before the preset repayment date like the credit merchant. Therefore, how the third-party transaction platform judges whether the overdue repayment of the credit user exists or not by using the transaction flow record is a problem to be solved.
Disclosure of Invention
The invention solves the technical problem of how a third-party transaction platform judges whether the credit repayment of a credit user is overdue or not.
In order to solve the above technical problem, an embodiment of the present invention provides a transaction data analysis method for a third party transaction platform, where the method includes: acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records; determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data; screening the repayment flow records belonging to repayment operation for the transaction flow records of each credit user under each credit merchant; and analyzing at least one transaction running record after the payment running record with the state as failure to determine whether the payment running record with the state as failure is overdue.
Optionally, the determining a credit merchant comprises: the method comprises the following steps: acquiring information of an initial credit merchant; step two: according to the information of the initial credit merchant, searching the associated transaction merchant of the initial credit merchant in the transaction flow data, wherein the associated transaction merchant refers to: for each credit user under the initial credit merchant, a merchant with whom at least one credit user has a transaction; step three: searching in the transaction flow data, finding out the merchants of which the merchant names contain preset keywords and/or the transaction flow records accord with preset transaction characteristics in the associated transaction merchants, taking the found merchants as newly added credit merchants, and adding the information of the newly added credit merchants to the information of the initial credit merchants; and repeating the second step and the third step until the newly added credit merchant does not exist, wherein the latest information of the initial credit merchant is the information of the credit merchant.
Optionally, the preset keywords include one or more of the following: credit, money, flowers, consumer finance, financing lease, trust; the preset transaction characteristics include one or more of: online transaction, single card average failure transaction proportion, single card average transaction verification times, median of transaction amount, maximum transaction amount, minimum transaction amount and total transaction amount.
Optionally, the screening out the payment running records belonging to the payment operation for the transaction running records of each credit user under each credit merchant comprises: acquiring an authentication type streamline mark and/or a credit card repayment mark; for the transaction flow record of each credit user under each credit merchant, searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark; and eliminating the transaction running records with the authentication running records and/or the credit card repayment marks, wherein at least one part of the rest transaction running records is the repayment running record.
Optionally, for the payment flow record with the status of failure, analyzing at least one transaction flow record after the payment flow record with the status of failure to determine whether the payment flow record with the status of failure is overdue includes: reading the deduction amount and the deduction time of the repayment flow record with the state of failure; reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure; searching for a transaction flow record with the same money amount as the deducted money amount and the same date as the deducted money time in the at least one transaction flow record, and recording as a same-day transaction flow record; if the same-day transaction flow record is found, reading the state of the last same-day transaction flow record; and if the last one-day transaction flow record is in a failure state or the one-day transaction flow record is not found, determining that the repayment flow record in the failure state is overdue.
Optionally, for the payment flow record with the status of failure, analyzing at least one transaction flow record after the payment flow record with the status of failure to determine whether the payment flow record with the status of failure is overdue includes: reading the deduction amount and the deduction time of the repayment flow record with the state of failure; reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure; searching for an exhibition flow record and/or a penalty flow record in the at least one transaction flow record; and if the extended period running record/or the fine interest running record is found, the repayment running record in the state of failure is overdue.
Optionally, the exhibition flow record refers to a transaction flow record in which the amount of money is the same as the deduction amount and the merchant name is the same as the credit merchant name of the repayment flow record in the state of failure in the at least one transaction flow record.
Optionally, the penalty information flow record refers to a transaction flow record in which the amount of gold in the at least one transaction flow record increases in the same proportion according to the date based on the deduction amount and the merchant name is the same as the credit merchant name of the repayment flow record in which the state is failed.
Optionally, searching for the extended period flow record in the at least one transaction flow record comprises: step one, setting an exhibition time window, wherein the exhibition time window starts from the next day of the money deduction time and has a first preset length; step two, searching the exhibition flow record in the exhibition time window; step three, if the exhibition flow record is found in the exhibition time window, reading the state of the last exhibition flow record in the exhibition time window, and if the state of the last exhibition flow record in the exhibition time window is failure, sliding the exhibition time window backwards by the first preset length; step four, repeatedly executing the step two to the step three until the exhibition flow record is not found in the exhibition time window, or the state of the last exhibition flow record in the exhibition time window is successful; step five, if the exhibition flow record is not found in the exhibition time window in step three, or the state of the last exhibition flow record in the exhibition time window is successful, finishing the search of the exhibition flow record.
Optionally, searching for a penalty flow record in the at least one transaction flow record comprises: step six, setting a penalty time window, wherein the penalty time window starts from the next day of the time of the found last exhibition running water record and has a second preset length, and if the exhibition running water record is not found, the penalty time window starts from the next day of the deduction time; step seven, searching the penalty running water record in the penalty time window; step eight, if the information-penalizing running water record is found in the information-penalizing time window, reading the state of the last information-penalizing running water record in the information-penalizing time window; if the state of the last penalty running water record in the penalty time window is failure, sliding the penalty time window backwards by the second preset length; step nine, repeatedly executing the step seven to the step eight until the penalty running water record is not found in the penalty time window, or the state of the last penalty running water record in the penalty time window is successful; step ten, if the information flow record is not found in the information flow time window in the step eight, or the state of the last information flow record in the information flow time window is successful, ending the search of the information flow record.
