Signal identification method and device based on debt item registration data, equipment and medium

Abstract

The invention provides a signal identification method and device based on debt item registration data, equipment and a medium, and relates to the field of data processing. According to the method, the debt item registration data of the target customer is acquired, and the movable property registration information and the analysis of the debt item mean value curve are combined, so that the financial condition of the customer and the mobility risk of the debt item are evaluated more comprehensively. The information is deeply analyzed according to the preset data identification conditions, potential risk points can be identified more accurately, and service signals or risk signals are generated, so that the accuracy of data identification is improved, the business opportunity discovery coverage range is expanded, and the service efficiency is improved; the risk prevention and control capability is improved, and the safety of financial transactions is enhanced.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a signal recognition method, device, equipment and medium based on debt registration data. Background Art

[0002] China Securities Depository and Clearing Corporation Limited is a unified registration and publicity system for movable property financing established by the Credit Information Center of the People's Bank of China. It is an electronic system that provides unified registration and inquiry services for movable property and rights guarantees to market entities through the Internet.

[0003] The use of Zhongdeng.com is mainly to inquire whether the debts of customers who transfer debts in factoring business have been mortgaged, so as to ensure the uniqueness of the debt transfer and ensure that the transfer of ownership of the debt is real and effective. The use channels are relatively single, and the business signals and other risk warning values ​​provided by the public data of Zhongdeng.com in the business are not fully utilized.

[0004] In the financial field, when a customer requests a withdrawal, he or she needs to transfer the debt to the provider. To ensure that the debt is not transferred a second time, the provider will check the registration status of the debt on the China Securities Depository and Clearing Corporation Limited website. If it has not been mortgaged or transferred, the provider's business personnel will register it to avoid secondary transfer or mortgage of the debt, which serves as an important guarantee for the recovery of factoring financing funds.

[0005] However, the value of debt fluctuates with changes in market conditions, and existing technologies may not be able to update these changes in real time, resulting in inaccurate assessment results; the period for customers to register debts on the China Securities Depository and Clearing Corporation Limited website may be long, during which time the actual value of the debt may have changed, but the China Securities Depository and Clearing Corporation Limited system fails to reflect such changes in a timely manner, resulting in inaccurate assessment of the actual debt mortgage value of customers requesting withdrawals by the lender, which increases financing risks.

[0006] Therefore, how to improve the data recognition accuracy of debt registration data has become a technical problem that needs to be solved urgently. Summary of the invention

[0007] The present application provides a signal recognition method, device, equipment and medium based on debt registration data, aiming to improve the data recognition accuracy of debt registration data.

[0008] In a first aspect, the present application provides a signal recognition method based on debt registration data, the signal recognition method based on debt registration data comprising the following steps:

[0009] Obtain the target customer's debt registration data;

[0010] Based on the target customer's debt registration data, obtain the target customer's movable property registration information, and calculate the target customer's debt mean curve within a preset time range;

[0011] When the movable property registration information and / or the debt item average curve meets the business identification condition, generating a business signal;

[0012] When the dynamic registration information and / or the debt item mean curve meets the early warning identification conditions, a risk signal is generated.

[0013] In a second aspect, the present application further provides a signal recognition device based on debt registration data, the signal recognition device based on debt registration data comprising:

[0014] Data acquisition module, used to obtain the target customer's debt registration data;

[0015] A data statistics module, for obtaining movable property registration information of the target customer based on the target customer's debt registration data, and calculating a debt mean curve of the target customer within a preset time range;

[0016] A business identification module, used to generate a business signal when the movable property registration information and / or the debt item mean value curve meets the business identification conditions;

[0017] The risk identification module is used to generate a risk signal when the dynamic registration information and / or the debt item mean curve meets the early warning identification conditions.

[0018] In a third aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the signal recognition method based on debt registration data as described above are implemented.

[0019] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the signal recognition method based on debt registration data as described above are implemented.

