CN116862656A

CN116862656A - Debt data processing method, device, equipment and storage medium

Info

Publication number: CN116862656A
Application number: CN202310863328.5A
Authority: CN
Inventors: 翁越; 张益烽; 王思懿
Original assignee: Ningbo Tongshang Bank Co ltd
Current assignee: Ningbo Tongshang Bank Co ltd
Priority date: 2023-07-13
Filing date: 2023-07-13
Publication date: 2023-10-10

Abstract

The application discloses a debt data processing method, device, equipment and storage medium. Performed by a liability management system, the method comprising: if a service application sent by a supply chain center is detected, determining debt data related to a target enterprise; the debt data comprises order data, invoice data, financial report data and credit investigation data; based on preset debt rules, credit approval and account approval are carried out on the debt business by interacting with a middle station of a supply chain, and account approval is carried out when the credit approval and the account approval pass; based on preset monitoring rules, the latest debt data of the target enterprise are periodically acquired, and correspondingly early warning and/or displaying are carried out. According to the technical scheme, the relevant debt data of the enterprises can be comprehensively analyzed, so that the enterprises can make business transactions with the approved target enterprises, the relevant data are monitored and early warning is timely executed, and powerful risk decision support is provided for the bank supply chain business.

Description

Debt data processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of big data, and in particular, to a method, an apparatus, a device, and a storage medium for processing debt data.

Background

With the development of financial science and technology, especially the continuous development of internet science and technology finance, the demand of finance field to data high-efficient management is also higher and higher, and in supply chain business, the daily operation monitoring of enterprise's actual production sales link is comparatively difficult for the bank, and traditional single manual audit mechanism has unable to satisfy the online business volume of handling with increasing, and the manual verification has certain operational risk.

Therefore, how to comprehensively analyze the debt data related to enterprises in the face of complex and changeable supply chain business processes, so as to conduct business exchange with approved target enterprises, monitor related data and timely execute early warning, and provide powerful risk decision support for bank supply chain business is a problem to be solved urgently at present.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for processing debt data, which can comprehensively analyze the debt data related to enterprises, so that the enterprises can conduct business communication with target enterprises passing approval, monitor the related data and timely execute early warning, and provide powerful risk decision support for bank supply chain business.

According to an aspect of the present application, there is provided a debt data processing method performed by a debt management system, comprising:

if a service application sent by a supply chain center is detected, determining debt data related to a target enterprise; the debt data comprises order data, invoice data, financial report data and credit investigation data;

based on preset debt rules, credit approval and account approval are carried out on the debt business by interacting with a middle station of a supply chain, and account approval is carried out when the credit approval and the account approval pass;

based on preset monitoring rules, the latest debt data of the target enterprise are periodically acquired, and correspondingly early warning and/or displaying are carried out.

According to another aspect of the present application, there is provided a debt data processing apparatus comprising:

the determining module is used for determining debt data related to a target enterprise if a service application sent by a supply chain center is detected; the debt data comprises order data, invoice data, financial report data and credit investigation data;

the payment module is used for carrying out credit approval and payment approval on the debt business by interacting with a middle station of a supply chain based on preset debt rules, and carrying out payment when the credit approval and the payment approval pass;

and the early warning module is used for periodically acquiring the latest debt data of the target enterprise based on a preset monitoring rule and correspondingly early warning and/or displaying.

According to another aspect of the present application, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the liability data processing method according to any of the embodiments of the present application.

According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the debt data processing method according to any one of the embodiments of the present application.

According to the technical scheme, if the debt management system detects a service application sent by a middle station of a supply chain, debt data related to a target enterprise are determined; based on preset debt rules, credit approval and account approval are carried out on the debt business by interacting with a middle station of a supply chain, and account approval is carried out when the credit approval and the account approval pass; based on preset monitoring rules, the latest debt data of the target enterprise are periodically acquired, and correspondingly early warning and/or displaying are carried out. By adopting the way, the debt management system can automatically and comprehensively analyze the debt data related to enterprises, so that the enterprises can conduct business with the target enterprises passing approval, the related data can be monitored to timely execute early warning, and powerful risk decision support is provided for the bank supply chain business.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for processing liability data according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a debt data system according to a second embodiment of the present application;

fig. 3 is a block diagram of a debt data processing apparatus according to a third embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," "target," "candidate," "alternative," and the like in the description and claims of the application and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, in the technical scheme of the application, the acquisition, storage, use, processing and the like of the data all conform to the relevant regulations of national laws and regulations.