Optionally, the method further includes: if the exhibition flow record is found and the state of the found last exhibition flow record is successful, recording the deducted money amount as the overdue money amount of the repayment flow record with the state of failure; reading the time of the last record in the exhibition flow record; and calculating the difference value between the time of the last record in the extended period running record and the deduction time, wherein the difference value is the overdue days of the repayment running record with the state of failure.
Optionally, the method further includes: if the punishment running water record is found, reading the sum of the found last punishment running water record; recording the sum of the last record in the penalty running record as the overdue sum of the repayment running record with the state of failure; reading the time of the last record in the penalty running water records; and calculating the difference value between the time of the last record in the fine charging flow record and the deduction time, wherein the difference value is the overdue days of the repayment flow record with the state of failure.
The embodiment of the invention also provides a transaction data analysis device for a third-party transaction platform, which comprises: the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for acquiring transaction flow data which comprises a plurality of transaction flow records; the searching module is used for determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data; the screening module is used for screening the repayment flow records belonging to the repayment operation aiming at the transaction flow records of each credit user under each credit merchant; and the analysis module is used for analyzing at least one transaction flow record after the repayment flow record with the state as failure so as to determine whether the repayment flow record with the state as failure is overdue.
The embodiment of the invention also provides a storage medium, wherein a computer instruction is stored on the storage medium, and the computer instruction executes the steps of the transaction data analysis method for the third-party transaction platform when running.
The embodiment of the invention also provides a terminal, which comprises a memory and a processor, wherein the memory is stored with computer instructions capable of running on the processor, and the processor executes the steps of the transaction data analysis method for the third-party transaction platform when running the computer instructions.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a transaction data analysis method for a third-party transaction platform, which comprises the following steps: acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records; determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data; screening the repayment flow records belonging to repayment operation for the transaction flow records of each credit user under each credit merchant; and analyzing at least one transaction running record after the payment running record with the state as failure to determine whether the payment running record with the state as failure is overdue. Through the technical scheme provided by the embodiment of the invention, the third-party transaction platform can analyze transaction running records between a credit merchant and a credit user, screen out the repayment running records belonging to the repayment operation, and judge whether the credit repayment of the credit user is overdue according to at least one transaction running record after the repayment running record in the state of failure.
Furthermore, before the condition that whether the credit repayment of the credit user is overdue is judged, the credit merchant is determined by using the information of the initial credit merchant which is known in advance, so that the transaction running record of the credit user under the credit merchant can be searched in the transaction running data, and the transaction running record of the credit user under the non-credit merchant is prevented from being mistaken as the transaction running record of the credit user under the credit merchant.
Further, after the repayment running record in the state of failure is found, the deduction amount and the deduction time of the repayment running record in the state of failure, the amount and the time of at least one transaction running record after the repayment running record in the state of failure and a preset extension time and/or a fine inquiry period are used for searching the extension running record and/or the fine running record, so that whether the repayment running record in the state of failure is overdue or not can be judged.
Further, after determining that the repayment running record with the state of failure is overdue, the overdue amount and the overdue days of the repayment running record with the state of failure can be calculated by using the deducted amount, the deducted time and the amount and time of the extended running record and/or the fine running record.
Drawings
Fig. 1 is a flowchart illustrating a transaction data analysis method for a third party transaction platform according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a specific process of determining the credit merchant in step S102 in fig. 1.
Fig. 3 is a schematic specific flowchart of step S103 in fig. 1.
Fig. 4 is another specific flowchart of step S103 in fig. 1.
Fig. 5 is a schematic diagram of another specific flowchart of step S103 in fig. 1.
Fig. 6 is a schematic specific flowchart of step S104 in fig. 1.
Fig. 7 is another detailed flowchart of step S104 in fig. 1.
Fig. 8 is a specific flowchart of step S703 in fig. 7.
Fig. 9 is a schematic structural diagram of a transaction data analysis device for a third party transaction platform according to an embodiment of the present invention.
Detailed Description
As described in the background art, the third-party transaction platform cannot acquire specific credit information such as account balance and repayment date of the credit user, and only can see transaction flow records of transactions between the credit user and the credit merchant, that is, the third-party transaction platform cannot judge whether the credit user is overdue just by judging whether the required repayment amount is stored in the account before the predetermined repayment date like the credit merchant, and needs to judge according to the transaction flow records.
