Overdue payment collection support device, overdue payment collection support method, and overdue payment collection support program

Abstract

To reduce a load in a case of allocating persons in charge of credit delinquency demand, and to level task loads of delinquency demand according to the level of each person in charge.SOLUTION: A delinquency demand support device according to the present embodiment includes: delinquent debtor acquisition means which acquires delinquent debtor data including a debtor of a credit whose payment is delinquent, the number of delinquent months and the average calling hours for delinquency demand for the credit on a scheduled collection date prior to a base date; and delinquency person in charge allocation means which sets a debtor category that shows the degree of attention of the debtor and the number of delinquent months as person in charge allocation setting for each person in charge who performs demand, refers to the person in charge allocation setting and the debtor data and allocates a person in charge who performs demand for each debtor on the basis of the delinquent debtor data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a delay urging support device, a delay urging support method, and a delay urging support program.

Background Art

[0002] For example, in the case where a debtor delays repayment of financial claims such as loans and credits, there is a business of making a demand. As a system related to the delay urging of financial claims, for example, there is Patent Document 1.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above Patent Document 1, there is no description regarding reducing the load when allocating a person in charge of delay urging of claims and leveling the workload of delay urging according to the level of each person in charge.

[0005] The present invention has been made in view of the above, and an object thereof is to provide a delay urging support device, a delay urging support method, and a delay urging support program capable of reducing the load when allocating a person in charge of delay urging of claims and leveling the workload of delay urging according to the level of each person in charge.

Means for Solving the Problems

[0006] To solve the above-mentioned problems and achieve the objective, the present invention provides a delinquent debt collection support device comprising a control unit and a storage unit for assigning personnel to make collection requests to debtors of debts that are in arrears, wherein the storage unit stores debtor data including debtors and debtor classifications indicating the debtor's level of attention, the control unit comprises a delinquent debtor acquisition means for acquiring delinquent debtor data including debtors, the number of months in arrears, and the average time spent making collection requests for debts with collection dates prior to the reference date, and a delinquent personnel assignment means for setting a debtor classification indicating the debtor's level of attention and the number of months in arrears as personnel assignment settings for each person responsible for making collection requests, and assigning personnel to make collection requests to each debtor based on the delinquent debtor data by referring to the personnel assignment settings and the debtor data.

[0007] Furthermore, according to one aspect of the present invention, the delinquency officer assignment means expands the combinations of debtor classification and delinquency months based on the officer assignment setting, generates first data by assigning the corresponding officer to each expanded combination, expands the data of debtor, delinquency months, and average call time for delinquency reminders from the delinquency debtor data and the debtor classification data from the debtor data, sorts the average call time for delinquency reminders in ascending order for each debtor classification and delinquency months, and assigns officers in order to those whose debtor classification and delinquency months match in the first data for each debtor classification and delinquency months.

[0008] Furthermore, according to one aspect of the present invention, the delinquent debtor data may include the number of delinquent collections, which is the number of times delinquent debts have been collected for each debtor, and the assigned personnel setting may also include the number of delinquent collections for each debtor.

[0009] Furthermore, according to one aspect of the present invention, the delinquency officer assignment means expands the combinations of the debtor category, number of months in arrears, and number of delinquent collections based on the officer assignment setting, generates first data by assigning the corresponding officer to each expanded combination, expands the data of debtor, number of months in arrears, number of delinquent collections, and average call time for delinquent collection from the delinquent debtor data and the debtor category data from the debtor data, then sorts the average call time for delinquent collections in ascending order for each debtor category, number of months in arrears, and number of delinquent collections, and assigns in order the officers whose debtor category, number of months in arrears, and number of delinquent collections match in the first data for each debtor category, number of months in arrears, and number of delinquent collections.

[0010] Furthermore, according to one aspect of the present invention, the storage unit stores a master set associated with each person responsible for reminding debtors, including a debtor classification indicating the debtor's level of attention, the number of months in arrears, and the number of times overdue collections have been made. The delinquency officer assignment means may read the settings of the master as the initial values ​​for the officer assignment settings, and set the officer assignment settings by adding, deleting, or editing these initial values.

[0011] Furthermore, according to one aspect of the present invention, the claim may include a financial claim.

[0012] Furthermore, in order to solve the above-mentioned problems and achieve the objectives, the present invention is a method for supporting overdue debt collection performed by an information processing device equipped with a control unit and a storage unit, wherein the storage unit stores debtor data including debtors and debtor classifications indicating the debtor's level of attention, and the method includes: an overdue debtor acquisition step performed by the control unit to acquire overdue debtor data including debtors, months of overdueness, and average call time for overdue debt collection for debts with collection dates prior to the reference date; and an overdue debtor assignment step in which, for each person in charge of collection, a debtor classification indicating the debtor's level of attention and months of overdueness are set as a person in charge assignment setting, and a person in charge of collection is assigned to each debtor based on the overdue debtor data by referring to the person in charge assignment setting and the debtor data.