[0020] The present application provides a signal recognition method, device, computer equipment and storage medium based on debt registration data. The method of the present application includes obtaining the debt registration data of the target customer; based on the debt registration data of the target customer, obtaining the movable property registration information of the target customer, and calculating the debt mean curve of the target customer within a preset time range; when the movable property registration information and / or the debt mean curve meet the business identification conditions, a business signal is generated; when the dynamic registration information and / or the debt mean curve meet the early warning identification conditions, a risk signal is generated. Through the above method, the present application obtains the debt registration data of the target customer, and combines the movable property registration information and the debt mean curve analysis to more comprehensively evaluate the financial status of the customer and the liquidity risk of the debt. Through the preset data recognition conditions, in-depth analysis of this information can more accurately identify potential risk points, thereby generating business signals or risk signals, improving the accuracy of data recognition, expanding the coverage of business opportunity discovery, and improving business service efficiency; improving risk prevention and control capabilities, and enhancing the security of financial transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flowchart of a first embodiment of a signal recognition method based on debt registration data provided by the present application;

[0023] Figure 2 A flow chart of a second embodiment of a signal recognition method based on debt registration data provided by the present application;

[0024] Figure 3 It is a structural schematic diagram of a first embodiment of a signal recognition device based on debt registration data provided by the present application;

[0025] Figure 4 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application.

[0026] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0029] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0030] Please refer to Figure 1 , Figure 1 A flowchart diagram of a first embodiment of a signal recognition method based on debt registration data provided in this application.

[0031] like Figure 1 As shown, the signal recognition method based on debt registration data includes steps S101 to S103.

[0032] S101. Obtaining debt registration data of target customers;

[0033] In one embodiment, while the business personnel search the Zhongdeng.com for the debts provided by the withdrawal customer, they connect to the Zhongdeng.com to automatically store data in the factoring system to form a basic table of debt registration data for the cooperative customers.

[0034] Generally, to obtain the target customer's debt registration data, it is usually necessary to go through the official channels or services provided by China Securities Depository and Clearing Corporation Limited (CSDC). CSDC is an important infrastructure of China's capital market, responsible for the establishment and management of securities accounts, the custody and transfer of securities, the registration of securities holders and the registration of rights and interests, and the clearing and delivery of securities and funds. CSDC's data specification interface is the standard for listed companies and other market participants to exchange data, including the mid-month / end-of-month major shareholder register data of listed companies, detailed data on dividend refunds, and detailed data on securities accounts for margin trading and refinancing.

[0035] In one embodiment, to obtain the target customer's debt registration data, the user's identity and query authority must first be clarified. The data provided by the China Securities Depository and Clearing Corporation usually involves sensitive information such as securities accounts, securities custody, transfers, and holder registers. Therefore, only legally authorized users can query relevant data.

[0036] S102, based on the target customer's debt registration data, obtaining the target customer's movable property registration information, and calculating the target customer's debt mean curve within a preset time range;

[0037] In one embodiment, the movable property registration information may include basic information of the guarantor and the secured creditor, a description of the collateral, a registration period, etc.

[0038] In one embodiment, the target customer's bond average curve within a preset time range can refer to the China Bond Yield Curve provided by the China Bond Information Network. The China Bond Yield Curve is compiled based on market data and reflects the yield changes of government bonds with different maturities. Users can query the yield curves of government bonds with different maturities through the China Bond Information Network to analyze the changing trend of the bond average.

[0039] In one embodiment, the preset time range can be set according to actual needs, such as one month, one quarter or one year.

[0040] S103: When the movable property registration information and / or the debt mean curve meets the business identification conditions, generate a business signal.

[0041] In one embodiment, the data identification conditions can be set according to the movable property registration information and the debt mean curve, such as setting the upper and lower thresholds of the trend change.

[0042] In one embodiment, according to data identification conditions, movable property registration information and / or debt mean curve are monitored and analyzed to identify risk signals or business signals.

[0043] Among them, the business signal indicates that the target customer is a potential customer, which can be pushed to the business department for follow-up to increase performance.

[0044] Risk signals indicate that the target customers are potential risk customers and can be pushed to the asset management department for follow-up and risk prevention and control.

[0045] Furthermore, when there is unrecorded first movable property information in the movable property registration information of the target customer, the current registration frequency of the first movable property information is queried; when the current registration frequency is less than a preset registration frequency threshold, the business signal is generated to mark the target customer as a potential customer according to the business signal.