In order to solve the above technical problem, an embodiment of the present invention provides a transaction data analysis method for a third party transaction platform, where the method includes: acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records; determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data; screening the repayment flow records belonging to repayment operation for the transaction flow records of each credit user under each credit merchant; and analyzing at least one transaction running record after the payment running record with the state as failure to determine whether the payment running record with the state as failure is overdue. Through the technical scheme provided by the embodiment of the invention, the third-party transaction platform can analyze transaction running records between the credit merchant and the credit user, screen out the repayment running records belonging to the repayment operation, and judge whether the credit repayment of the credit user is overdue according to at least one transaction running record after the repayment running record in the state of failure.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 is a flowchart illustrating a transaction data analysis method for a third party transaction platform according to an embodiment of the present invention. The third party transaction platform can be a server for transaction and settlement, and terminal devices used by various merchants and users communicate with the third party transaction platform and perform various transactions through the third party transaction platform. Still further, the merchants may include credit merchants, which refer to merchants who provide credit services, such as internet consumption finance companies, small credit loan companies, etc., and non-credit merchants, which refer to merchants who do not provide credit services, such as restaurants, convenience stores, etc.; the users may include credit users who are users who borrow funds directed to a credit merchant and non-credit users who are users who do not borrow funds directed to a credit merchant.
A transaction data analysis method for a third party transaction platform as described in fig. 1 may include the following steps:
step S101, acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records.
In a specific implementation, the third-party transaction platform may obtain transaction flow data, where the transaction flow data includes transaction flow records between multiple merchants and multiple users. The transaction flow data can be stored in the third-party transaction platform, or can be stored outside the third-party transaction platform and acquired by the third-party transaction platform through a network access mode. The transaction flow record is information generated by transaction operations of the merchant and the user, and is usually, but not limited to, one transaction flow record per transaction. The third party transaction platform can obtain the specific content of each transaction flow record by reading each transaction flow record, wherein the specific content comprises one or more of information such as transaction amount, transaction time, merchant name, transaction state and the like.
Step S102, determining a credit merchant, and searching transaction flow records belonging to the credit merchant in the transaction flow data.
In particular, the merchants under the third-party transaction platform are usually many (for example, up to several tens of millions), and not all merchants are credit merchants, but only the transaction flow records of the credit merchants are overdue, so that in order to more accurately locate the transaction flow records under the credit merchants, the transaction flow data needs to be filtered to find the transaction flow records of the credit users under the credit merchants, that is, the transaction flow records generated by the transactions between the credit users and the credit merchants.
Referring to fig. 2, fig. 2 shows a specific flow of the step of determining the credit merchant in step S102, which may specifically include:
step S201: acquiring information of an initial credit merchant;
further, the initial credit merchant may be one or more credit merchants, and the information of the initial credit merchant may be preset or may be obtained from outside, for example, read from a known credit merchant database, and may include one or more of the information of the name, identification, account number, and the like of the initial credit merchant;
step S202: searching the associated transaction merchant of the initial credit merchant in the transaction running data according to the information of the initial credit merchant;
further, the associated transaction merchant refers to: for each credit user under the initial credit merchant, the merchants having transactions with at least one credit user, for example, for each initial credit merchant, the transaction flow records in the transaction flow data may be traversed, if information (e.g., name, identifier, account number, etc.) of the initial credit merchant is included in a certain transaction flow record, the information (e.g., name, identifier, account number, etc.) of the credit user involved in the transaction flow record is read, the transaction flow records in the transaction flow data are traversed again, the transaction flow record including the information of the credit user is found, and the other transaction party (except the initial credit merchant) in the found transaction flow record is used as the associated transaction merchant of the initial credit merchant;
step S203: and searching in the transaction flow data, finding out the merchants of which the merchant names contain preset keywords and/or the transaction flow records accord with preset transaction characteristics in the associated transaction merchants, using the found merchants as newly added credit merchants, and adding the information of the newly added credit merchants to the information of the initial credit merchants.
Further, the merchant name of each associated transaction merchant may be retrieved to find the merchant having the predetermined keyword in the merchant name, where the predetermined keyword may include one or more of the following: credit, money, flowers, consumer finance, financing lease, trust; and retrieving the transaction flow records of each associated transaction merchant to find the merchant with the transaction flow records meeting preset transaction characteristics, wherein the preset transaction characteristics may include one or more of the following items: the average failure transaction proportion of the single card of the online transaction, the average transaction verification times of the single card, the median of the transaction amount, the maximum transaction amount, the minimum transaction amount and the total transaction amount. For both conditions, merchants that satisfy one or both of them may add their information to the information of the initial credit merchant.
And repeating the step S202 and the step S203 until the new credit merchant does not exist, wherein the latest information of the initial credit merchant is the information of the credit merchant.
Thus, after obtaining information for all credit merchants, the third party transaction platform may find transaction flow records for the credit merchants from the transaction flow data based on the information (e.g., name, identification, account number, etc.) for the credit merchants. More specifically, for each credit merchant, the found transaction flow record for that credit merchant includes the transaction flow record between that credit merchant and the respective credit user. Next, for the transaction flow record of each credit merchant, the transaction flow record of each credit user is searched according to the difference of each credit user (such as the difference of name, identification, account number and the like).
