Bank-enterprise account checking dynamic period management method and device

Abstract

The invention discloses a bank and enterprise account checking dynamic period management method and device. The method comprises the steps of obtaining stream data of a bank account; determining complex events matched with a plurality of predefined mode rules from the streaming data according to the detail information of the account activation events; determining an account checking weight of the streaming data based on the plurality of complex events; correcting the reconciliation weight by using the dynamically updated reconciliation period to obtain a corrected reconciliation weight; according to a preset reconciliation period grade table, determining a target reconciliation period corresponding to the corrected reconciliation weight; and based on the target reconciliation period, generating business information for helping the salesman to perform bank-enterprise reconciliation. According to the method, the flow data is quickly and efficiently processed by utilizing accounting multi-dimensional elements (namely a plurality of mode rules), and the reconciliation weight of the flow data is analyzed through multiple dimensions (namely a plurality of complex events) so as to map business information of a personalized cycle, improve customer experience, reduce fund risks and effectively improve the reconciliation operation efficiency of bank salesmen.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and device for dynamic cycle management of bank-enterprise reconciliation. Background Art

[0002] In the era of financial technology, as the Internet continues to develop, applying Internet technology to the financial field can not only promote better development, but also enhance the development of the financial industry, improve risk management capabilities, and effectively prevent financial crimes.

[0003] Bank-enterprise reconciliation refers to the process in which banks provide corporate customers with statements and require customers to complete balance verification and feedback the results in accordance with industry regulatory requirements. The bank-enterprise reconciliation cycle refers to the additional service provided by the bank to customers, and the bank issues statements to customers at a fixed period according to customer needs. This customer-led reconciliation cycle is subjective, and risks often appear in this subjectivity. Customers want to issue balance statements on a semi-annual basis. Assuming that customers frequently move their accounts in a certain month and the bank still issues reconciliation statements on a semi-annual basis, it is risky for the company. As a monitoring method for customer fund protection and risk identification, banks need an objective reconciliation cycle to meet industry supervision while providing customers with a more convenient and secure reconciliation service. For customers with frequent capital flow, risks can be identified more accurately, and for customers with small capital and low flow, the cycle can be expanded to prevent frequent account closures due to unreconciled accounts, which affects customer experience. For bank reconciliation clerks, they can manage the reconciliation business process more efficiently and finely, reduce the accumulation of large batches of bills, and continuously improve the efficiency of reconciliation operations.

[0004] The traditional bank-enterprise reconciliation cycle is issued at a fixed period, which cannot prevent reconciliation risks in a more refined manner. It is issued according to the period selected by the customer and does not have a certain regularity. It takes a lot of time for bank employees to run the reconciliation workflow. Bank employees have no way to judge customer habits. The static reconciliation cycle will most likely cause bank employees to encounter a scenario where they have to deal with a large number of reconciliation statements. Summary of the invention

[0005] The present application provides a method and device for dynamic cycle management of bank-enterprise reconciliation, aiming to improve the efficiency of bank clerks' reconciliation operations.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A bank-enterprise reconciliation dynamic cycle management method, comprising:

[0008] Obtaining bank account stream data; the stream data includes account movement events occurring at any time and corresponding detailed information;

[0009] According to the detailed information of the account movement event, determining a complex event matching a plurality of predefined pattern rules from the stream data;

[0010] Determining a reconciliation weight of the stream data based on multiple complex events;

[0011] The reconciliation weight is modified using the dynamically updated reconciliation cycle to obtain a modified reconciliation weight; the reconciliation cycle is dynamically updated based on the current status of the bank account;

[0012] Determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle rating table; the reconciliation cycle rating table includes a plurality of sample reconciliation weights and corresponding recommended reconciliation cycles;

[0013] Based on the target reconciliation cycle, business information is generated to help sales staff conduct bank-enterprise reconciliation; the business information includes the collection plan and account closure date of the bank account.

[0014] Optionally, the detailed information includes at least the transaction amount and the account balance;

[0015] According to the detailed information of the account movement event, a complex event matching a plurality of predefined pattern rules is determined from the stream data, including:

[0016] Acquire a plurality of predefined pattern rules; the plurality of pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule;

[0017] Based on multiple consecutive account movement events in the stream data within a specified time period, determining a first complex event that matches the first rule;

[0018] Determine, based on the account movement events in the stream data that occur multiple times continuously within the specified time and whose transaction amounts meet the specified threshold, as second complex events that match the second rule;

[0019] Based on the account movement event in which the transaction amount exceeds the specified amount in the stream data, determining it as a third complex event matching the third rule;

[0020] Based on the account movement event in which the account balance in the stream data meets the preset limit, determining it as a fourth complex event matching the fourth rule;

[0021] Based on the account movement event whose balance floating value in the stream data meets the preset requirements, it is determined to be the fifth complex event matching the fifth rule; the balance floating value is the change value of the account balance within the specified time.