[0046] In one embodiment, when processing the movable property registration information of the target customer, if there is unrecorded first movable property information, that is, the target customer is found to have registered the first movable property information in the Zhongdeng system, but the first movable property information is not recorded in the provider, then the provider can query the current registration frequency of the first movable property information. Generally, this can be done through the unified registration and publicity system for movable property financing, which is used to handle the registration and query business of movable property and rights guarantees. Users can independently register through the system and query the registration frequency of relevant movable property.

[0047] In one embodiment, if the current registration frequency of the first movable property information of the target customer is less than the preset registration frequency threshold, it can be considered that the target customer has little activity on the movable property corresponding to the first movable property information, or the movable property has not been fully utilized as collateral. At this time, a business signal can be generated and pushed to the business personnel of the business department to prompt the business personnel to pay attention to this situation.

[0048] In a specific embodiment, for a white-list customer with whom we have cooperated, if it is found that they have new first movable property information registered on the China Securities Depository and Clearing Corporation Limited that is not registered by the company, and the registration frequency does not exceed the normal range, it can be determined that they have the intention to raise funds and a business signal can be generated.

[0049] In one embodiment, the traffic signal may be a prompt or alert that flags users who may have underappreciated or underutilized mobile resources.

[0050] In one embodiment, based on the business signal, the business personnel can further analyze the target customer's financial status and movable property utilization to determine whether they are potential customers. Potential customers refer to those users who may have a need for movable property financing but have not yet performed such operations. In this way, financial institutions can more effectively identify and contact potential customers and provide them with corresponding financing services.

[0051] Furthermore, when the debt item average value curve shows an upward trend, the average value of debt items of at least two preset evaluation periods is calculated to obtain the first average value of debt items in the current evaluation period and the second average value of debt items in the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods; the increase in the average value of the first debt item relative to the second average value of debt items is calculated; and when the increase in the average value is greater than a first increase threshold, the business signal is generated.

[0052] In one embodiment, to calculate the increase in the average value of debt items, it is first necessary to determine the average value of the first debt item in the current evaluation period and the average value of the second debt item in the previous evaluation period. Generally, debt financial data in the debt registration data can be collected and analyzed to determine the average value of the debt in each evaluation period.

[0053] For example, the calculation formula for the average value increase is:

[0054]

[0055] In one embodiment, based on the principle that the larger the average value of the debt registration items, the more favorable the customer development trend, if the average value increase is greater than the preset first increase threshold, a business signal is generated. For example, if it is found that the average value of the debt items registered by the target customer on the China Deposit Network in the current evaluation cycle has increased by more than 50% (first increase threshold) compared with the average value of the previous evaluation cycle, a business signal is triggered.

[0056] In one embodiment, when a business signal is generated, the customer information and business source of the target customer are pushed to the account manager corresponding to the business end APP. The account manager follows up based on the prompted customer information and prompt reasons and converts them into business orders.

[0057] S104: When the dynamic registration information and / or the debt item average curve meets the early warning identification conditions, a risk signal is generated.

[0058] Furthermore, when the debt mean curve shows a downward trend, the first debt registration data of the target customer in the current evaluation cycle and the second debt registration data of the previous evaluation cycle are obtained; based on the first debt registration data and the second debt registration data, the first debt average value of each registered debt corresponding to the target customer in the current evaluation cycle and the second debt average value of each registered debt in the previous evaluation cycle are calculated; the decline in debt value of the first debt average value relative to the second debt average value is calculated; when the decline in debt value is greater than a preset decline threshold, the risk signal is generated.

[0059] In one embodiment, the first debt registration data of the target customer in the current evaluation cycle and the second debt registration data of the previous evaluation cycle are obtained according to the debt registration data. Each evaluation cycle can be set according to actual needs, such as one month, one quarter, half a year or one year, such as comparing the first debt registration data of the current month with the second debt registration data of the previous month.