Example 1

FIG. 1 is a flow chart of a method for processing liability data according to an embodiment of the present application; the embodiment is applicable to the situation that a bank interacts with a middle station of a supply chain to realize processing and monitoring after loan of a target enterprise, the method can be executed by a debt data processing device, the debt data processing device can be realized in the form of hardware and/or software, and the debt data processing device can be configured in electronic equipment and executed by a debt management system. As shown in fig. 1, the debt data processing method includes:

s101, if a service application sent by a supply chain center is detected, determining debt data related to a target enterprise.

The supply chain center can be a supply chain management platform based on internet technology preset in a bank, and can digitally manage and control the whole life cycle of the supply chain. The business application refers to a request sent by a station in the supply chain to the debt management system to manage the debt data of the target enterprise. The target enterprise refers to an enterprise for managing debt data of a target. The debt data refers to related data related to a lending business between a target enterprise and a bank, and the debt data can comprise order data, invoice data, financial report data and credit investigation data. The order data related to different business purchase/sales orders are different, and the order data can comprise material information, unit price, total price and the like of purchase/sales, for example. The financial reporting data may include asset liabilities, profit and loss, cash flow, and related financial index information for the target business.

Optionally, the supply chain center may enter customer information and financial information of the target enterprise when detecting a loan request issued by the target enterprise, perform project setup, and issue a service application to the debt management system to instruct the debt management system to determine debt data and establish a debt rule pool.

Optionally, determining the liability data associated with the target business includes: interacting with a management system of a target enterprise to acquire supply chain logistics information related to the enterprise or borrower so as to determine debt data related to the target enterprise; and/or after acquiring the client manager offline collected corporate financing information, importing the debt data of the debt management system to determine debt data related to the target corporate. The debt management system of the bank can be in butt joint with the management system of an external core enterprise through an external access channel so as to acquire relevant debt data.

Optionally, after acquiring the financing information of the enterprise collected under the line of the customer manager, the debt data of the debt management system is imported to determine the debt data related to the target enterprise, including: after acquiring enterprise financing information collected under a client manager line, importing an invoice picture of a debt management system; character recognition is carried out on the invoice picture by adopting an OCR tool, keywords in the invoice title are determined, the invoice template is automatically matched through the keywords, and invoice elements are recognized based on the invoice template; invoice data associated with the target business is determined based on the invoice elements. Wherein OCR (optical character recognition) is a preset word recognition tool. The invoice element may include at least one of: buyer information, seller information, goods information, price tax totals, drawer, payee, review and date of the drawer.

Alternatively, the liability management system may query credit reports of individuals and businesses by interfacing with an in-line second generation credit system to determine credit data, i.e., to determine liability data associated with the target business.

S102, based on preset debt rules, credit approval and payment approval are carried out on the debt business through interaction with a platform in a supply chain, and payment is carried out when the credit approval and payment approval pass.

The debt rule refers to preset debt data or rules which are required to be met by a target enterprise. The liability rules may include: credit rules, debit rules, and post-credit rules; the credit rule may be, for example, whether the buyer and the seller are in a white list, the debit rule may be, for example, whether the debt data meets the financing proportion requirement, and the post-credit rule may be, for example, whether the amount of the redemption product exceeds the amount, whether the expiration date or the redemption product exceeds the redemption product period, whether the payment exceeds the payment period, and the like.

For example, the financing proportion requirement may be an order financing item placing amount < = order amount (financing proportion).

Optionally, based on a preset debt rule, credit approval is performed on the debt service by interacting with a middle station in the supply chain, including: if a credit application initiated by a supply chain center is detected, determining a debt rule corresponding to the debt data from a debt rule pool based on an embedded rule engine; and determining index items to be checked of the debt data according to the debt rules, and performing initial check and review on whether the index values of the index items of the debt data meet the conditions of the corresponding debt rules by interacting with a middle station of a supply chain so as to complete credit approval. The credit application may be an application initiated by a client manager from a supply chain center for credit to the debt data of the target enterprise, and the credit application may be a group credit, an indirect credit or a single credit application, for example. The embedded rules engine may be, for example, a Drools rules engine (an open business rules engine that is easy to access enterprise policies, easy to adjust, and easy to manage).

Optionally, the manners of conducting initial review and review on the target enterprise according to the financial report data may be: and (3) according to the financial report data, carrying out balance verification of the assets and liabilities, analyzing asset conditions of the target enterprises, carrying out risk level and credit level evaluation of the target enterprises, generating a rating result, and if the rating result is within a preset safety level range, determining that primary review and review of the target enterprises pass.

Optionally, the manner of conducting initial review and review on the target enterprise according to the credit data and the financial report data may be: invoking a credit index platform through ESB (Enterprise Service Bus ), and evaluating the credit grade of the target enterprise according to the credit data; and evaluating the risk level of the target enterprise according to the financial accounting data, scoring different scoring items of the target enterprise according to the financial accounting data and the credit investigation data based on a preset scoring rule, adding the scores obtained by the scoring items, and mapping according to the interval where the total score is located to obtain the final rating result.