[0013] Furthermore, in order to solve the above-mentioned problems and achieve the objectives, the present invention is a delinquent debt collection support program to be executed by an information processing device equipped with a control unit and a storage unit, wherein the storage unit stores debtor data including debtors and debtor classifications indicating the debtor's level of attention, and the control unit is configured to execute a delinquent debtor acquisition step for delinquent debtors for debts with a scheduled collection date prior to the reference date, including the debtor, the number of months of delinquency, and the average time spent on delinquent debt collection calls for debts that are delinquent in payment, and a delinquent debtor assignment step for which the control unit executes a debtor classification indicating the debtor's level of attention and the number of months of delinquency as a person in charge of debtor collection, and assigns a person in charge to each debtor to perform debtor collection based on the delinquent debtor data by referring to the person in charge assignment settings and the debtor data. [Effects of the Invention]

[0014] According to the present invention, the burden on assigning personnel responsible for collecting overdue debts is reduced, and the workload of collecting overdue debts can be leveled out according to the skill level of each person in charge. [Brief explanation of the drawing]

[0015] [Figure 1] Figure 1 is a block diagram showing an example of the configuration of the overdue payment reminder support device in this embodiment. [Figure 2] Figure 2 shows an example of the configuration of the assignee allocation setting master. [Figure 3] Figure 3 is a diagram illustrating the overall processing outline of the control unit of the overdue payment reminder support device in this embodiment. [Figure 4] Figure 4 is a diagram illustrating a specific example of the processing of the control unit of the overdue payment reminder support device in this embodiment. [Figure 5] Figure 5 is a diagram illustrating a specific example of the processing of the control unit of the overdue payment reminder support device in this embodiment. [Figure 6] Figure 6 is a diagram illustrating a specific example of the processing performed by the control unit of the overdue payment reminder support device in this embodiment. [Figure 7] FIG. 7 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 8] FIG. 8 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 9] FIG. 9 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 10] FIG. 10 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 11] FIG. 11 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 12] FIG. 12 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 13] FIG. 13 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 14] FIG. 14 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 15] FIG. 15 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 16] FIG. 16 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 17] FIG. 17 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 18] FIG. 18 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 19] FIG. 19 is a diagram for explaining a specific example of the processing of the control unit of the delay urging support device in the present embodiment. [Figure 20]Figure 20 is a diagram illustrating a specific example of the processing of the control unit of the overdue payment reminder support device in this embodiment. [Figure 21] Figure 21 is a diagram illustrating a specific example of the processing of the control unit of the overdue payment reminder support device in this embodiment. [Figure 22] Figure 22 is a diagram illustrating a specific example of the processing performed by the control unit of the overdue payment reminder support device in this embodiment. [Figure 23] Figure 23 is a diagram illustrating a specific example of the processing performed by the control unit of the overdue payment reminder support device in this embodiment. [Modes for carrying out the invention]

[0016] Embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to these embodiments.

[0017] [1. Overview] For example, regarding financial claims such as loans and credit, there is the task of demanding repayment when a debtor defaults on their repayment. This repayment task presents the following challenges 1-3.

[0018] Problem 1: The task of assigning debtors to be contacted for debt collection was done manually on a daily basis, which placed a heavy burden on the staff.

[0019] Challenge 2: Debtors need to be assigned according to the skills and level of the person in charge.

[0020] Problem 3: The time required for debt collection varies significantly from person to person.

[0021] Therefore, in this embodiment, by systematizing the assignment of personnel according to their skill level based on indicators such as the degree of delinquency and the debtor's evaluation, the burden of assigning personnel for delinquency collection is reduced, and the workload of delinquency collection is leveled out according to the skill level of each personnel. Specifically, this embodiment provides the following effects 1 to 3.

[0022] Effect 1: By having the system automatically perform the allocation process, the time required for the allocation itself can be reduced.

[0023] Effect 2: By using indicators such as debtor classification, number of months in arrears, and number of collection attempts for overdue payments, assignments can be made according to the skill level of the person in charge.

[0024] Effect 3: By using indicators such as the average call duration, the time spent on follow-up can be standardized for each person in charge.

[0025] The delinquency collection support device of this embodiment can be applied to the entire financial industry (non-banks, financial institutions) and all industries that conduct financial business (lending, credit), as well as to other industries and sectors, and can be applied to delinquency collection of debts other than financial claims.

[0026] [2. Structure] Figure 1 is a block diagram showing an example of the configuration of the overdue payment reminder support device 100 according to this embodiment. The overdue payment reminder support device 100 is a commercially available desktop personal computer. However, the overdue payment reminder support device 100 is not limited to stationary information processing devices such as desktop personal computers, but may also be portable information processing devices such as commercially available notebook personal computers, PDAs (Personal Digital Assistants), smartphones, and tablet personal computers. In Figure 1, the overdue payment reminder support device 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. Each part of the overdue payment reminder support device 100 is connected to communicate via any communication path.

[0027] The communication interface unit 104 connects the delinquency reminder support device 100 to the network 300 via communication devices such as routers and wired or wireless communication lines such as dedicated lines. The communication interface unit 104 has the function of communicating data with other devices via communication lines. Here, the network 300 has the function of connecting the delinquency reminder support device 100 and the server 200 so that they can communicate with each other, and is, for example, the internet or a LAN (Local Area Network).

[0028] The input / output interface unit 108 is connected to an input device 112 and an output device 114. The output device 114 can be a monitor (including a home television), a speaker, or a printer. The input device 112 can be a keyboard, mouse, microphone, or a monitor that works in conjunction with the mouse to provide pointing device functionality. Note that in the following, the output device 114 may be referred to as the monitor 114.

[0029] The memory unit 106 stores various databases, tables, and files. The memory unit 106 also stores computer programs that work in cooperation with the OS (Operating System) to give instructions to the CPU (Central Processing Unit) to perform various processes. As the memory unit 106, for example, memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, and optical disks can be used.

[0030] The storage unit 106 includes a person-assigned-assignment setting master 106a, a data table 106b, and a work table 106c. Figure 2 shows an example of the configuration of the person-assigned-assignment setting master 106a.

[0031] The 담당자 정리요

[0032] Data table 106b is a table for storing various types of data, such as collection schedule data, collection results data, transaction history data, direct debit request data, debtor / debt linking data, debtor data, negotiation history data, delinquent debt data, and delinquent debtor data.

[0033] The planned collection data is information that is planned to be collected from the debtor and may include the debt number, collection sequence number, planned collection year and month, planned collection date, and planned collection amount.