[0060] In one embodiment, based on the collected first debt registration data and second debt registration data, the average value of the first debt corresponding to each registered debt of the target customer in the current evaluation cycle and the average value of the second debt in the previous evaluation cycle are calculated. Then, the decline in the debt value of the first debt average value relative to the second debt average value is calculated.

[0061] For example, the calculation formula for the decline in debt value may be:

[0062]

[0063] In one embodiment, based on the principle that the lower the unit price of movable property, the higher the risk, for target customers in cooperation, the average value of movable property registration items is calculated by adding up the monthly and quarterly registered debt amounts of the customer / the number of debts. If it is found that the average value of the registered debts in the latest month or the latest quarter has dropped to an integer multiple level compared with the previous time period, a risk signal is triggered.

[0064] Specifically, the calculated value decline is compared with the preset decline threshold. If the value decline is greater than the preset decline threshold, it is considered that there is a potential risk. After confirming that the value decline exceeds the threshold, a risk signal is generated, which will be sent to the risk management department for further analysis and evaluation by professionals to determine whether measures need to be taken, such as adjusting the credit limit, reorganizing assets or taking legal action.

[0065] Through the above methods, it is possible to effectively monitor changes in the value of debt items, discover potential risks in a timely manner, and take corresponding risk management measures. This not only improves the efficiency of risk management, but also enhances the ability of financial institutions to respond to market changes.

[0066] This embodiment analyzes the customer claim registration data of China Securities Depository and Clearing Corporation, sorts out risk signals and business signals, and follows up by the asset management department and the business department respectively, to help risk prevention and control and increase performance increment. The application of this technology has a significant effect on improving business operation efficiency, business opportunity identification and service efficiency, and risk management level, and has a great support for the overall operation of the company. It has increased the scope of use of public data on movable property registration, and used China Securities Depository and Clearing Corporation data in post-financing risk prevention and control and business signal discovery in addition to contracts; it has increased the coverage of business opportunity discovery and improved business service efficiency; it has increased the risk discovery dimension and efficiency, improved risk control efficiency, and improved asset management level.

[0067] This embodiment provides a signal recognition method based on debt registration data. The method of this application obtains the debt registration data of the target customer and combines the movable property registration information and the debt mean curve analysis to more comprehensively assess the customer's financial status and the liquidity risk of the debt. Through in-depth analysis of this information under preset data recognition conditions, potential risk points can be more accurately identified, thereby generating business signals or risk signals, improving the accuracy of data recognition, expanding the coverage of business opportunity discovery, and improving business service efficiency; improving risk prevention and control capabilities, and enhancing the security of financial transactions.

[0068] Please refer to Figure 2 , Figure 2A flowchart diagram of a second embodiment of a signal recognition method based on debt registration data provided in this application.

[0069] In this embodiment, Figure 2 As shown, based on the above Figure 1 In the illustrated embodiment, after S102, the following further includes:

[0070] S201, based on the movable property registration information, obtaining debt access data of each registered debt corresponding to the target customer, wherein the debt access data at least includes first access data of each registered debt in a current evaluation cycle and second access data of each registered debt in a previous evaluation cycle;

[0071] In one embodiment, the debt item access data includes the debt item access party that queries each registered debt item of the target customer and the debt item access times.

[0072] In one embodiment, based on the principle that the more queries the movable property registration platform has and the more querying agencies there are, the higher the customer risk is, the number of queries by external agencies and the querying agencies, i.e., the debt accessing party and the number of debt accesses are obtained.

[0073] S202, calculating the access frequency increase based on the first access data and the second access data;

[0074] In one embodiment, the number of visits and the number of query institutions for each registered debt item in two different evaluation periods are used.

[0075] Exemplarily, the calculation of the increase in access frequency may be:

[0076]

[0077] S203: When the increase in the access frequency is greater than a preset second increase threshold, generate the risk signal.

[0078] In one embodiment, if the increase in access frequency exceeds a preset second increase threshold, a risk signal is generated. If the increase in access frequency in the current evaluation cycle increases by ≥50% compared with the previous evaluation cycle, a risk signal is triggered.

[0079] Among them, the second increase threshold is a value set by risk managers based on historical data, industry standards and professional judgment, and is used to assess whether action needs to be taken on abnormal access frequency.