[0022] Optionally, based on multiple complex events, determining the reconciliation weight of the stream data includes:

[0023] Based on the number of occurrences of the first complex event, determining a corresponding first eigenvalue;

[0024] Based on the transaction amount of the second complex event, determining a corresponding second characteristic value;

[0025] Based on the transaction amount of the third complex event, determining a corresponding third characteristic value;

[0026] Based on the account balance of the fourth complex event, determining a corresponding fourth eigenvalue;

[0027] Based on the balance floating value of the fifth complex event, determining a corresponding fifth characteristic value;

[0028] A reconciliation weight of the stream data is determined based on the first feature value, the second feature value, the third feature value, the fourth feature value, and the fifth feature value.

[0029] Optionally, the process of dynamically updating the reconciliation cycle based on the current status of the bank account includes:

[0030] When the bank account reaches a specified payment date, obtaining the current status of the bank account and a preset reconciliation cycle;

[0031] If the current status indicates that the bank account has bad behavior, the preset reconciliation cycle is adjusted according to a specified frequency to obtain a dynamically updated reconciliation cycle; the dynamically updated reconciliation cycle is less than the preset reconciliation cycle;

[0032] If the current status indicates that there is no bad behavior in the bank account, based on the specified period, it is determined as a dynamically updated reconciliation period.

[0033] Optionally, using the dynamically updated reconciliation cycle, the reconciliation weight is modified to obtain a modified reconciliation weight, including:

[0034] Obtaining a preset reconciliation cycle for the bank account;

[0035] Determine the target period value based on the maximum value between the dynamically updated reconciliation period and the preset reconciliation period;

[0036] Based on the target period value, determining a corresponding target reconciliation weight;

[0037] A maximum value between the target reconciliation weight and the reconciliation weight of the stream data is determined as a modified reconciliation weight.

[0038] A bank-enterprise reconciliation dynamic cycle management device, comprising:

[0039] A data acquisition unit, used to acquire stream data of a bank account; the stream data includes account movement events occurring at any time and corresponding detailed information;

[0040] A rule matching unit, configured to determine, from the stream data, complex events that match a plurality of predefined pattern rules according to the detailed information of the account movement event;

[0041] A weight determination unit, configured to determine a reconciliation weight of the stream data based on a plurality of complex events;

[0042] A weight correction unit, configured to correct the reconciliation weight using the dynamically updated reconciliation cycle to obtain a corrected reconciliation weight; the reconciliation cycle is dynamically updated based on the current state of the bank account;

[0043] A cycle determination unit, configured to determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle rating table; the reconciliation cycle rating table includes a plurality of sample reconciliation weights and corresponding recommended reconciliation cycles;

[0044] An information generating unit is used to generate business information for helping salespersons to conduct bank-enterprise reconciliation based on the target reconciliation cycle; the business information includes the collection plan and account closure date of the bank account.

[0045] Optionally, the detailed information includes at least a transaction amount and an account balance; and the rule matching unit is specifically used to:

[0046] Acquire a plurality of predefined pattern rules; the plurality of pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule;

[0047] Based on multiple consecutive account movement events in the stream data within a specified time period, determining a first complex event that matches the first rule;

[0048] Determine, based on the account movement events in the stream data that occur multiple times continuously within the specified time and whose transaction amounts meet the specified threshold, as second complex events that match the second rule;

[0049] Based on the account movement event in which the transaction amount exceeds the specified amount in the stream data, determining it as a third complex event matching the third rule;

[0050] Based on the account movement event in which the account balance in the stream data meets the preset limit, determining it as a fourth complex event matching the fourth rule;

[0051] Based on the account movement event whose balance floating value in the stream data meets the preset requirements, it is determined to be the fifth complex event matching the fifth rule; the balance floating value is the change value of the account balance within the specified time.

[0052] Optionally, the rule matching unit is specifically used for:

[0053] Based on the number of occurrences of the first complex event, determining a corresponding first eigenvalue;

[0054] Based on the transaction amount of the second complex event, determining a corresponding second characteristic value;

[0055] Based on the transaction amount of the third complex event, determining a corresponding third characteristic value;

[0056] Based on the account balance of the fourth complex event, determining a corresponding fourth eigenvalue;

[0057] Based on the balance floating value of the fifth complex event, determining a corresponding fifth characteristic value;

[0058] A reconciliation weight of the stream data is determined based on the first feature value, the second feature value, the third feature value, the fourth feature value, and the fifth feature value.

[0059] A storage medium includes a stored program, wherein the program is executed by a processor to execute the bank-enterprise reconciliation dynamic cycle management method.

[0060] An electronic device, comprising: a processor, a memory and a bus; the processor and the memory are connected via the bus;

[0061] The memory is used to store programs, and the processor is used to run programs, wherein the program executes the bank-enterprise reconciliation dynamic cycle management method when the processor runs it.