[0034] The collection performance data is information on the actual amount collected from debtors and may include the debt number, collection sequence number, transaction sequence number, collection date, collection amount, delinquent collection FLG ("0: not collected", "1: collected") indicating whether or not a delinquent debt was collected, and collection completion FLG ("0: not collected", "1: collected") indicating whether or not the debt was collected.

[0035] Transaction history data may include the loan number, transaction sequence number, transaction type ("1: Loan disbursement", "2: Collection"), transaction date, loan amount, collection amount, and next scheduled collection date.

[0036] Account transfer request data may include the debt number, account transfer date, billing amount, and transfer result code ("0: Transfer in progress," "Z: Requested").

[0037] Debtor / claim linking data is data used to link debtors with claims, and may include claim numbers and debtor numbers.

[0038] Negotiation history data may include debtor number, negotiation date, negotiation start time, negotiation end time, and negotiation category ("1: Telephone call", "2: Demand letter").

[0039] Delinquent debt data may include debt number, number of months overdue, number of collection attempts, and total amount overdue.

[0040] The delinquent debtor data may include debtor number, number of months delinquent, number of collection attempts, average call duration, total amount of debt, and collection officer. Of these, "debtor number," "number of months delinquent," "number of collection attempts," "average call duration," and "total amount of debt" are set during the delinquent debtor extraction process. "Collection officer" is set during the debt officer assignment process.

[0041] Work table 106c is a table used as a work area for the control unit 102, and work data (intermediate data), etc. are generated there. For example, in the delinquent debtor allocation process described later, the first to third data, which are intermediate data until the person in charge of the delinquent debtor data is assigned, are generated (expanded) there.

[0042] The control unit 102 is a CPU or the like that comprehensively controls the overdue payment reminder support device 100. The control unit 102 has internal memory for storing control programs such as the OS, programs that define various processing procedures, and required data, and executes various information processing based on these stored programs.

[0043] The control unit 102 is configured to access the person in charge assignment setting master 106a, data table 106b, work table 106c, etc., which are stored in the storage unit 106. Note that the person in charge assignment setting master 106a, data table 106b, and work table 106c may be located in another location (for example, on the server 200), as long as the control unit 102 can access them.

[0044] Functionally, the control unit 102 comprises a data registration unit 102a, a delinquent debtor acquisition unit 102b, a personnel assignment unit 102c, and a screen display control unit 103c.

[0045] The data registration unit 102a registers and updates various types of data to the data table 106b, including data scheduled for collection, data on actual collection, transaction history data, direct debit request data, debtor / debtor linkage data, and negotiation history data.

[0046] The delinquent debtor acquisition unit 102b acquires delinquent debtor data, including the debtor, the number of months in arrears, and the average duration of calls made to reminders, for debts with a scheduled collection date prior to the reference date specified by the operator, on the delinquent debtor extraction processing screen displayed on the monitor 114, for example.

[0047] The 담당자

[0048] Specifically, the 담당자

[0049] The delinquent debtor data may include the number of times each debtor has collected their delinquent debts. The assigned personnel settings may also include the number of times each debtor has collected their delinquent debts.

[0050] In this case, the person in charge assignment unit 102c expands the combinations of the debtor category, number of months in arrears, and number of collections for arrears based on the person in charge assignment settings, generates first data by assigning the corresponding person in charge to each expanded combination, expands the data of debtor, number of months in arrears, number of collections for arrears, and average call time for arrears from the arrears debtor data and the data of debtor category from the debtor data, then sorts the average call time for arrears in ascending order for each debtor category, number of months in arrears, and number of collections for arrears, and assigns in order the person in charge whose debtor category, number of months in arrears, and number of collections for arrears match in the first data for each debtor category, number of months in arrears, and number of collections for arrears.

[0051] The person in charge assignment unit 102c may read the settings from the person in charge assignment setting master 106a as the initial values ​​for the person in charge assignment settings, and perform the person in charge assignment settings by adding, deleting, or editing these initial values.

[0052] The screen display control unit 102d controls the display and input reception of various screens (for example, the delinquent debtor extraction processing screen and the delinquent debtor assignment processing screen) to be displayed on the monitor 114.

[0053] [3. Specific Examples] Referring to Figures 1 to 23, a specific example of the processing of the control unit 102 of the overdue payment reminder support device 100 in this embodiment will be described.

[0054] (3-1. Overall Processing) Figure 3 is a flowchart illustrating the overall processing outline of the control unit 102 of the overdue payment reminder support device 100 in this embodiment. The overall processing outline of the control unit 102 of the overdue payment reminder support device 100 will be explained with reference to Figure 3.

[0055] In Figure 3, the delinquent debtor acquisition unit 102b executes the delinquent debtor extraction process (step S1). Specifically, in the delinquent debtor extraction process, for example, on the delinquent debtor extraction process screen displayed on monitor 114, the delinquent debtor acquisition unit 102b refers to the collection schedule data, collection performance data, transaction history data, direct debit request data, debtor / claim linking data, and negotiation history data in data table 106b for receivables with a collection schedule prior to the reference date specified by the operator, and acquires delinquent debtor data including the debtor, the number of months of delinquency, the number of collections for the delinquent receivables, and the average time spent on calling to reminders for delinquent payments.

[0056] The 담당자

[0057] In this case, the person in charge assignment unit 102c may use the work table 106c to expand the combinations of the debtor category, number of months in arrears, and number of collections of arrears based on the person in charge assignment settings, generate first data by assigning the corresponding person in charge to each expanded combination, and after expanding the data of debtor, number of months in arrears, number of collections of arrears, and average call time for reminders from the arrears debtor data and the debtor category data from the debtor data, sort the average call time for reminders in ascending order for each debtor category, number of months in arrears, and number of collections of arrears, and then assign in order the person in charge whose debtor category, number of months in arrears, and number of collections of arrears match in the first data for each debtor category, number of months in arrears, and number of collections of arrears.