[0080] Furthermore, after obtaining the target customer's debt registration data, it also includes: based on the debt registration data, obtaining the target customer's debt registration frequency curve and procurement data curve; when the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or decreases, generating the risk signal.

[0081] In one embodiment, the debt registration frequency curve includes statistics and analysis of the number of debt registrations performed by target customers within a certain period of time.

[0082] In one embodiment, the purchase data curve may be the purchase behavior data of a customer in a certain period, including purchase frequency, purchase volume, etc. The purchase data may be obtained from the purchase system or supply chain management platform of the target customer, or may be extracted from the debt registration data.

[0083] In one embodiment, based on the principle that the higher the debt registration frequency is, the less the purchase scale increases or decreases significantly, the higher the customer risk is. When it is found that the debt registration frequency increases but the purchase scale stagnates or decreases, a risk signal is triggered.

[0084] In one embodiment, when the debt registration frequency curve shows an upward trend and the procurement data curve shows a stagnant or downward trend, this may indicate that the customer has reduced procurement activities while increasing debt, which may be a signal of financial risk and a risk signal needs to be generated to remind business personnel to conduct risk prevention and control for the target customer.

[0085] See also Figure 3 , Figure 3 It is a structural schematic diagram of a first embodiment of a signal recognition device based on debt registration data provided by the present application. The signal recognition device based on debt registration data is used to execute the aforementioned signal recognition method based on debt registration data.

[0086] like Figure 3 As shown, the signal identification device 300 based on debt registration data includes: a data acquisition module 301, a data statistics module 302, a business identification module 303 and a risk identification module 304.

[0087] The data acquisition module 301 is used to acquire the debt registration data of the target customer;

[0088] The data statistics module 302 is used to obtain the movable property registration information of the target customer based on the debt registration data of the target customer, and calculate the debt mean curve of the target customer within a preset time range;

[0089] A business identification module 303, configured to generate a business signal when the movable property registration information and / or the debt item mean value curve meets a business identification condition;

[0090] The risk identification module 304 is used to generate a risk signal when the dynamic registration information and / or the debt item average curve meets the early warning identification conditions.

[0091] In one embodiment, the service identification module 303 includes:

[0092] A registration frequency query unit, configured to query the current registration frequency of the first movable property information when there is unrecorded first movable property information in the movable property registration information of the target customer;

[0093] The first business signal generating unit is used to generate the business signal when the current registration frequency is less than a preset registration frequency threshold, so as to mark the target customer as a potential customer according to the business signal.

[0094] In one embodiment, the service identification module 303 further includes:

[0095] A debt item average value acquisition unit, used to calculate the debt item average values ​​of at least two preset evaluation periods when the debt item average value curve is trending upward, and obtain the first debt item average value of the current evaluation period and the second debt item average value of the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods;

[0096] An average value increase calculation unit, used to calculate the average value increase of the first debt item average value relative to the second debt item average value;

[0097] The second business signal generating unit is used to generate the business signal when the increase in the average value of the items is greater than the first increase threshold.

[0098] In one embodiment, the risk identification module 304 further includes:

[0099] A debt registration data acquisition unit, configured to acquire the first debt registration data of the target customer in the current evaluation cycle and the second debt registration data of the previous evaluation cycle when the debt mean curve is trending downward;

[0100] a debt item average value calculation unit, configured to calculate, based on the first debt item registration data and the second debt item registration data, a first debt item average value of each registered debt item corresponding to the target customer in a current evaluation cycle, and a second debt item average value of each registered debt item in a previous evaluation cycle;

[0101] a debt value reduction calculation unit, used to calculate the debt value reduction of the first debt average value relative to the second debt average value;

[0102] The first risk signal generating unit is used to generate the risk signal when the decline in the value of the debt item is greater than a preset decline threshold.