[0062] The technical solution provided by the present application obtains the stream data of a bank account. According to the detailed information of the account dynamic event, a complex event that matches a plurality of predefined pattern rules is determined from the stream data. Based on the plurality of complex events, the reconciliation weight of the stream data is determined. The reconciliation weight is corrected using the dynamically updated reconciliation cycle to obtain the corrected reconciliation weight. According to the preset reconciliation cycle grade table, the target reconciliation cycle corresponding to the corrected reconciliation weight is determined. Based on the target reconciliation cycle, business information is generated to help the salesperson conduct bank-enterprise reconciliation. The present application utilizes multi-dimensional elements of accounts (i.e., a plurality of pattern rules) to quickly and efficiently process stream data, and analyzes the reconciliation weight of the stream data through multiple dimensions (i.e., a plurality of complex events) to map the business information of personalized cycles, improve customer experience, and reduce financial risks, thereby effectively improving the reconciliation efficiency of bank salespersons. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flow chart of a bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application;

[0065] Figure 2 A flowchart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application;

[0066] Figure 3 A flowchart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application;

[0067] Figure 4 A flowchart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application;

[0068] Figure 5 A flowchart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application;

[0069] Figure 6 A schematic diagram of the architecture of a bank-enterprise reconciliation dynamic cycle management device provided in an embodiment of the present application;

[0070] Figure 7 A schematic diagram of the architecture of an accounting system provided in an embodiment of the present application;

[0071] Figure 8 A schematic diagram of the architecture of a stream data processing unit provided in an embodiment of the present application;

[0072] Fig. 9 A schematic diagram of the execution effect of a result output unit provided in an embodiment of the present application;

[0073] Fig.10 A schematic diagram of balance changes provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0076] like Figure 1 As shown, it is a flow chart of a dynamic cycle management method for bank-enterprise reconciliation provided in an embodiment of the present application, including the following steps.

[0077] S101: Obtaining stream data of a bank account.

[0078] The flow data includes account events that occur at any time and the corresponding detailed information.

[0079] The so-called account change event usually refers to the fund change event in the bank account, including but not limited to deposit, withdrawal, transfer, consumption and other operations. The detailed information of the account change event includes but is not limited to the transaction amount, account balance and so on.

[0080] In some examples, real-time data such as account balance, details, and basic account information of a bank account can be obtained through a preset message queue, and the obtained real-time data can be cleaned to obtain streaming data of the bank account.

[0081] In a possible implementation manner, product data that does not participate in the reconciliation operation in the real-time data may be eliminated according to the specified reconciliation scope.

[0082] In a possible implementation, the amount shown in the real-time data may be converted into an amount in a specified currency.

[0083] In a possible implementation, the amount data in the real-time data that does not meet the customer's actual transaction requirements can be eliminated based on the account balance.

[0084] S102: According to the detailed information of the account activity event, a complex event matching a plurality of predefined pattern rules is determined from the stream data.

[0085] The detailed information of the account movement event includes at least the transaction amount and the account balance. According to the transaction amount and the account balance of the account movement event, a complex event matching multiple pre-defined pattern rules can be determined from the stream data.

[0086] It should be noted that the pattern rule can be understood as a matching rule defined based on the characteristics of the account movement event and the combination relationship between the account movement events. In some examples, the pattern rule can be set by a technician according to actual conditions.

[0087] Optionally, based on the detailed information of the account event, the implementation process of determining complex events that match multiple pre-defined pattern rules from the stream data can be seen in Figure 2 The steps are shown and the corresponding explanations.

[0088] S103: Determine the reconciliation weight of the stream data based on multiple complex events.

[0089] The multiple complex events include but are not limited to a first complex event, a second complex event, a third complex event, a fourth complex event and a fifth complex event.

[0090] Optionally, based on multiple complex events, the implementation process of determining the reconciliation weight of streaming data can be found in Figure 3 The steps are shown and the corresponding explanations.

[0091] S104: using the dynamically updated reconciliation cycle, modifying the reconciliation weight to obtain a modified reconciliation weight.

[0092] Among them, the reconciliation cycle is dynamically updated based on the current status of the bank account.

[0093] Optionally, the process of dynamically updating the reconciliation period based on the current status of the bank account can be found in Figure 4 The steps are shown and the corresponding explanations.

[0094] Optionally, the reconciliation weights can be modified using the dynamically updated reconciliation cycle to obtain the implementation process of modifying the reconciliation weights. For details, see Figure 5 The steps are shown and the corresponding explanations.

[0095] S105: Determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle level table.

[0096] Among them, the reconciliation cycle rating table includes multiple sample reconciliation weights and corresponding recommended reconciliation cycles.

[0097] In some examples, the reconciliation cycle level table can be seen in Table 1.