Further, after no credit merchant is added, the information of all current credit merchants can be manually checked to improve the accuracy. Or after step S203 is executed each time, manually checking the newly-found credit merchant to ensure that the transaction flow records belonging to the credit merchant found in each step are as accurate as possible, and avoiding that the transaction flow records not belonging to the credit merchant are mistakenly used as the transaction flow records belonging to the credit merchant.
Still referring to fig. 1, after step S102, continuing to step S103, for each credit user' S transaction flow record under each of the credit merchants, a repayment flow record belonging to a repayment operation is screened out.
In particular implementations, the transaction flow record of the credit user under the credit merchant (i.e., the transaction flow record generated by the transaction of the credit merchant with the credit user) may include records unrelated to the repayment operation, such as non-bill consumption flow records, which refer to fees generated by actions of the credit user such as inquiring, applying for certification, etc., which fees are generated by the transaction of the credit user under the credit merchant but are not the repayment flow record generated by the repayment operation of the credit user. Therefore, for the transaction flow record of each credit user under each credit merchant, the third party transaction platform needs to screen out the repayment flow record belonging to the repayment operation.
Referring to fig. 3, fig. 3 shows a specific flow of step S103 in fig. 1, and the step of screening the payment running records belonging to the payment operation may include:
step S301: acquiring an authentication type streamline mark and/or a credit card repayment mark;
further, the authentication-type streamline mark and the credit card repayment mark can be preset and stored in the third-party transaction platform, or can be input by a management user of the third-party transaction platform;
step S302: for the transaction flow record of each credit user under each credit merchant, searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark;
for each credit merchant, retrieving the transaction flow records of each credit user having transactions with the credit merchant, and finding out the transaction flow records of each credit user having the authentication type flow marks and/or credit card repayment marks;
step S303: and eliminating the transaction running records with the authentication running records and/or the credit card repayment marks, wherein at least one part of the rest transaction running records is the repayment running record.
After the transaction running records with the authentication running marks and/or the credit card repayment marks are removed, all the remained repayment running records belonging to the repayment operation can be obtained, and one-step or multi-step screening can be carried out until the repayment running records are obtained.
In a specific implementation, the payment running record belonging to the payment operation may include a payment running record belonging to a staged payment operation, and the payment running record belonging to the staged payment operation refers to a payment running record generated by dividing into multiple returns without once clearing borrowed funds under a credit merchant when a credit user performs the payment operation. The credit merchant and the credit user can agree in advance on the form of periodic repayment, such as equal-amount principal, equal-amount principal interest, interest before interest after interest, and the like. The third party transaction platform can analyze the repayment flow records belonging to the repayment operation and further screen out the repayment flow records belonging to different forms of staged repayment operations.
Referring to fig. 4, fig. 4 shows another specific flow of step S103 in fig. 1, in the example shown in fig. 4, the third party platform screens payment flow records belonging to an equal amount interest form staged payment operation from a plurality of transaction flow records, and the steps in fig. 4 may include:
step S401: acquiring an authentication type streamline mark and/or a credit card repayment mark;
step S402: for the transaction flow record of each credit user under each credit merchant, searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark;
step S403: rejecting the transaction flow records with the authentication flow marks and/or the credit card repayment marks;
step S404: searching the transaction flow records with the same amount of money for the transaction flow records after the transaction flow records with the authentication type flow marks and/or the credit card repayment marks are removed;
step S405: reading the date of a first-term equal-amount original flow record, wherein the first-term equal-amount original flow record is the date of a first transaction flow record in the transaction flow records with the same amount, and searching the transaction flow records with the same amount or the dates within preset days (for example, 2 days) different from the dates of the first-term equal-amount original flow records;
step S406: reading an equal-amount instinct running water record, wherein the equal-amount instinct running water record is the transaction running water record and the first-period equal-amount instinct running water record which are searched in the step S405;
step S407: and if the sum of the number of the equal-amount instinct flow records is larger than a preset threshold (for example, 3), determining that the equal-amount instinct flow records are repayment flow records belonging to the equal-amount instinct form staged repayment operation.