[0103] In one embodiment, the signal recognition device 300 based on debt registration data further includes a first risk recognition module, including:

[0104] a debt item access number acquisition unit, configured to acquire debt item access data of each registered debt item corresponding to the target customer based on the movable property registration information, wherein the debt item access data at least includes first access data of each registered debt item in a current evaluation cycle and second access data of each registered debt item in a previous evaluation cycle;

[0105] an access frequency increase calculation unit, configured to calculate the access frequency increase based on the first access data and the second access data;

[0106] The second risk signal generating unit is used to generate the risk signal when the increase in the access frequency is greater than a preset second increase threshold.

[0107] In one embodiment, the debt item access data includes the debt item access party that queries each registered debt item of the target customer and the debt item access times.

[0108] In one embodiment, the signal identification device 300 based on debt registration data further includes a second risk identification module, including:

[0109] A purchase data acquisition unit, used for acquiring a debt registration frequency curve and a purchase data curve of the target customer based on the debt registration data;

[0110] The third risk signal generating unit is used to generate the risk signal when the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or decreases.

[0111] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned signal recognition method based on debt registration data embodiment, and will not be repeated here.

[0112] The apparatus provided in the above embodiment may be implemented in the form of a computer program. The computer program may be Figure 4 Runs on the computer device shown.

[0113] See also Figure 4 , Figure 4 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0114] See also Figure 4 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0115] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any signal recognition method based on debt registration data.

[0116] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0117] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any signal recognition method based on the debt registration data.

[0118] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0120] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0121] Obtain the target customer's debt registration data;

[0122] Based on the target customer's debt registration data, obtain the target customer's movable property registration information, and calculate the target customer's debt mean curve within a preset time range;

[0123] When the movable property registration information and / or the debt item average curve meets the business identification condition, generating a business signal;

[0124] When the dynamic registration information and / or the debt item mean curve meets the early warning identification conditions, a risk signal is generated.

[0125] In one embodiment, when the processor generates a business signal when the movable property registration information and / or the debt item mean value curve meets the business identification condition, it is used to implement:

[0126] When there is unrecorded first movable property information in the movable property registration information of the target customer, querying the current registration frequency of the first movable property information;

[0127] When the current registration frequency is less than a preset registration frequency threshold, the service signal is generated to mark the target customer as a potential customer according to the service signal.

[0128] In one embodiment, when the processor generates a business signal when the movable property registration information and / or the debt item mean value curve meets the business identification condition, it is also used to implement:

[0129] When the debt item average value curve is on the rise, the debt item average value of at least two preset evaluation periods is calculated to obtain the first debt item average value of the current evaluation period and the second debt item average value of the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods;

[0130] Calculate the increase in the average value of the first debt item relative to the average value of the second debt item;

[0131] When the increase in the average value of the items is greater than a first increase threshold, the business signal is generated.

[0132] In one embodiment, when the processor generates a risk signal when the dynamic registration information and / or the debt item mean curve meets the early warning identification condition, it is also used to implement:

[0133] When the debt item mean value curve shows a downward trend, obtaining the first debt item registration data of the target customer in the current evaluation cycle and the second debt item registration data of the previous evaluation cycle;

[0134] Based on the first debt registration data and the second debt registration data, calculating the average value of the first debt of each registered debt corresponding to the target customer in the current evaluation cycle, and the average value of the second debt of each registered debt in the previous evaluation cycle;

[0135] Calculate the decline in the value of the first debt item relative to the average value of the second debt item;

[0136] When the decline in the value of the debt item is greater than a preset decline threshold, the risk signal is generated.

[0137] In one embodiment, after the processor implements the acquisition of movable property registration information of the target customer based on the debt registration data of the target customer and the calculation of the debt mean curve of the target customer within a preset time range, it is further configured to implement:

[0138] Based on the movable property registration information, acquiring debt item access data of each registered debt item corresponding to the target customer, wherein the debt item access data at least includes first access data of each registered debt item in a current evaluation cycle and second access data of each registered debt item in a previous evaluation cycle;

[0139] Calculating the access frequency increase based on the first access data and the second access data;

[0140] When the increase in the access frequency is greater than a preset second increase threshold, the risk signal is generated.

[0141] In one embodiment, the debt item access data includes the debt item access party that queries each registered debt item of the target customer and the debt item access times.