[0098] Table 1

[0099]

[0100]

[0101] The contents shown in Table 1 above are only for illustration. Specifically, after obtaining the modified reconciliation weight, the weight range corresponding to the modified reconciliation weight is determined to obtain the corresponding weight level and the recommended reconciliation cycle. The weight range and weight level can realize the division of sample reconciliation weights.

[0102] S106: Based on the target reconciliation cycle, business information is generated to help the salesperson perform bank-enterprise reconciliation.

[0103] Among them, business information includes the bank account collection plan and account closure date.

[0104] It should be noted that after obtaining the target reconciliation cycle, the bank reconciliation staff can monitor and classify the accounts in real time according to the dynamic reconciliation cycle of each account, estimate the monthly reconciliation data volume and formulate a collection plan, evaluate the frequency of issuing bills for the reconciliation account and customize the account closure date. This effectively reduces the workload of bank staff and improves the collection efforts for enterprises.

[0105] In some examples, combined Figure 2-Figure 5 The method shown in the present application can be applied to Figure 7 In the accounting system shown, the accounting system includes a data cleaning unit, a stream data processing unit, a parameter tuning unit and a result output unit.

[0106] In a possible implementation, the data cleaning unit is used to obtain data such as account balance, details, basic account information, etc. through the message queue, and perform data cleaning to obtain the flow data of the bank account. The data cleaning unit includes a data acquisition module and a data cleaning module. The data acquisition module is the input of the entire device. Through the message queue, the account balance, details, product type, and account type data of the accounting system are obtained. The time series is set and input into the data cleaning module. The data cleaning module performs preliminary screening of valid data on the data transmitted from the message queue.

[0107] In a possible implementation, the stream data processing unit consists of three parts: pattern rule definition, stream data processing, and feature splicing processing. The specific structure can be found in Figure 8 shown.

[0108] In a possible implementation, the parameter tuning unit includes a boundary analysis module and a result tuning module. The boundary analysis module is used to perform special data boundary scenario processing on the output result of the stream data processing unit (i.e., the reconciliation weight of the stream data). The result tuning module is used to correct and tune the reconciliation weight in combination with the dynamically updated reconciliation cycle.

[0109] In a possible implementation, the result output unit includes an analysis result output module and a reconciliation cycle plan formulation module. The analysis result output module is used to organize and output the output results of the parameter tuning unit (i.e., the modified reconciliation weights). The reconciliation cycle plan formulation module is used to summarize the results of the analysis result output module to obtain business information. This process can be seen in Fig. 9 shown.

[0110] In general, the embodiments of the present application may have the following advantages: First, through a complex event processing library based on stream computing, combined with precise analysis of massive data, the reconciliation cycle can be calculated efficiently, accurately, dynamically and in real time; Second, the reconciliation statements for all accounts can be issued in a staggered manner to a certain extent, which greatly reduces the periodic workload of bank employees and improves the efficiency of single operations; Third, compared with traditional bank-enterprise reconciliation, there are two advantages for customers: First, for customers with large amounts of funds and frequent turnover, reconciliation statements can be issued at a higher frequency, and reconciliation risks can be more effectively prevented; second, for customers with small amounts of funds and infrequent turnover, reconciliation statements are issued with a less frequent reconciliation cycle to prevent customers from developing resistance due to frequent reconciliations and affecting the operation of bank-enterprise cooperation.

[0111] The process shown in S101-S106 above uses multi-dimensional elements of accounts (i.e., multiple pattern rules) to quickly and efficiently process streaming data, and analyzes the reconciliation weight of streaming data through multiple dimensions (i.e., multiple complex events) to map business information of personalized cycles, improve customer experience, reduce financial risks, and effectively improve the reconciliation efficiency of bank employees.

[0112] like Figure 2 As shown, it is a flow chart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application, including the following steps.

[0113] S201: Acquire a plurality of predefined pattern rules.

[0114] The plurality of pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule.

[0115] S202: Determine, based on the account movement events that occur multiple times in succession within a specified time in the stream data, a first complex event that matches the first rule.

[0116] The first rule may be understood as multiple account movement events that occur consecutively in a short period of time. Therefore, the first complex event in the stream data may be determined by monitoring the number of account movement events that occur within the time period t.

[0117] In some examples, the expression form of the first rule can be shown in Table 2.

[0118] Table 2

[0119]

[0120] The contents shown in Table 2 above are only for illustration, and n1 and n2 are preset constants.

[0121] S203: Determine, based on the account movement events in the stream data that occur multiple times in succession within a specified time and whose transaction amounts meet a specified threshold, as second complex events that match the second rule.

[0122] Among them, the second rule can be understood as large-amount account movement events (i.e., account movement events whose transaction amounts meet the specified threshold) that occur multiple times in a short period of time. To this end, the second complex event in the stream data can be determined by monitoring the number of occurrences of specified large-amount account movement events within different amount ranges within the time period t.