[0058] (3-2. Sample Data) Figures 4 to 23 are diagrams showing sample data to illustrate a specific example of the processing of the control unit 102 of the overdue payment reminder support device 100 in this embodiment. A specific example of the processing of the control unit 102 of the overdue payment reminder support device 100 in this embodiment will be explained with reference to Figures 4 to 23.

[0059] (S1: Delinquent debtor extraction process) The process of extracting delinquent debtors will be explained in detail with reference to Figures 4 to 12. The delinquent debtor acquisition unit 102b, for example, on the delinquent debtor extraction processing screen displayed on the monitor 114, obtains delinquent debtor data, including the debtor, the number of months of delinquency, the number of times the delinquent debt has been collected, and the average time spent on reminders, for delinquent debtors for which payment is overdue, for debts with a collection date prior to the delinquent extraction reference date specified by the operator, by referring to the collection schedule data, collection performance data, transaction history data, direct debit request data, debtor / debt linking data, and negotiation history data in the data table 106b.

[0060] (Example of debtor data) The following explanation describes the case where the debtor extraction process was performed on November 1, 2022.

[0061] Figure 4 shows example data for debtors (1) to (4). For example, as shown in Figure 4(A), debtor (1) has debtor number "100", the target debt (1) has debt number "1000", loan date "2022 / 7 / 1", loan amount "140,000 yen", contract date "26th", repayment method "bank transfer", and is in a state of no delinquency as of 2022 / 11.

[0062] As shown in Figure 4(B), the debtor (2) has debtor number "200", the target debt (2) has debt number "2000", loan date "2022 / 7 / 1", loan amount "140,000 yen", contract date "26th", repayment method "bank transfer", and as of November 2022, it is in arrears (bank transfer request pending).

[0063] As shown in Figure 4(C), debtor (3) has debtor number "300", the target debt (3) has debt number "3000", loan date "2022 / 7 / 1", loan amount "140,000 yen", contract date "26th", repayment method "bank transfer", and is in arrears as of November 2022. Target debt (4) has debt number "4000", loan date "2022 / 7 / 1", loan amount "140,000 yen", contract date "26th", repayment method "bank transfer", and is in arrears as of November 2022.

[0064] (Assumption data) The following explanation assumes that the data shown in Figures 5 to 9 is registered in data table 106b.

[0065] Figure 5(A) shows an example of collection schedule data, and Figure 5(B) shows an example of collection results data. In Figure 5, for explanatory purposes, collection schedule data and collection results data with the same debt number and collection sequence number are treated as the same row, and an explanation of whether or not the collection is overdue is added.

[0066] The collection schedule data includes the following fields: debt number, collection SEQ, expected collection year and month, expected collection date, and expected collection amount.

[0067] The collection performance data includes the following fields: debt number, collection sequence number, transaction sequence number, collection date, collection amount, overdue collection FLG, and collection completion FLG.

[0068] For example, for the first line, debt number "1000" and collection sequence "1", the collection schedule data shows a planned collection month and year "2022 / 8", a planned collection date "2022 / 8 / 26", and a planned collection amount "30,000 yen". The collection results data shows transaction sequence "2", collection date "2022 / 8 / 26", collection amount "30,000 yen", overdue collection FLG "0", and completed collection FLG "1", indicating that the debt was collected on the scheduled date.

[0069] Furthermore, for the debt number "3000" and collection sequence "2" on line 12, the planned collection data shows a planned collection month and year of "2022 / 9", a planned collection date of "2022 / 9 / 26", and a planned collection amount of "30,000 yen". The actual collection data shows transaction sequence "3", collection date of "2022 / 9 / 30", collection amount of "20,000 yen", overdue collection FLG "1", and completed collection FLG "0". This indicates that the debt was not collected on the planned date and is partially overdue. For this overdue debt, the actual collection data shows transaction sequence "4", collection date of "2022 / 10 / 3", collection amount of "10,000 yen", overdue collection FLG "1", and completed collection FLG "1", indicating that the remaining amount has been collected.

[0070] Figure 6 shows an example of transaction history data. The transaction history data includes the following fields: loan number, transaction sequence number, transaction type, transaction date, loan amount, recovered amount, and next expected recovery date.

[0071] If the recovered amount is less than the amount scheduled for recovery in the recovery data, the next scheduled recovery date will be the same as the previous date. For example, for the debt number "3000", transaction SEQ "3", transaction type "2: Recovery", transaction date "2022 / 9 / 30", and recovered amount "20,000 yen" on line 10, the recovery has not been fully applied, so the next scheduled recovery date will remain "2022 / 9 / 26".

[0072] Figure 7 shows an example of direct debit request data. The direct debit request data includes the following fields: debt number, request year and month, direct debit date, billing amount, and debit result code.

[0073] Figure 8 shows an example of debtor-credit linkage data. Debtor-credit linkage data is data used to link credit numbers and debtor numbers, and includes fields for credit number and debtor number.

[0074] Figure 9 shows an example of negotiation history data. The negotiation history data includes the following fields: debtor number, negotiation date, negotiation start time, negotiation end time, and negotiation category. In the example shown in the figure, the first row contains debtor number "300", negotiation date "2022 / 9 / 29", negotiation start time "12:30:05", negotiation end time "12:35:10", and negotiation category "1: Call".

[0075] Figure 10 shows an example of the display of the delinquent debtor extraction processing screen. The delinquent debtor extraction processing screen has a field for specifying the delinquent extraction reference date and an execute button. When the delinquent extraction reference date is specified and the execute button is pressed, the delinquent debtor acquisition unit 102b executes the delinquent debtor extraction process based on the specified delinquent extraction reference date. In the example shown in the figure, the delinquent extraction reference date "2022 / 11 / 1" is specified, and in the following example, the delinquent debtor extraction process is executed based on the delinquent extraction reference date "2022 / 11 / 1".