Optionally, on the basis of a preset debt rule, performing an account-out approval on the debt business by interacting with a station in the supply chain, including: if an account-out application initiated by the supply chain center is detected, calling the supply chain center to perform account-out registration, and after the account-out registration is detected to be completed by the supply chain center, performing debt data rechecking according to the input debt data; according to preset debt rules, preliminarily checking whether the debt data meet the debt rules, and under the condition that the preliminary checking is passed, sequentially sending the debt data to a management unit responsible person, a paying operation post, a paying initial check post and a paying final check post for secondary checking, and if the secondary checking is passed, determining that the paying approval of the debt data is completed.

The posting application is an application in which a target enterprise initiates a money-consuming request through a supply chain center, requests a bank to posting money from an account of a user, and pays funds in a specified manner and at a specified destination.

For example, the payment data and the financing information of the supply chain such as the order contract can be collected and then calculated according to a rule formula, and finally, the rule calculation result is obtained, and whether the debt data meets the debt rule is checked, namely, whether the debt data meets the debt rule is initially checked according to the preset debt rule.

And S103, periodically acquiring the latest debt data of the target enterprise based on a preset monitoring rule, and correspondingly early warning and/or displaying.

Optionally, based on a preset monitoring rule, the latest debt data of the target enterprise is periodically acquired for early warning and/or display, including: based on a preset monitoring rule, periodically acquiring data such as an invoice, a payment bill, accounts receivable and the like of a target enterprise, and determining a loan destination, a shipping condition corresponding to an order, a logistics condition and a warehouse goods storage condition so as to analyze and determine an operation condition of the target enterprise after obtaining the loan; and carrying out early warning and/or displaying the operation condition of the target enterprise according to the monitoring result of the operation condition.

Optionally, if the repayment information of the target enterprise sent by the related personnel is detected, corresponding repayment invoice data is obtained, and the repayment invoice data is associated with the payment order; checking whether the repayment invoice data of the target enterprise meets the verification and marketing requirements according to the repayment invoice data and the related data of the payment order; if yes, the accounts receivable verification and invoice verification of the target enterprise are carried out.

By way of example, the flow of invoice verification may be as follows: the debt management system receives invoice pictures collected under the line of a client manager, and the invoice pictures are recognized as structured data through an OCR tool to be stored locally, and meanwhile, the structured data are automatically uploaded to an image system to be stored. Invoice data needs to be associated with the posting or order data for verification, and whether verification requirements are met or not can be checked through rules during submission. And after meeting the requirements, submitting the flow to the debt management post for approval, and ending the order verification flow when the approval passes.

For example, the flow of accounts receivable verification may be as follows: if the target enterprise repayment is detected, selecting a corresponding account-out order, inquiring a core account running line of the target enterprise, and associating the account-out order with the target enterprise running line; and verifying and approving the used invoice, submitting the flow to a debt management post after the rule verification (if the receivables are the same as the real receivables and considered to pass the rule verification) is met, and ending the business after the approval passes.

Example two

Fig. 2 is a schematic structural diagram of a debt data system according to a second embodiment of the present application; the embodiment provides a preferred example of implementing debt data management by using the debt data system to interact with the system modules such as the supply chain center station and the like on the basis of the embodiment. As shown in fig. 2, the debt data system of a bank provided by the present application may include: front-end Internet user side and office portal, back-end credit management, debt rule management, debt data management, flow management, early warning management and system management.

The credit management module may include management of customer information, contract information, credit information, and billing information, among others. The liability rules management module may include units of liability projects, liability schemes, liability factors, rule configurations, policy assignments, change approvals, execution logs, and statistical analysis. The liability data management module may include units for funds flow management, statement design, logistics management, statistical analysis, data access, data synchronization, OCR recognition, and impact storage. The process management module may include units such as process preview, my agent, my application, my sponsored, credit approval, audit of posting, post-credit monitoring, and process monitoring. The early warning management module can comprise early warning rules, signal generation, early warning details, early warning processing, post-credit detection, report design, statistical analysis, data synchronization and other units. The system management module may include units such as user management, menu management, rights management, organization management, role management, process monitoring, log management, and performance monitoring.

Alternatively, the bank may interact with the bond management system using an in-line access system for business processing, and specific in-line access systems or tools may include a supply chain center, an OCR recognition tool, a second generation credit system, an image platform, an invoice verification system, and the like.

Optionally, the liability management system may interact with the core corporation using an external access channel to obtain liability data of the corporation for business processing.

It should be noted that, the whole debt management system, the in-line access system and the out-line access system all perform data interaction based on the big data access mode.