[0123] In some examples, the expression form of the second rule can be shown in Table 3.

[0124] Table 3

[0125]

[0126] The contents shown in Table 3 above are only for illustration, and n1, n2, n3, a1, a2 and a3 are preset constants.

[0127] S204: Based on the account movement event in which the transaction amount exceeds the specified amount in the stream data, determine it as a third complex event matching the third rule.

[0128] Among them, the third rule can be understood as the occurrence of a single large-scale account movement event (i.e., an account movement event in which the transaction amount exceeds the specified amount). To this end, the third complex event in the stream data can be determined by monitoring financial data exceeding the specified amount.

[0129] S205: Based on the account movement event in which the account balance in the stream data meets the preset limit, determine it as a fourth complex event matching the fourth rule.

[0130] Among them, the fourth rule can be understood as the account balance reaching a preset limit. To this end, the fourth complex event in the flow data can be determined by monitoring the account balance above a certain limit in the transaction flow.

[0131] S206: Based on the account movement event in which the floating value of the balance in the stream data meets the preset requirements, determine it as a fifth complex event matching the fifth rule.

[0132] The floating balance value is the change in the account balance within a specified time. The fifth rule can be understood as the account balance fluctuates greatly in a short period of time, and then recovers to the original value within a certain amount. Therefore, the fifth complex event in the stream data can be determined by monitoring the floating balance value.

[0133] In some examples, assume that at the beginning of time period t, the original value of the bank account balance is 20,000 yuan, and the account balance changes 6 times during time period t. Fig.10 As shown, the floating values ​​of the balance are RMB 15,000, RMB 23,000, RMB 14,000, RMB 30,000, RMB 14,500 and RMB 21,000 respectively.

[0134] In a possible implementation manner, the floating balance value meets the preset requirement, which can be understood as the floating balance value satisfies the specified value range.

[0135] The process shown in S201 - S206 above can use predefined pattern rules to identify corresponding complex events from the stream data.

[0136] like Figure 3 As shown, it is a flow chart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application, including the following steps.

[0137] S301: Determine a corresponding first characteristic value based on the number of occurrences of a first complex event.

[0138] The implementation process of determining the corresponding first eigenvalue based on the number of occurrences of the first complex event can be understood as performing an estimation calculation according to the frequency shown in Table 2 to obtain the first eigenvalue. The specific calculation process can be seen in formula (1).

[0139]

[0140] In formula (1), T1 represents the first eigenvalue, n represents the number of occurrences of the first complex event within a specified time, and n minj Represents the minimum value of the number of occurrences in the jth frequency segment, n maxj Represents the maximum number of occurrences in the jth frequency segment.

[0141] S302: Determine a corresponding second characteristic value based on the transaction amount of the second complex event.

[0142] The process of determining the corresponding second eigenvalue based on the transaction amount of the second complex event can be understood as performing weight calculation according to multi-dimensional factors to obtain the second eigenvalue. The specific calculation process can be seen in formula (2).

[0143]

[0144] In formula (2), T2 represents the second eigenvalue, n i The number of occurrences of each amount segment in the second complex event represented within the specified time. The total number of occurrences within the specified time is n = n1 + n2 + ... + n k , P j Represents the weight ratio of the jth amount segment. The higher the amount segment, the higher the P j The larger the exponential growth span, and 1=P1+P2+...+P m .

[0145] S303: Determine a corresponding third characteristic value based on the transaction amount of the third complex event.

[0146] Among them, the transaction amount of the third complex event can be reduced according to a preset proportional value to obtain the corresponding third characteristic value T3.

[0147] S304: Based on the account balance of the fourth complex event, determine a corresponding fourth characteristic value.

[0148] The account balance of the fourth complex event may be reduced according to a preset proportional value to obtain a corresponding fourth eigenvalue T4.

[0149] S305: Based on the balance floating value of the fifth complex event, determine the corresponding fifth characteristic value.

[0150] Among them, based on the balance floating value of the fifth complex event, determining the corresponding fifth eigenvalue can be understood as the fifth eigenvalue obtained by calculating the degree of dispersion based on the proportion of upper and lower balance fluctuations, the difference between the initial and final balances, and the variance.

[0151] In some examples, the fifth complex event within a specified time is monitored, and the account balance bal in each fifth complex event is obtained. i , record the number of records that float up and down, if there is no floating, directly output 0, if there is floating, calculate the degree of dispersion according to the variance, and output it according to the reduction factor X. Specifically, the calculation process can be seen in formulas (3) and (4).

[0152]

[0153] In formula (3), bal evaRepresents the average account balance within a specified period of time, Z and Y are specific limit values, and sriBal represents the degree of dispersion.

[0154] S306: Determine the reconciliation weight of the stream data based on the first eigenvalue, the second eigenvalue, the third eigenvalue, the fourth eigenvalue, and the fifth eigenvalue.