[0076] Refer to Figures 11 and 12 to explain the flow of the delinquent debtor extraction process (1) to (5).

[0077] (1) Extract receivables whose next scheduled collection date in the transaction history data is less than the default extraction date. In Figure 11, in this example, we extract receivables whose next scheduled collection date is before the delinquency extraction date "2022 / 11 / 1". If the next scheduled collection date is the same as the delinquency extraction date, it is treated as not delinquent and is therefore excluded from extraction.

[0078] For example, the next scheduled collection date for debt number "1000" on "2022 / 11 / 28" is after the delinquency extraction reference date of "2022 / 11 / 1", so it will not be included in the extraction. The next scheduled collection date for debt number "2000" on "2022 / 10 / 26" is before the delinquency extraction reference date of "2022 / 11 / 1", so it will be included in the extraction.

[0079] (2) Calculate the number of months overdue and the number of times overdue collection has been attempted. As shown in Figure 11(A), the "Number of Months in Delinquency" is calculated starting from 1 on the day following the next scheduled collection date in the transaction history data, up to the delinquency reference date, and is counted up for each contract date. The "Number of Delinquency Collections" is calculated by aggregating the number of collection sequences with the delinquency collection flag FLG=1 in the collection performance data.

[0080] For example, for debt number "2000" with a next scheduled collection date of "2022 / 10 / 26", it has not been collected as of the delinquency extraction date of "2022 / 11 / 1". Therefore, the number of months in arrears is "1", and the number of delinquent collections is "0" (because the total of collection sequences for delinquent collection FLG=1 is "0").

[0081] For debt number "4000" with a next scheduled collection date of "2022 / 9 / 26", it has not been collected as of the delinquency extraction date of "2022 / 11 / 1". Therefore, the number of months in arrears is "2", and the number of delinquent collections is "0" (because the total of collection sequences for delinquent collection FLG=1 is "0").

[0082] (3) If the direct debit request data indicates that a direct debit request is in progress, the number of months in arrears will be reduced by one month. In Figure 12, for debt number "2000" with a next scheduled collection date of "2022 / 10 / 26", the number of months in arrears is reduced by one month to "0" because the direct debit request is pending.

[0083] (4) For debts with a delinquent period of months > 0, create delinquent debt data. The "Number of Months in Delinquency" in the delinquent debt data will be updated to reflect the maximum value for debts from the same debtor. The "Number of Delinquency Collections" in the delinquent debt data will be updated to reflect the total number of delinquency collections from the same debtor.

[0084] In this example, delinquent debt data is generated as shown in Figure 12(A). In the example shown in the figure, the first row contains debt number "3000", number of months delinquent "1", number of delinquent collections "1", and total delinquent amount "30,000 yen", while the second row contains debt number "4000", number of months delinquent "2", number of delinquent collections "0", and total delinquent amount "60,000 yen".

[0085] (5) Based on the delinquent debt data, delinquent debtor data is created by referring to debtor / debt linking data and negotiation history data. The "Debtor Number" in the delinquent debtor data is converted by referring to the debtor-debtor linking data in the delinquent debtor data shown in Figure 8. The "Average Call Time" in the delinquent debtor data is calculated by referring to the negotiation history data and calculating the average call time per month for the same debtor.

[0086] Figure 12(B) shows an example of negotiation history data. In this figure, the average call duration for debtor "300" is (00:05:05 + 00:05:19 + 00:02:28) ÷ 2 (divided by 2 because it is 2 months) = 00:06:26.

[0087] Figure 12(C) shows an example of the data generated for delinquent debtors. In the example shown in the figure, the debtor number is "300", the number of months in arrears is "2", the number of times delinquent collections have been made is "1", the average call duration is "00:06:26", and the total amount of delinquent debt is "90,000".

[0088] (S2: Assignment of delinquent debt collection officers) Refer to Figures 13 to 23 to explain a specific example of the delinquent debt collection assignment process. The debtor assignment unit 102c sets a debtor classification indicating the debtor's level of attention, the number of months in arrears, and the number of times the debt has been collected as debtor assignment settings for each debtor responsible for collection. Referring to these debtor assignment settings and debtor data, the unit assigns a debtor responsible for collection to each debtor based on the acquired delinquent debtor data.

[0089] (Assumption data) This section explains how to perform the overdue payment assignment process using a different data example than the one described above.

[0090] Figure 13 shows an example of delinquent debtor data. In the delinquent debtor assignment process, the "delinquent debtor" is assigned (set) from the delinquent debtor data. Figure 14 shows an example of debtor data. Figure 15 shows an example of the debtor assignment setting master 106a. The debtor assignment setting master 106a is the master for the initial display of the debtor assignment setting.

[0091] Figure 16 shows an example of the display of the delinquent debt officer assignment processing screen 500. The delinquent debt officer assignment processing screen 500 includes a header area 501 with a button to select "Clear all delinquent debt officers for all debtors and assign new ones" or "Assign delinquent debt officers to debtors who do not have an assigned officer" and a confirmation button 601, an area 502 for setting officer assignments, an area 503 for displaying officer assignment results, an area 504 for displaying debtor assignment results, a new button, a delete button, an edit button, an execution assignment button, and a registration button.

[0092] If the "Clear all delinquency officers for all debtors and reassign" button is selected in Header Area 501, all delinquency officers in the debtor data will be cleared and the delinquency officer reassignment process will be executed. If the "Assign delinquency officers to debtors who do not currently have any" button is selected, the process of assigning delinquency officers to debtors who do not currently have any delinquency officers assigned will be executed.