The debt management system provided by the application can provide intelligent management support for the whole life cycle of the supply chain business, and greatly improves the risk management capability of commercial banks on the supply chain business.

Example III

Fig. 3 is a block diagram of a debt data processing apparatus according to a third embodiment of the present application; the present embodiment may be applied to a situation that a bank interacts with a central office of a supply chain to implement processing and post-loan monitoring on a target enterprise, where the debt data processing apparatus may be implemented in hardware and/or software and configured in a device having a debt data processing function, and executed by a debt management system, as shown in fig. 3, where the apparatus specifically includes:

a determining module 301, configured to determine debt data related to a target enterprise if a service application sent by a station in a supply chain is detected; the debt data comprises order data, invoice data, financial report data and credit investigation data;

the posting module 302 is configured to perform credit approval and posting approval on the debt service by interacting with a middle station in the supply chain based on a preset debt rule, and perform posting when the credit approval and posting approval pass;

and the early warning module 303 is configured to periodically acquire the latest debt data of the target enterprise based on a preset monitoring rule, and perform early warning and/or display accordingly.

Further, the determining module 301 is specifically configured to:

interacting with a management system of a target enterprise to acquire supply chain logistics information related to the enterprise or borrower so as to determine debt data related to the target enterprise; and/or

After collecting the financing information of the business under the line of the customer manager, the debt data of the debt management system is imported to determine the debt data related to the target business.

Further, the determining module 301 is further configured to:

after acquiring enterprise financing information collected under a client manager line, importing an invoice picture of a debt management system;

character recognition is carried out on the invoice picture by adopting an OCR tool, keywords in the invoice title are determined, the invoice template is automatically matched through the keywords, and invoice elements are recognized based on the invoice template;

invoice data associated with the target business is determined based on the invoice elements.

Further, the accounting module 302 is specifically configured to:

if a credit application initiated by a supply chain center is detected, determining a debt rule corresponding to the debt data from a debt rule pool based on an embedded rule engine; the debt rule includes: credit rules, debit rules, and post-credit rules;

and determining index items to be checked of the debt data according to the debt rules, and performing initial check and review on whether the index values of the index items of the debt data meet the conditions of the corresponding debt rules by interacting with a middle station of a supply chain so as to complete credit approval.

Further, the accounting module 302 is further configured to:

if an account-out application initiated by the supply chain center is detected, calling the supply chain center to perform account-out registration, and after the account-out registration is detected to be completed by the supply chain center, performing debt data rechecking according to the input debt data;

according to preset debt rules, preliminarily checking whether the debt data meet the debt rules, and under the condition that the preliminary checking is passed, sequentially sending the debt data to a management unit responsible person, a paying operation post, a paying initial check post and a paying final check post for secondary checking, and if the secondary checking is passed, determining that the paying approval of the debt data is completed.

Further, the early warning module 303 is specifically configured to:

based on a preset monitoring rule, periodically acquiring data such as an invoice, a payment bill, accounts receivable and the like of a target enterprise, and determining a loan destination, a shipping condition corresponding to an order, a logistics condition and a warehouse goods storage condition so as to analyze and determine an operation condition of the target enterprise after obtaining the loan;

and carrying out early warning and/or displaying the operation condition of the target enterprise according to the monitoring result of the operation condition.

Further, the device is also used for:

if the repayment information of the target enterprise sent by the related personnel is detected, corresponding repayment invoice data are obtained, and the repayment invoice data are associated with the payment order;

checking whether the repayment invoice data of the target enterprise meets the verification and marketing requirements according to the repayment invoice data and the related data of the payment order;

if yes, the accounts receivable verification and invoice verification of the target enterprise are carried out.

Example IV

Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application. Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as debt data processing methods.

In some embodiments, the liability data processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the debt data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the debt data processing method in any other suitable way (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.

The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims

1. A method of liability data processing, performed by a liability management system, comprising:

2. The method of claim 1, wherein determining liability data associated with the target business comprises:

3. The method of claim 2, wherein the step of importing the debt data of the debt management system after acquiring the customer manager offline collected corporate financing information to determine the debt data associated with the target corporation comprises:

4. The method of claim 1, wherein credit approval of the debt service by interacting with the supply chain center based on the preset debt rules comprises:

5. The method of claim 1, wherein the posting approval of the debt service by interacting with the supply chain center based on the preset debt rules comprises:

6. The method of claim 1, wherein periodically acquiring the latest debt data of the target business for pre-warning and/or display based on preset monitoring rules, comprises:

7. The method as recited in claim 1, further comprising:

8. A debt data processing apparatus, comprising:

9. An electronic device, the electronic device comprising:

at least one processor; and

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the liability data processing method of any of claims 1-7.

10. A computer readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the liability data processing method according to any of claims 1-7 when executed.