[0155] The first eigenvalue, the second eigenvalue, the third eigenvalue, the fourth eigenvalue and the fifth eigenvalue may be fused to obtain the reconciliation weight of the stream data. Specifically, the implementation method of feature fusion may be as shown in formula (5).

[0156]

[0157] In formula (5), T represents the reconciliation weight.

[0158] The process shown in S301-S306 above can determine the reconciliation weight of the stream data based on multiple complex events.

[0159] like Figure 4 As shown, it is a flow chart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application, including the following steps.

[0160] S401: When the bank account reaches the designated payment date, the current status of the bank account and the preset reconciliation cycle are obtained.

[0161] Among them, the current status of the bank account and the preset reconciliation cycle can be obtained directly from the accounting system.

[0162] S402: Determine whether the current status indicates that the bank account has bad behavior.

[0163] If the current status indicates that the bank account has bad behavior, S403 is executed; if the current status indicates that the bank account has no bad behavior, S404 is executed.

[0164] In some examples, bad behavior includes, but is not limited to: there is a record of customers failing to reconcile accounts for multiple consecutive times, resulting in account closure, or the bank account is involved in a case or has regulatory requirements.

[0165] S403: Adjust the preset reconciliation cycle according to the specified frequency to obtain a dynamically updated reconciliation cycle.

[0166] Among them, the dynamically updated reconciliation cycle is shorter than the preset reconciliation cycle.

[0167] In some examples, if a customer has a record of account closure due to multiple consecutive failures to reconcile, it is necessary to increase the frequency of the reconciliation cycle, that is, shorten the preset reconciliation cycle. In addition, if the bank account is involved in a case or has regulatory requirements, the reconciliation cycle can be directly increased to the highest frequency, that is, shorten the preset reconciliation cycle to the minimum value.

[0168] It is understandable that, for bad behaviors in different scenarios, the preset reconciliation cycle can be adjusted according to the corresponding specified frequency to obtain a dynamically updated reconciliation cycle.

[0169] S404: Based on the specified period, determine the dynamically updated reconciliation period.

[0170] If the current status indicates that there is no bad behavior in the bank account, then the specified period X=-1 is used as the dynamically updated reconciliation period.

[0171] The process shown in S401-S404 above can realize dynamic update of the reconciliation cycle based on the current status of the bank account.

[0172] like Figure 5 As shown, it is a flow chart of another bank-enterprise reconciliation dynamic cycle management method provided in an embodiment of the present application, including the following steps.

[0173] S501: Obtain a preset reconciliation cycle for a bank account.

[0174] Among them, every time the bank account reaches the payment date, the preset reconciliation cycle of the bank account needs to be read.

[0175] S502: Determine a target period value based on a maximum value between the dynamically updated reconciliation period and a preset reconciliation period.

[0176] The dynamically updated reconciliation cycle is compared with the preset reconciliation cycle to determine the maximum value between the dynamically updated reconciliation cycle and the preset reconciliation cycle.

[0177] S503: Determine a corresponding target reconciliation weight based on the target period value.

[0178] Among them, the target reconciliation weight corresponding to the target cycle value can be determined by querying the reconciliation cycle level table.

[0179] S504: Determine a maximum value of the target reconciliation weight and the reconciliation weight of the stream data as a modified reconciliation weight.

[0180] The target reconciliation weight is compared with the reconciliation weight of the stream data, and the comparison process is shown in formula (6) to obtain the modified reconciliation weight.

[0181]

[0182] In formula (6), T end represents the modified reconciliation weight, T represents the reconciliation weight of the streaming data, and T X Represents the target reconciliation weight.

[0183] The process shown in S501-S504 above can be used to modify the reconciliation weight using the dynamically updated reconciliation cycle to obtain the modified reconciliation weight.

[0184] like Figure 6 As shown, it is a schematic diagram of the architecture of a bank-enterprise reconciliation dynamic cycle management device provided in an embodiment of the present application, including the units shown below.

[0185] The data acquisition unit 100 is used to acquire the stream data of the bank account; the stream data includes the account movement events occurring at any time and the corresponding detailed information.

[0186] The rule matching unit 200 is used to determine complex events that match multiple predefined pattern rules from the stream data according to the detailed information of the account activity event.

[0187] Optionally, the detailed information includes at least the transaction amount and the account balance; the rule matching unit 200 is specifically used to: obtain multiple pre-defined pattern rules; the multiple pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule; based on account movement events that occur multiple times in succession within a specified time in the stream data, determine it as a first complex event that matches the first rule; based on account movement events that occur multiple times in succession within a specified time in the stream data and the transaction amount meets a specified threshold, determine it as a second complex event that matches the second rule; based on account movement events in the stream data where the transaction amount exceeds the specified amount, determine it as a third complex event that matches the third rule; based on account movement events in the stream data where the account balance meets a preset limit, determine it as a fourth complex event that matches the fourth rule; based on account movement events in the stream data where the balance floating value meets preset requirements, determine it as a fifth complex event that matches the fifth rule; the balance floating value is the change in the account balance within the specified time.