[0093] (1) When the operator selects either the "Clear all delinquency officers for all debtors and assign new ones" button or the "Assign delinquency officers to debtors who do not currently have any assigned officers" button and presses the confirm button, the data registered in the officer assignment setting master 106a is initially displayed in area 502. The following describes the case when "Clear all delinquency officers for all debtors and assign new ones" is selected.

[0094] (2) Set the data for assigning personnel in area 502. If you want to register a new person, press the new button 602, and the personnel assignment setting screen 600 shown in Figure 17 will be launched. The personnel assignment setting screen 600 has fields for entering the personnel code, debtor category, number of months in arrears, and number of times in arrears have been collected, as well as a confirm button.

[0095] On the 담당자 안나일

[0096] In addition, when it is desired to delete the content of the data in area 502, the data can be deleted by pressing the delete button. When it is desired to edit the content of the data in area 502, after pressing the edit button, the data is edited. If there is no problem with the data currently registered in the assignee allocation setting master 106a, it remains as it is.

[0097] (3) When the allocation execution button is pressed, a delay assignee allocation process as described in detail below is executed, and the assignee allocation results (number of allocated cases per assignee, total call average time, total balance) are displayed in area 503, and the debtor allocation results (debtor classification, number of delay months, number of delay recovery times, number of delay debtors, number of unallocated cases) are displayed in area 504. It can be confirmed that the time (load) of each assignee is leveled by the total call average time (the above effect 3).

[0098] (4) When the registration button 606 is pressed, the allocation results are registered. Specifically, the overdue debtor data with the overdue assignee set is registered in the data table 106b, and the assignee allocation setting master 106a is updated with the content of the assignee allocation setting in area 502.

[0099] Referring to FIGS. 18 to 23, a specific example of the overdue assignee allocation process in (3) above will be described. [[ID=十六]] [[ID=十七]]

[0100] [[ID=十八]] [[ID=十九]](1. When setting the number of delay months, the number of delay recoveries, and the debtor classification)[[ID=二十]] [[ID=二十一]]Referring to FIGS. 18 to 20, the case where the number of delay months, the number of delay recoveries, and the debtor classification are set in the assignee allocation setting will be described. [[ID=二十二]] [[ID=二十三]]

[0101] [[ID=二十四]] [[ID=二十五]](1) Determine the assignee for each debtor classification, number of delay months, and number of delay recoveries. [[ID=二十六]] [[ID=二十七]]FIG. 18(A) is a diagram showing an example of the assignee allocation setting. FIG. 18(B) is a diagram showing an example of the first data (intermediate data) expanded in the work table 106c. [[ID=二十八]] [[ID=二十九]]

[0102] [[ID=三十]] In Figure 18(A), the assignee assignment settings allow for assignments tailored to the assignee's level and skills by setting the debtor category, number of months in arrears, and number of collection attempts, according to the assignee's level and skills (Effect 2 above). For example, debtors who are difficult to collect from can be assigned a debtor category of "1: Caution." For example, an assignee with low proficiency can be assigned a debtor category of "0: Normal," preventing them from being assigned debtors who are deemed difficult to collect from.

[0103] As shown in Figure 18(B), based on the assigned personnel settings, the combinations of debtor category, number of months in arrears, and number of collections for arrears are expanded, and the first data is generated (expanded) by assigning the corresponding personnel to each expanded combination.

[0104] (2) Sort the delinquent debtors in the following order: debtor category, number of months in arrears, number of times in arrears have been collected, and average call duration, and assign a person in charge accordingly.

[0105] Figures 19(A) and (B) show examples of the second data (intermediate data) and third data (intermediate data) to be expanded in worktable 106c.

[0106] As shown in Figure 19(A), the debtor number, months of delinquency, number of collections, and average call time data from the delinquent debtor data, along with the debtor category data from the debtor data, are expanded into worktable 106c as the second data. Then, as shown in Figure 19(B), the average call time is sorted in ascending order for each debtor category, months of delinquency, and number of collections. For each debtor category, months of delinquency, and number of collections, the person in charge who matches the debtor category, months of delinquency, and number of collections in the first data is assigned in order to generate the third data. This makes it possible to equalize the collection time for each person in charge (effect 3 above).

[0107] For example, in the example shown in Figure 19(B), the first row contains debtor number "100", debtor category "0: normal", number of months in arrears "1", and number of times in arrears collected "0". Since no matching person has been assigned in the first data, it is left unassigned.

[0108] The second row contains debtor number "100", debtor category "0: normal", number of months in arrears "1", and number of times in arrears collection "1", and assigns the matching person in charge "AAA: person in charge 1" to it.

[0109] (3) Next, the delinquent debtor data is updated based on the third data. Figure 20 shows an example of the updated delinquent debtor data. In addition, based on the delinquent debtor data, the assignment results for area 503 and the debtor assignment results for area 504 are displayed on the delinquent debtor assignment processing screen in Figure 16.

[0110] (2. When the number of months in arrears and debtor category are set (number of arrears collections not set)) Refer to Figures 21 to 23 to explain the case where the number of months of delinquency and debtor classification are set in the assignee settings (the number of delinquent collections is not set).

[0111] (1) A person in charge will be assigned to each debtor category and the number of months of arrears. Figure 21(A) shows an example of assigning personnel. Figure 21(B) shows an example of the first data to be expanded into worktable 106c.

[0112] In Figure 21(A), the number of overdue collections is not set in the 담당자 (person in charge) assignment settings. The number of overdue collections is not a required item, but an optional one. By setting the debtor category and the number of months overdue in the 담당자 assignment settings according to the level and skills of the person in charge, it becomes possible to assign debtors according to the level and skills of the person in charge (Effect 2 above). For example, a debtor who is difficult to collect from can be set to debtor category "1: Caution". For example, a person in charge with a low level of proficiency can set the debtor category to "0: Normal", so that they will not be assigned debtors who are judged to be difficult to collect from.