Referring to fig. 5, fig. 5 shows another specific flow of step S103 in fig. 1, in the example shown in fig. 5, the third party platform may filter out payment flow records belonging to an installment payment operation in the form of an equal amount principal, and the steps in fig. 5 may include:
step S501: acquiring an authentication type streamline mark and/or a credit card repayment mark;
step S502: for the transaction flow record of each credit user under each credit merchant, searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark;
step S503: rejecting the transaction flow records with the authentication flow marks and/or the credit card repayment marks;
step S504: searching the transaction flow records with the same merchant name for the transaction flow records after the transaction flow records with the authentication type flow marks and/or the credit card repayment marks are removed;
step S505: reading the amount and date of a first-term equal-amount principal running record, wherein the first-term equal-amount principal running record is the date of a first transaction running record in the transaction running records with the same name, and searching the transaction running records with the same date as the first-term equal-amount principal running record or the date within a preset number of days (such as 2 days) from the date of the first-term equal-amount principal running record for the transaction running records with the same name;
step S506: for the transaction flow records found in step S505, finding transaction flow records whose amount is in a decreasing trend and is within a preset amount (e.g., 50 yuan) of the amount of the first-term equal-amount intrinsic information flow record;
step S507: reading the equivalent principal running record, wherein the equivalent principal running record is the transaction running record found in the step S506 and the first-stage equivalent interest running record;
step S508: and if the sum of the number of the equivalent principal fund flow records exceeds a preset threshold value (for example, 3), determining that the equivalent principal fund flow records are payment flow records belonging to the equivalent principal fund form staged payment operation.
In a specific implementation, the "payment running record pertaining to the payment operation" in step S103 may be the payment running record screened with reference to the step shown in fig. 3, may be the payment running record screened with reference to the steps shown in fig. 4 and/or fig. 5, and may also be the payment running record obtained according to another one-step or multi-step screening, for example, the payment running record pertaining to a non-installment payment operation, the payment running record pertaining to the payment operation after interest, and the like, which is not limited herein.
Still referring to fig. 1, after the step S103, continuing to execute the step S104, for the payment running record with the status of failure, analyzing at least one transaction running record after the payment running record with the status of failure to determine whether the payment running record with the status of failure is overdue.
Referring to fig. 6, fig. 6 shows a specific flow of step S104, and the step of step S104 may include:
step S601: reading the deduction amount and the deduction time of the repayment flow record with the state of failure;
step S602: reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure;
step S603: searching for a transaction flow record with the same money amount as the deducted money amount and the same date as the deducted money time in the at least one transaction flow record, and recording as a same-day transaction flow record;
step S604: if the same-day transaction flow record is found, reading the state of the last same-day transaction flow record;
step S605: and if the last one-day transaction flow record is in a failure state or the one-day transaction flow record is not found, determining that the repayment flow record in the failure state is overdue.
Referring to fig. 7, fig. 7 shows another specific flow of step S104, in which case the step of step S104 may include:
step S701: reading the deduction amount and the deduction time of the repayment running record with the state as failure, wherein the deduction amount is the amount of the repayment running record with the state as failure, and the deduction time is the time of the repayment running record with the state as failure;
step S702: reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure;
step S703: searching for an exhibition flow record and/or a penalty flow record in the at least one transaction flow record;
step S704: and if the extended period running record/or the fine interest running record is found, determining that the repayment running record with the state of failure is overdue.
Further, if the payment flow record with the state of failure is determined to be overdue, the third-party transaction platform can also determine the overdue amount and the overdue time according to the information of the payment flow record and/or the fine payment flow record.
Furthermore, if the exhibition flow record is found and the state of the found last exhibition flow record is successful, recording the deducted amount as the overdue amount of the repayment flow record with the state of failure; reading the time of the last exhibition flow record; and calculating the difference between the time of the last extended period running water record and the deduction time, and recording the difference as the overdue days of the repayment running water record with the state of failure.
If the punishment running water record is found, reading the sum of the found last punishment running water record; recording the sum of the last penalty running record as the overdue sum of the repayment running record with the state of failure; reading the time of the last penalty running water record; and calculating the difference between the time of the last fine interest flow record and the deduction time, and recording the difference as the overdue days of the payment flow record with the state as failure.
If neither the extended running water record nor the fine running water record is found in at least one transaction running water after the repayment running water record in the state of failure, the third-party transaction platform can perform various appropriate treatments, such as marking as 'abnormal' and ignoring the repayment running water record in the state of failure, and not judging whether the repayment running water record is overdue or not.
Referring to fig. 8, fig. 8 shows a specific flow of step S703, in which case the step of step S703 may include:
step one, setting an exhibition time window, wherein the exhibition time window is a search range for searching the exhibition flow record each time, starts from the next day of the deduction time and has a first preset length (the first preset length is a time length, and can be 14 days for example);
searching for the exhibition flow record in the exhibition time window;
step three, if the exhibition flow record is found in the exhibition time window, reading the state of the last exhibition flow record in the exhibition time window, and if the state of the last exhibition flow record in the exhibition time window is failure, sliding the exhibition time window backwards by the first preset length;
step four, repeatedly executing the step two to the step three until the exhibition flow record is not found in the exhibition time window, or the state of the last exhibition flow record in the exhibition time window is successful;
step five, if the exhibition flow record is not found in the exhibition time window in the step three, or the state of the last exhibition flow record in the exhibition time window is successful, finishing the search of the exhibition flow record;
step six, setting a penalty time window, wherein the penalty time window starts from the next day of the time of the found last exhibition running water record and has a second preset length (the second preset length is a time length, such as 7 days), and if the exhibition running water record is not found, the penalty time window starts from the next day of the deduction time;
step seven, searching the penalty running water record in the penalty time window;
step eight, if the information-penalizing running water record is found in the information-penalizing time window, reading the state of the last information-penalizing running water record in the information-penalizing time window; if the state of the last penalty running water record in the penalty time window is failure, sliding the penalty time window backwards by the second preset length;
step nine, repeatedly executing the step seven to the step eight until the penalty running water record is not found in the penalty time window, or the state of the last penalty running water record in the penalty time window is successful;
step ten, if the information flow record is not found in the information flow time window in the step eight, or the state of the last information flow record in the information flow time window is successful, ending the search of the information flow record.