[0188] Optionally, the rule matching unit 200 is specifically used to: determine the corresponding first characteristic value based on the number of occurrences of the first complex event; determine the corresponding second characteristic value based on the transaction amount of the second complex event; determine the corresponding third characteristic value based on the transaction amount of the third complex event; determine the corresponding fourth characteristic value based on the account balance of the fourth complex event; determine the corresponding fifth characteristic value based on the balance floating value of the fifth complex event; determine the reconciliation weight of the stream data based on the first characteristic value, the second characteristic value, the third characteristic value, the fourth characteristic value and the fifth characteristic value.

[0189] The weight determination unit 300 is used to determine the reconciliation weight of the stream data based on multiple complex events.

[0190] The weight correction unit 400 is used to correct the reconciliation weight using the dynamically updated reconciliation cycle to obtain a corrected reconciliation weight; the reconciliation cycle is dynamically updated based on the current status of the bank account.

[0191] Optionally, the weight correction unit 400 is specifically used to: obtain the current status of the bank account and the preset reconciliation cycle when the bank account reaches the specified payment date; if the current status indicates that the bank account has bad behavior, adjust the preset reconciliation cycle according to the specified frequency to obtain a dynamically updated reconciliation cycle; the dynamically updated reconciliation cycle is less than the preset reconciliation cycle; if the current status indicates that the bank account has no bad behavior, determine it as the dynamically updated reconciliation cycle based on the specified period.

[0192] Optionally, the weight correction unit 400 is specifically used to: obtain a preset reconciliation cycle for a bank account; determine a target cycle value based on a maximum value between a dynamically updated reconciliation cycle and a preset reconciliation cycle; determine a corresponding target reconciliation weight based on the target cycle value; and determine a revised reconciliation weight based on a maximum value between the target reconciliation weight and the reconciliation weight of the stream data.

[0193] The cycle determination unit 500 is used to determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle level table; the reconciliation cycle level table includes a plurality of sample reconciliation weights and corresponding recommended reconciliation cycles.

[0194] The information generation unit 600 is used to generate business information for helping sales staff to conduct bank-enterprise reconciliation based on the target reconciliation cycle; the business information includes the collection plan and account closure date of the bank account.

[0195] Each unit shown above utilizes multi-dimensional elements of accounts (i.e., multiple pattern rules) to quickly and efficiently process streaming data, and analyzes the reconciliation weight of streaming data through multiple dimensions (i.e., multiple complex events) to map business information of personalized cycles, improve customer experience, reduce financial risks, and effectively improve the reconciliation efficiency of bank staff.

[0196] The present application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the bank-enterprise reconciliation dynamic cycle management method provided by the present application.

[0197] The present application also provides an electronic device, including: a processor, a memory and a bus. The processor and the memory are connected via a bus, the memory is used to store programs, and the processor is used to run programs, wherein the bank-enterprise reconciliation dynamic cycle management method provided by the present application is executed when the program is run.

[0198] In addition, the functions described above in the embodiments of the present application may be at least partially performed by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0199] Although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present application. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0200] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A bank-enterprise reconciliation dynamic cycle management method, characterized in that: include: Obtaining bank account stream data; the stream data includes account movement events occurring at any time and corresponding detailed information; According to the detailed information of the account movement event, determining a complex event matching a plurality of predefined pattern rules from the stream data; Determining a reconciliation weight of the stream data based on multiple complex events; Using the dynamically updated reconciliation cycle, the reconciliation weight is modified to obtain a modified reconciliation weight; The reconciliation cycle is dynamically updated based on the current status of the bank account; Determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle rating table; the reconciliation cycle rating table includes a plurality of sample reconciliation weights and corresponding recommended reconciliation cycles; Based on the target reconciliation cycle, business information is generated to help sales staff conduct bank-enterprise reconciliation; the business information includes the collection plan and account closure date of the bank account.

2. The method according to claim 1, characterized in that The detailed information at least includes the transaction amount and the account balance; According to the detailed information of the account movement event, a complex event matching a plurality of predefined pattern rules is determined from the stream data, including: Acquire a plurality of predefined pattern rules; the plurality of pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule; Based on multiple consecutive account movement events in the stream data within a specified time period, determining a first complex event that matches the first rule; Determine, based on the account movement events in the stream data that occur multiple times continuously within the specified time and whose transaction amounts meet the specified threshold, as second complex events that match the second rule; Based on the account movement event in which the transaction amount exceeds the specified amount in the stream data, determining it as a third complex event matching the third rule; Based on the account movement event in which the account balance in the stream data meets the preset limit, determining it as a fourth complex event matching the fourth rule; Based on the account movement event whose balance floating value in the stream data meets the preset requirements, it is determined to be the fifth complex event matching the fifth rule; the balance floating value is the change value of the account balance within the specified time.