[0142] In one embodiment, after obtaining the target customer's debt registration data, the processor is further configured to:

[0143] Based on the debt registration data, obtaining a debt registration frequency curve and a purchase data curve of the target customer;

[0144] When the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or decreases, the risk signal is generated.

[0145] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any one of the signal recognition methods based on debt registration data provided in the embodiments of the present application.

[0146] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A signal recognition method based on debt registration data, characterized in that: The method comprises: Obtain the target customer's debt registration data; Based on the target customer's debt registration data, obtain the target customer's movable property registration information, and calculate the target customer's debt mean curve within a preset time range; When the movable property registration information and / or the debt item average curve meets the business identification condition, generating a business signal; When the dynamic registration information and / or the debt item mean curve meets the early warning identification conditions, a risk signal is generated.

2. The signal recognition method based on debt registration data according to claim 1 is characterized in that: When the movable property registration information and / or the debt item average curve meets the business identification condition, generating a business signal includes: When there is unrecorded first movable property information in the movable property registration information of the target customer, querying the current registration frequency of the first movable property information; When the current registration frequency is less than a preset registration frequency threshold, the service signal is generated to mark the target customer as a potential customer according to the service signal.

3. The signal recognition method based on debt registration data according to claim 1 is characterized in that: When the movable property registration information and / or the debt item average curve meets the business identification condition, generating a business signal also includes: When the debt item average value curve is on the rise, the debt item average value of at least two preset evaluation periods is calculated to obtain the first debt item average value of the current evaluation period and the second debt item average value of the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods; Calculate the increase in the average value of the first debt item relative to the average value of the second debt item; When the increase in the average value of the items is greater than a first increase threshold, the business signal is generated.

4. The signal recognition method based on debt registration data according to claim 1 is characterized in that: When the dynamic registration information and / or the debt item average curve meets the early warning identification conditions, generating a risk signal includes: When the debt item mean value curve shows a downward trend, obtaining the first debt item registration data of the target customer in the current evaluation cycle and the second debt item registration data of the previous evaluation cycle; Based on the first debt registration data and the second debt registration data, calculating the average value of the first debt of each registered debt corresponding to the target customer in the current evaluation cycle, and the average value of the second debt of each registered debt in the previous evaluation cycle; Calculate the decline in the value of the first debt item relative to the average value of the second debt item; When the decline in the value of the debt item is greater than a preset decline threshold, the risk signal is generated.

5. The signal recognition method based on debt registration data according to claim 1 is characterized in that: After obtaining the movable property registration information of the target customer based on the debt registration data of the target customer and calculating the debt mean curve of the target customer within a preset time range, the method further includes: Based on the movable property registration information, acquiring debt item access data of each registered debt item corresponding to the target customer, wherein the debt item access data at least includes first access data of each registered debt item in a current evaluation cycle and second access data of each registered debt item in a previous evaluation cycle; Calculating the access frequency increase based on the first access data and the second access data; When the increase in the access frequency is greater than a preset second increase threshold, the risk signal is generated.

6. The signal recognition method based on debt registration data according to claim 5 is characterized in that: The debt item access data includes the debt item access party that inquires about each registered debt item of the target customer and the debt item access times.

7. The signal recognition method based on debt registration data according to claim 1 is characterized in that: After obtaining the target customer's debt registration data, the method further includes: Based on the debt registration data, obtaining a debt registration frequency curve and a purchase data curve of the target customer; When the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or decreases, the risk signal is generated.

8. A signal recognition device based on debt registration data, characterized in that: The signal recognition device based on debt registration data includes: Data acquisition module, used to obtain the target customer's debt registration data; A data statistics module, for obtaining movable property registration information of the target customer based on the debt registration data of the target customer, and calculating a debt mean curve of the target customer within a preset time range; A business identification module, used to generate a business signal when the movable property registration information and / or the debt item mean value curve meets the business identification conditions; The risk identification module is used to generate a risk signal when the dynamic registration information and / or the debt item mean curve meets the early warning identification conditions.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the signal recognition method based on debt registration data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the signal recognition method based on debt registration data as described in any one of claims 1 to 7 are implemented.

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