In one non-limiting example, the third party transaction platform finds that a credit user (card number X) has a recurring repayment record with a status of failed under the credit merchant "a-spending finance company" with a time of 2020, 1 month and 1 day, and an amount of 5000 yuan.
Executing the step one: an exhibition time window is set to be 14 days, and the exhibition time window is started from the next day of the deduction time, namely, from 1 month and 2 days of 2020.
And (5) executing the step two: searching for the trade running record in the exhibition time window, namely searching for the trade running record in 14 days from the trade running record with the time of 2020, 1 month and 2 days;
the transaction flow record in table 1 is found:
table 1:
| card number | Credit merchant | Time | Amount of money | Status of transaction | |
| X | Consumption finance company A | Year 2020, 1 month 2 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 3 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 4 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 5 days | 5000 | Failure of | |
| X | A Xiao (medicine for treating A)Fee finance company | Year 2020, 1 month and 6 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 7 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 8 days | 5000 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 9 days | 5008 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 10 days | 5009 | Failure of | |
| X | Consumption finance company A | Year 2020, 1 month and 11 days | 5010 | Successful | |
| X | Consumption finance company A | Year 2020, 2 and 1 | 3000 | Successful | |
| X | Consumption finance | Year | 2020, 2 and 11 | 4000 | Successful |
| X | Consumption finance company A | 3 months and 3 days in 2020 | 2000 | Successful | |
| X | B consumption finance company | Year 2020, 4 and 3 | 1000 | Failure of |
Continuing to execute the step two: the trade journal record from 2 days 1 month in 2020 to 8 days 1 month in 2020 can be found as the exhibition journal record;
after the exhibition period time window is slid backwards for 14 days, no exhibition period running water record is found in the exhibition period time window, and the fifth step is executed: finishing the searching of the exhibition period flow record;
and continuously executing the step six: setting a penalty time window of 7 days starting the next day of the time of the last exhibition journal found, i.e., the trade journal of 1 month and 9 days of 2020 in table 1;
step seven is executed, the penalty running water records are searched in the penalty time window of 7 days, the trade running water records from 1 month and 9 days in 2020 to 1 month and 11 days in 2020 are found to be the penalty running water records,
and after the penalty time window is slid backwards for 7 days, finding no penalty running water record in the penalty time window, executing a step ten, and finishing the finding of the penalty running water record.
Due to the fact that the extended and punishment running water records are found, the repayment running water record of the credit user in 1 month and 1 day of 2020 is overdue, according to the last punishment running water record, the overdue amount is 5010 yuan, and the overdue days are 10 days.
Fig. 9 is a schematic structural diagram of a transaction data analysis device for a third party transaction platform according to an embodiment of the present invention. The transaction data analysis device for the third party transaction platform in this embodiment may include a receiving module 11, a searching module 21, a screening module 31, and an analyzing module 41, where:
the receiving module 11 is configured to obtain transaction flow data, where the transaction flow data includes a plurality of transaction flow records.
The search module 21 is configured to determine a credit merchant and search the transaction flow data for a transaction flow record belonging to the credit merchant.
The screening module 31 is used for screening the repayment flow records belonging to the repayment operation aiming at the transaction flow records of each credit user under each credit merchant.
The analysis module 41 is configured to, for a payment flow record with a status of failure, analyze at least one transaction flow record after the payment flow record with the status of failure to determine whether the payment flow record with the status of failure is overdue.
In a specific implementation, the search module 21 may include: an acquiring subunit (not shown), a querying subunit (not shown), and a matching subunit (not shown), wherein:
the acquisition subunit is used for acquiring information of the initial credit merchant;
the inquiry subunit is used for searching the associated transaction merchant of the credit merchant in the transaction pipelining data according to the information of the initial credit merchant;
the matching subunit is used for searching in the transaction flow data, finding out the commercial tenant containing preset keywords and/or the transaction flow record in the commercial tenant name in the associated transaction commercial tenant and meeting preset transaction characteristics, taking the found commercial tenant as a new commercial tenant, and adding the information of the new commercial tenant into the information of the initial credit commercial tenant.