[0113] As shown in Figure 21(B), based on the assigned personnel settings, the combinations of debtor classification and the number of months in arrears are expanded, and a first data set is generated (expanded) by assigning a personnel (personnel code) to each expanded combination.

[0114] (2) Sort the delinquent debtors by debtor category, number of months in arrears, and average call duration, and assign a person in charge accordingly.

[0115] Figures 22(A) and (B) show examples of the second data (intermediate data) and third data (intermediate data) to be expanded into worktable 106c. As shown in Figure 22(A), the debtor number, number of months in arrears, and average call time data from the delinquent debtor data, along with the debtor category data from the debtor data, are expanded into worktable 106c as the second data. Then, as shown in Figure 22(B), the average call time is sorted in ascending order for each debtor category and number of months in arrears, and the personnel who match the debtor category and number of months in arrears in the first data are sequentially assigned to each debtor category and number of months in arrears to generate the third data. This makes it possible to equalize the time spent on reminders for each person in charge (effect 3 above).

[0116] For example, in the example shown in Figure 22(B), the first row contains debtor number "100", debtor category "0: normal", and number of months in arrears "1", and assigns the matching person in charge "AAA: person in charge 1" to the first row of the first data.

[0117] The second row contains debtor number "100", debtor category "0: normal", number of months in arrears "1", and number of times collection is overdue "1". The person in charge "BBB: person in charge 2" is assigned to match the second row of the first data.

[0118] (3) Next, update the delinquent debtor data based on the third data. Figure 23 shows an example of the updated delinquent debtor data.

[0119] As described above, according to this embodiment, the delinquent debtor acquisition unit 102b acquires delinquent debtor data, including the debtor, the number of months of delinquency, and the average time spent on reminder calls for debts with a scheduled collection date prior to the reference date. The person in charge of reminders sets a debtor classification indicating the debtor's level of attention and the number of months of delinquency as a person in charge assignment setting for each person in charge, and assigns a person in charge to remind each debtor based on the delinquent debtor data by referring to the person in charge assignment setting and the debtor data. This reduces the workload when assigning persons in charge of reminding debtors about delinquent debts and makes it possible to equalize the workload of reminding debtors about delinquent debts while matching the level of each person in charge.

[0120] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving operational efficiency and promoting appropriate management decisions within companies, thereby enabling contributions to SDGs Goals 8 and 9.

[0121] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and digital processes, thereby contributing to SDGs Goals 12, 13, and 15.

[0122] Furthermore, this embodiment can contribute to strengthening control and governance, thereby enabling contributions to SDG Goal 16.

[0123] [5. Other Embodiments] In addition to the embodiments described above, the present invention may be implemented in various different embodiments within the scope of the technical idea described in the claims.

[0124] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods.

[0125] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registration data and search conditions for each process, screen examples, and database configuration shown in this specification and in the drawings may be changed at will unless otherwise specified.

[0126] Furthermore, with respect to the delinquency collection support device 100, each component shown in the diagram is a functional concept and does not necessarily need to be physically configured as shown.

[0127] For example, the processing functions of the overdue payment reminder support device 100, particularly those performed in the control unit, may be implemented in whole or in part by a CPU and a program interpreted and executed by the CPU, or they may be implemented as wired logic hardware. The program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processing described in this embodiment, and is mechanically read by the overdue payment reminder support device 100 as needed. That is, a storage unit such as ROM or HDD (Hard Disk Drive) contains a computer program that works in cooperation with the OS to give instructions to the CPU and perform various processing tasks. This computer program is executed by being loaded into RAM and works in cooperation with the CPU to constitute the control unit.

[0128] Furthermore, this computer program may be stored on an application program server connected to the delinquency collection support device 100 via any network, and it is possible to download all or part of it as needed.

[0129] Furthermore, the program for executing the processing described in this embodiment may be stored on a non-temporary computer-readable recording medium, or it may be configured as a program product. Here, "recording medium" includes any "portable physical medium" such as memory cards, USB (Universal Serial Bus) memory, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROMs, EPROMs (Erasable Programmable Read Only Memory), EEPROMs (Registered Trademark) (Electrically Erasable and Programmable Read Only Memory), CD-ROMs (Compact Disk Read Only Memory), MOs (Magneto-Optical disks), DVDs (Digital Versatile Disks), and Blu-ray (Registered Trademark) Discs.

[0130] Furthermore, "program" refers to a data processing method described in any language or writing method, regardless of its format, such as source code or binary code. Note that "program" is not necessarily limited to a single, monolithic structure; it also includes distributed structures consisting of multiple modules or libraries, and those that work in cooperation with other programs, such as an operating system, to achieve their functions. Regarding the specific configuration and reading procedures for reading the recording medium in each device shown in the embodiments, as well as the installation procedures after reading, well-known configurations and procedures can be used.

[0131] The various databases stored in the memory unit are memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and website provision.

[0132] Furthermore, the delinquency reminder support device 100 may be configured as an information processing device such as a known personal computer or workstation, or as an information processing device to which any peripheral devices are connected. Alternatively, the delinquency reminder support device 100 may be implemented by installing software (including programs or data, etc.) on the device that enables the processing described in this embodiment.