3. The method according to claim 2, characterized in that Based on multiple complex events, the reconciliation weight of the stream data is determined, including: Based on the number of occurrences of the first complex event, determining a corresponding first eigenvalue; Based on the transaction amount of the second complex event, determining a corresponding second characteristic value; Based on the transaction amount of the third complex event, determining a corresponding third characteristic value; Based on the account balance of the fourth complex event, determining a corresponding fourth eigenvalue; Based on the balance floating value of the fifth complex event, determining a corresponding fifth characteristic value; A reconciliation weight of the stream data is determined based on the first feature value, the second feature value, the third feature value, the fourth feature value, and the fifth feature value.

4. The method according to claim 1, characterized in that: The process of dynamically updating the reconciliation cycle based on the current status of the bank account includes: When the bank account reaches a specified payment date, obtaining the current status of the bank account and a preset reconciliation cycle; If the current status indicates that the bank account has bad behavior, the preset reconciliation cycle is adjusted according to a specified frequency to obtain a dynamically updated reconciliation cycle; the dynamically updated reconciliation cycle is less than the preset reconciliation cycle; If the current status indicates that there is no bad behavior in the bank account, based on the specified period, it is determined as a dynamically updated reconciliation period.

5. The method according to claim 1, characterized in that The reconciliation weight is modified using the dynamically updated reconciliation cycle to obtain a modified reconciliation weight, including: Obtaining a preset reconciliation cycle for the bank account; Determine the target period value based on the maximum value between the dynamically updated reconciliation period and the preset reconciliation period; Based on the target period value, determining a corresponding target reconciliation weight; A maximum value between the target reconciliation weight and the reconciliation weight of the stream data is determined as a modified reconciliation weight.

6. A bank-enterprise reconciliation dynamic cycle management device, characterized in that: include: A data acquisition unit, used to acquire stream data of a bank account; the stream data includes account movement events occurring at any time and corresponding detailed information; A rule matching unit, configured to determine, from the stream data, complex events that match a plurality of predefined pattern rules according to the detailed information of the account movement event; A weight determination unit, configured to determine a reconciliation weight of the stream data based on a plurality of complex events; A weight correction unit, used to correct the reconciliation weight using the dynamically updated reconciliation cycle to obtain a corrected reconciliation weight; The reconciliation cycle is dynamically updated based on the current status of the bank account; A cycle determination unit, configured to determine a target reconciliation cycle corresponding to the modified reconciliation weight according to a preset reconciliation cycle rating table; the reconciliation cycle rating table includes a plurality of sample reconciliation weights and corresponding recommended reconciliation cycles; An information generating unit is used to generate business information for helping salespersons to conduct bank-enterprise reconciliation based on the target reconciliation cycle; the business information includes the collection plan and account closure date of the bank account.

7. The device according to claim 6, characterized in that The detailed information at least includes the transaction amount and the account balance; the rule matching unit is specifically used for: Acquire a plurality of predefined pattern rules; the plurality of pattern rules include a first rule, a second rule, a third rule, a fourth rule and a fifth rule; Based on multiple consecutive account movement events in the stream data within a specified time period, determining a first complex event that matches the first rule; Determine, based on the account movement events in the stream data that occur multiple times continuously within the specified time and whose transaction amounts meet the specified threshold, as second complex events that match the second rule; Based on the account movement event in which the transaction amount exceeds the specified amount in the stream data, determining it as a third complex event matching the third rule; Based on the account movement event in which the account balance in the stream data meets the preset limit, determining it as a fourth complex event matching the fourth rule; Based on the account movement event whose balance floating value in the stream data meets the preset requirements, it is determined to be the fifth complex event matching the fifth rule; the balance floating value is the change value of the account balance within the specified time.

8. The device according to claim 7, characterized in that The rule matching unit is specifically used for: Based on the number of occurrences of the first complex event, determining a corresponding first eigenvalue; Based on the transaction amount of the second complex event, determining a corresponding second characteristic value; Based on the transaction amount of the third complex event, determining a corresponding third characteristic value; Based on the account balance of the fourth complex event, determining a corresponding fourth eigenvalue; Based on the balance floating value of the fifth complex event, determining a corresponding fifth characteristic value; A reconciliation weight of the stream data is determined based on the first feature value, the second feature value, the third feature value, the fourth feature value, and the fifth feature value.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein the program, when executed by a processor, executes the bank-enterprise reconciliation dynamic cycle management method described in any one of claims 1-5.

10. An electronic device, characterized in that: include: processor, memory, and bus; The processor is connected to the memory via the bus; The memory is used to store programs, and the processor is used to run programs, wherein the program, when run by the processor, executes the bank-enterprise reconciliation dynamic cycle management method described in any one of claims 1-5.

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