Those skilled in the art will understand that the transaction data analysis device for the third party transaction platform in the present embodiment may be used to implement the transaction data analysis method for the third party transaction platform in the embodiments shown in fig. 1 to 8.
For more details on the working principle and working mode of the transaction data analysis device for the third party transaction platform, reference may be made to the related descriptions in fig. 1 to fig. 8, and details are not repeated here.
Further, the embodiment of the present invention also discloses a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method technical solution in the embodiments shown in fig. 1 to 8 is executed. Preferably, the storage medium may include a computer-readable storage medium such as a non-volatile (non-volatile) memory or a non-transitory (non-transient) memory. The storage medium may include, but is not limited to, ROM, RAM, magnetic or optical disks, and the like.
Further, an embodiment of the present invention further discloses a terminal, including a memory and a processor, where the memory stores a computer program capable of running on the processor, and the processor executes the method technical solutions in the embodiments shown in fig. 1 to 8 when running the computer program, where the terminal may include, but is not limited to, a mobile phone, a computer, a tablet computer, and other terminal devices.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (14)
1. A transaction data analysis method for a third party transaction platform is characterized by comprising the following steps:
acquiring transaction flow data, wherein the transaction flow data comprises a plurality of transaction flow records;
determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data;
screening the repayment flow records belonging to repayment operation for the transaction flow records of each credit user under each credit merchant;
analyzing at least one transaction running record after the repayment running record with the state as failure for the repayment running record with the state as failure to determine whether the repayment running record with the state as failure is overdue;
wherein, for the transaction flow record of each credit user under each credit merchant, screening the repayment flow record belonging to the repayment operation comprises:
acquiring an authentication type streamline mark and/or a credit card repayment mark;
for the transaction flow record of each credit user under each credit merchant, searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark;
and eliminating the transaction running records with the authentication running records and/or the credit card repayment marks, wherein at least one part of the rest transaction running records is the repayment running record.
2. The transaction data analysis method for a third party transaction platform of claim 1, wherein the determining a credit merchant comprises:
the method comprises the following steps: acquiring information of an initial credit merchant;
step two: according to the information of the initial credit merchant, searching the associated transaction merchant of the initial credit merchant in the transaction flow data, wherein the associated transaction merchant refers to: for each credit user under the initial credit merchant, a merchant with whom at least one credit user has a transaction;
step three: searching in the transaction flow data, finding out the merchants of which the merchant names contain preset keywords and/or the transaction flow records accord with preset transaction characteristics in the associated transaction merchants, taking the found merchants as newly added credit merchants, and adding the information of the newly added credit merchants to the information of the initial credit merchants;
and repeating the second step and the third step until the newly added credit merchant does not exist, wherein the latest information of the initial credit merchant is the information of the credit merchant.
3. The transaction data analysis method for the third party transaction platform according to claim 2, wherein the preset keywords comprise one or more of the following items: credit, money, flowers, consumer finance, financing lease, trust;
the preset transaction characteristics include one or more of: online transaction, single card average failure transaction proportion, single card average transaction verification times, median of transaction amount, maximum transaction amount, minimum transaction amount and total transaction amount.
4. The transaction data analysis method for a third party transaction platform of claim 1, wherein analyzing at least one transaction flow record after the payment flow record with the status of failure to determine whether the payment flow record with the status of failure is overdue comprises:
reading the deduction amount and the deduction time of the repayment flow record with the state of failure;
reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure;
searching for a transaction flow record with the same money amount as the deducted money amount and the same date as the deducted money time in the at least one transaction flow record, and recording as a same-day transaction flow record;
if the same-day transaction flow record is found, reading the state of the last same-day transaction flow record;
and if the last one-day transaction flow record is in a failure state or the one-day transaction flow record is not found, determining that the repayment flow record in the failure state is overdue.
5. The transaction data analysis method for a third party transaction platform of claim 1, wherein analyzing at least one transaction flow record after the payment flow record with the status of failure to determine whether the payment flow record with the status of failure is overdue comprises:
reading the deduction amount and the deduction time of the repayment flow record with the state of failure;
reading the amount and time of at least one transaction flow record after the repayment flow record with the state of failure;
searching for an exhibition flow record and/or a penalty flow record in the at least one transaction flow record;
and if the extended period running record and/or the fine interest running record are found, the repayment running record with the state of failure is overdue.
6. The transaction data analysis method for a third party transaction platform of claim 5, wherein the extended period journal record is a transaction journal record of the at least one transaction journal record having an amount that is the same as the deducted amount and a merchant name that is the same as a credit merchant name of the repayment journal record in which the status is failed.
7. The transaction data analysis method for a third party transaction platform of claim 5, wherein the penalty information flow record is a transaction flow record in which the amount of money in the at least one transaction flow record increases by the same proportion on the date based on the deducted amount and the merchant name is the same as the credit merchant name of the repayment flow record whose status is failed.