[0133] Furthermore, the specific forms of distribution and integration of the devices are not limited to those shown in the figures, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit according to various additions or functional loads. In other words, the embodiments described above may be implemented in any combination, or the embodiments may be implemented selectively. [Explanation of Symbols]

[0134] 100 Delinquency Collection Support Device 102 Control Unit 102a Data Registration Section 102b Delinquent Debtor Acquisition Department 102c Assignment Department 102d Screen display control unit 104 Communication Interface Section 106 Storage section 106a Assignment Settings Master 106b Data Table 106c Work Table 108 Input / Output Interface Section 112 Input device 114 Output device 300 Networks

Claims

1. A delinquency collection support device comprising a control unit and a storage unit, for assigning personnel to contact debtors of debts that are in arrears in collection, The aforementioned storage unit includes: The system stores debtor data, including the debtor and a debtor classification indicating the level of attention required by the debtor. The control unit, A means for acquiring delinquent debtor data, including the debtor, the number of months in arrears, and the average duration of calls made to reminders for delinquent debts, for debts with a scheduled collection date prior to the reference date, and A means for assigning debt collection agents to each debtor, which sets a debtor classification indicating the debtor's level of attention and the number of months in arrears as agent assignment settings for each agent responsible for collection, and assigns an agent to each debtor based on the arrears debtor data, by referring to the agent assignment settings and the debtor data, Equipped with, The aforementioned means for assigning delinquent payment personnel is: Based on the aforementioned assignment settings for personnel, the combinations of debtor classification and the number of months in arrears are expanded, and for each expanded combination, the corresponding personnel are assigned to generate the first data. A debt collection support device characterized by, after expanding the data of debtor, number of months in arrears, and average call time for debt collection from the delinquent debtor data and the debtor category data from the debtor data, sorting the average call time for debt collection in ascending order for each debtor category and number of months in arrears, and assigning, in order, personnel whose debtor category and number of months in arrears match in the first data for each debtor category and number of months in arrears.

2. The aforementioned delinquent debtor data includes the number of times the delinquent debt has been collected for each debtor, The delinquency collection support device according to claim 1, characterized in that the aforementioned personnel assignment setting includes the number of delinquency collections for each debtor.

3. The aforementioned means for assigning delinquent payment personnel is: Based on the aforementioned assignment settings for personnel, the combinations of the debtor category, number of months in arrears, and number of collections for arrears are expanded, and for each expanded combination, the corresponding personnel are assigned to generate the first data. The delinquent debt collection support device according to claim 2, characterized in that, after expanding the data of debtor, number of months in arrears, number of collections for delinquent debts, and average call time for delinquent debt collection from the delinquent debtor data and the debtor category data from the debtor data, the average call time for delinquent debt collection is sorted in ascending order for each debtor category, number of months in arrears, and number of collections for delinquent debts, and for each debtor category, number of months in arrears, and number of collections for delinquent debts, the person in charge whose debtor category, number of months in arrears, and number of collections for delinquent debts match in the first data in order.

4. The aforementioned storage unit includes: A master data set is stored that associates the debtor's level of attention, the number of months in arrears, and the number of times overdue collections have been made with each person in charge of debt collection. The delinquency officer assignment means reads the settings of the master as the initial value of the officer assignment setting, and sets the officer assignment setting by adding, deleting, or editing the initial value, as described in claim 1.

5. The delinquency collection support device according to any one of claims 1 to 4, characterized in that the aforementioned claim includes a financial claim.

6. A method for supporting overdue payment reminders, which is performed by an information processing device equipped with a control unit and a storage unit, The aforementioned storage unit includes: The system stores debtor data, including the debtor and a debtor classification indicating the level of attention required by the debtor. The control unit is executed as follows: The process involves acquiring delinquent debtor data, including the debtor, the number of months in arrears, and the average duration of calls made to reminders, for debts with a scheduled collection date prior to the reference date. For each person responsible for collection efforts, a debtor classification indicating the debtor's level of attention and the number of months in arrears are set as a person-assigned setting, and a person-assigned setting and the debtor data are referenced to assign a person to each debtor to collect the debt based on the delinquent debtor data, in a delinquent debtor assignment process. Includes, In the aforementioned process of assigning overdue payment personnel, Based on the aforementioned assignment settings for personnel, the combinations of debtor classification and the number of months in arrears are expanded, and for each expanded combination, the corresponding personnel are assigned to generate the first data. A method for supporting late payment collection, characterized by expanding the data of debtor, number of months in arrears, and average call time for late payment collection from the aforementioned delinquent debtor data, and the debtor category data from the debtor data, then sorting the average call time for late payment collection in ascending order for each debtor category and number of months in arrears, and then assigning, in order, personnel whose debtor category and number of months in arrears match in the first data.

7. A delinquency reminder support program to be executed by an information processing device equipped with a control unit and a storage unit, The aforementioned storage unit includes: The system stores debtor data, including the debtor and a debtor classification indicating the level of attention required by the debtor. In the control unit, The process involves acquiring delinquent debtor data, including the debtor, the number of months in arrears, and the average duration of calls made to reminders, for debts with a scheduled collection date prior to the reference date. For each person responsible for collection efforts, a debtor classification indicating the debtor's level of attention and the number of months in arrears are set as a person-assigned setting, and a person-assigned setting and the debtor data are referenced to assign a person to each debtor to collect the debt based on the delinquent debtor data, in a delinquent debtor assignment process. This is a program to support overdue payment collection efforts, In the aforementioned process of assigning overdue payment personnel, Based on the aforementioned assignment settings for personnel, the combinations of debtor classification and the number of months in arrears are expanded, and for each expanded combination, the corresponding personnel are assigned to generate the first data. A debt collection support program characterized by, after expanding the data of debtor, number of months in arrears, and average call time for debt collection from the aforementioned delinquent debtor data, and the debtor category data from the debtor data, sorting the average call time for debt collection in ascending order for each debtor category and number of months in arrears, and assigning, in order, personnel whose debtor category and number of months in arrears match in the first data for each debtor category and number of months in arrears.

Images