8. The transaction data analysis method for a third party transaction platform of claim 5, wherein searching for an exhibition flow record in the at least one transaction flow record comprises:
step one, setting an exhibition time window, wherein the exhibition time window starts from the next day of the money deduction time and has a first preset length;
step two, searching the exhibition flow record in the exhibition time window;
step three, if the exhibition flow record is found in the exhibition time window, reading the state of the last exhibition flow record in the exhibition time window, and if the state of the last exhibition flow record in the exhibition time window is failure, sliding the exhibition time window backwards by the first preset length;
step four, repeatedly executing the step two to the step three until the exhibition flow record is not found in the exhibition time window, or the state of the last exhibition flow record in the exhibition time window is successful;
step five, if the exhibition flow record is not found in the exhibition time window in step three, or the state of the last exhibition flow record in the exhibition time window is successful, finishing the search of the exhibition flow record.
9. The method of claim 8, wherein searching for a penalty flow record in the at least one transaction flow record comprises:
step six, setting a penalty time window, wherein the penalty time window starts from the next day of the time of the found last exhibition running water record and has a second preset length, and if the exhibition running water record is not found, the penalty time window starts from the next day of the deduction time;
step seven, searching the penalty running water record in the penalty time window;
step eight, if the information-penalizing running water record is found in the information-penalizing time window, reading the state of the last information-penalizing running water record in the information-penalizing time window; if the state of the last penalty running water record in the penalty time window is failure, sliding the penalty time window backwards by the second preset length;
step nine, repeatedly executing the step seven to the step eight until the penalty running water record is not found in the penalty time window, or the state of the last penalty running water record in the penalty time window is successful;
step ten, if the information flow record is not found in the information flow time window in the step eight, or the state of the last information flow record in the information flow time window is successful, ending the search of the information flow record.
10. The transaction data analysis method for a third party transaction platform of claim 5, further comprising:
if the exhibition flow record is found and the state of the found last exhibition flow record is successful, recording the deducted money amount as the overdue money amount of the repayment flow record with the state of failure;
reading the time of the last exhibition flow record;
and calculating the difference between the time of the last extended period running water record and the deduction time, and recording the difference as the overdue days of the repayment running water record with the state of failure.
11. The transaction data analysis method for a third party transaction platform of claim 5, further comprising:
if the punishment running water record is found, reading the sum of the found last punishment running water record;
recording the sum of the last penalty running record as the overdue sum of the repayment running record with the state of failure;
reading the time of the last penalty running water record;
and calculating the difference between the time of the last fine interest flow record and the deduction time, and recording the difference as the overdue days of the payment flow record with the state as failure.
12. A transaction data analysis device for a third party transaction platform, comprising:
the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for acquiring transaction flow data which comprises a plurality of transaction flow records;
the searching module is used for determining a credit merchant and searching transaction flow records belonging to the credit merchant in the transaction flow data;
the screening module is used for screening the repayment flow records belonging to the repayment operation aiming at the transaction flow records of each credit user under each credit merchant;
the analysis module is used for analyzing at least one transaction flow record after the repayment flow record with the state as failure to determine whether the repayment flow record with the state as failure is overdue or not;
wherein the screening module comprises:
the acquisition submodule is used for acquiring the authentication type streamline mark and/or the credit card repayment mark;
the searching sub-module is used for searching the transaction flow record with the authentication type flow mark and/or the credit card repayment mark for the transaction flow record of each credit user under each credit merchant;
and the removing submodule is used for removing the transaction running record with the authentication running mark and/or the credit card repayment mark, and at least one part of the rest transaction running records is the repayment running record.
13. A storage medium having stored thereon computer instructions, wherein the computer instructions are operable to perform the steps of the method for transaction data analysis for a third party transaction platform according to any of claims 1 to 11.
14. A terminal comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, wherein the processor, when executing the computer instructions, performs the steps of the method of transaction data analysis for a third party transaction platform according to any of claims 1 to 11.
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| US20060206424A1 (en) * | 2005-03-10 | 2006-09-14 | Ken Algiene | Systems and methods for rewarding debit transactions |
| CN111489254A (en) * | 2020-04-14 | 2020-08-04 | 上海数喆数据科技有限公司 | Credit risk assessment intelligent engine system based on historical credit big data |
| CN111754322A (en) * | 2020-06-17 | 2020-10-09 | 广州宇中网络科技有限公司 | Multi-party oriented financial fund settlement algorithm information management method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060206424A1 (en) * | 2005-03-10 | 2006-09-14 | Ken Algiene | Systems and methods for rewarding debit transactions |
| CN111489254A (en) * | 2020-04-14 | 2020-08-04 | 上海数喆数据科技有限公司 | Credit risk assessment intelligent engine system based on historical credit big data |
| CN111754322A (en) * | 2020-06-17 | 2020-10-09 | 广州宇中网络科技有限公司 | Multi-party oriented financial fund settlement algorithm information management method and system |
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