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

Abstract

The present application provides a signal recognition method, device, equipment, and medium based on debt registration data, which relates to the field of data processing. The present application method obtains the debt registration data of the target customer and combines it with 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. By conducting in-depth analysis of this information based on 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; enhancing risk prevention and control capabilities, and enhancing the security of financial transactions.

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 (CCDNC) 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 check 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 the authenticity and effectiveness of the transfer of debt ownership. The use channels are relatively single, and the business signals and other risk warnings 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 the 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 bonds 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 bonds on the China Securities Depository and Clearing Corporation Limited website may be long, during which time the actual value of the bonds 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 bond mortgage value of customers requesting withdrawals by the lender, thereby increasing 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, apparatus, device 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 target customers' 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 a 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 target customers' debt registration data;

[0015] a data statistics module, configured to obtain movable property registration information of the target customer based on the target customer's debt registration data, and calculate a debt mean curve of the target customer within a preset time range;

[0016] A business identification module, 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;

[0017] The risk identification module is used to generate a risk signal when the dynamic registration information and / or the debt 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 on 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 a 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, generating a business signal; when the dynamic registration information and / or the debt mean curve meet the early warning identification conditions, generating a risk signal. Through the above-mentioned method, the present application obtains the debt registration data of the target customer and combines the movable property registration information and the analysis of the debt mean curve to more comprehensively assess the financial status of the customer and the liquidity risk of the debt. By conducting in-depth analysis of this information through 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. 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 any creative work.

[0022] Figure 1 This is a flowchart of a first embodiment of a signal recognition method based on debt registration data provided by this application;

[0023] Figure 2 This is a flow chart of a second embodiment of a signal recognition method based on debt registration data provided by this application;

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

[0025] Figure 4 This 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 objectives, 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 this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0029] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0030] Please refer to Figure 1 , Figure 1 This is a flow chart of the first embodiment of a signal recognition method based on debt registration data provided by 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 target customer's debt registration data;

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

[0034] Generally, obtaining target clients' debt registration data requires access to official channels or services provided by the China Securities Depository and Clearing Corporation Limited (CSDC). CSDC is a key infrastructure in 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 rights, and the clearing and settlement of securities and funds. CSDC's data standard interface serves as a standard for data exchange between listed companies and other market participants. This standard includes information such as mid-month / end-of-month major shareholder rosters, dividend refund details, and detailed data on margin trading and securities lending accounts secured by refinancing.

[0035] In one embodiment, obtaining the target client's debt registration data requires first clarifying the user's identity and query permissions. The data provided by China Securities Depository and Clearing Corporation (CSDC) typically involves sensitive information such as securities accounts, securities custody, transfers, and holder registers. Therefore, only authorized users can query the relevant data.

[0036] S102: 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;

[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 average bond yield curve for a target customer within a preset timeframe can be referenced by 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 fluctuations of government bonds of different maturities. Users can query the yield curves of government bonds of different maturities on the China Bond Information Network to analyze the changing trends of the average bond yields.

[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 based on 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 customer is a potential risk customer 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 based on the business signal.

[0046] In one embodiment, when processing the target client's movable property registration information, if there is unrecorded first movable property information—that is, if the target client's first movable property information is found in the Zhongdeng system but not recorded by the lender—the lender can query the current registration frequency of the first movable property information. This can generally be done through the Movable Property Financing Unified Registration and Publicity System, which handles the registration and inquiry of movable property and rights guarantees. Users can independently register through the system and query the registration frequency of the relevant movable property.

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

[0048] In a specific embodiment, for a whitelisted 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 signals, business personnel can further analyze the target customer's financial status and movable asset utilization to determine whether they are potential customers. Potential customers are those who may have a need for movable asset financing but have not yet engaged in such operations. In this way, financial institutions can more effectively identify and reach potential customers and provide them with relevant financing services.

[0051] Furthermore, when the debt item average value curve shows an upward trend, the average value of the debt items of at least two preset evaluation periods is calculated to obtain the first average value of the debt item in the current evaluation period and the second average value of the debt item in the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods; the average value increase of the first average value of the debt item relative to the second average value of the debt item is calculated; and when the average value increase is greater than the 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 first average value of debt items in the current assessment period and the second average value of debt items in the previous assessment period. Generally, debt financial data, such as debt registration data, can be collected and analyzed to determine the average value of debt items in each assessment period.

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

[0054]

[0055] In one embodiment, based on the principle that a higher average value of registered debt items indicates a more favorable customer development trend, a business signal is generated if the average value increase exceeds a preset first increase threshold. For example, if the average value of a target customer's debt items registered on the China Securities Depository and Clearing Corporation (CSDC) website in the current evaluation cycle increases by more than 50% (the first increase threshold) compared to the average value in 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 it into a business order.

[0057] S104: When the dynamic registration information and / or the debt item mean 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 debt value decline of the first debt average value relative to the second debt average value is calculated; when the debt value decline is greater than a preset decline threshold, the risk signal is generated.

[0059] In one embodiment, based on the debt registration data, the target customer's first debt registration data in the current evaluation cycle and the second debt registration data in the previous evaluation cycle are obtained. Each evaluation cycle can be set based on actual needs, such as a month, a quarter, a half-year, or a year. For example, the first debt registration data of the current month is compared with the second debt registration data of the previous month.

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

[0061] For example, the formula for calculating the decline in debt value can 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 the monthly and quarterly registered debt amounts of the customer / the number of debts. If it is found that the average value of the debts registered 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 a preset decline threshold. If the decline exceeds the threshold, a potential risk is identified. Once the decline is confirmed to have exceeded the threshold, a risk signal is generated and sent to the risk management department for further analysis and assessment by professionals to determine whether measures are necessary, such as adjusting credit limits, restructuring assets, or taking legal action.

[0065] Through the above methods, changes in bond values ​​can be effectively monitored, potential risks can be identified promptly, and appropriate risk management measures can be implemented. 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 client claim registration data of China Securities Depository and Clearing Corporation (CSDC) to identify risk signals and business signals, which are then followed up by the asset management department and the business department, respectively, to facilitate risk prevention and control and increase performance. The application of this technology significantly improves business operation efficiency, business opportunity identification and service efficiency, and risk management level, providing significant support for the company's overall operations. It expands the scope of use of public movable property registration data, and uses CSDC data for post-financial risk prevention and control and business signal discovery beyond contracts. It also increases the coverage of business opportunity discovery and improves business service efficiency. It also enhances the dimension and efficiency of risk discovery, improves risk control efficiency, and enhances asset management level.

[0067] This embodiment provides a signal recognition method based on debt registration data. This method obtains target customers' debt registration data and combines it with movable property registration information and debt mean curve analysis to more comprehensively assess the customer's financial status and the liquidity risk of the debt. By conducting in-depth analysis of this information based on preset data recognition conditions, potential risk points can be more accurately identified, thereby generating business signals or risk signals. This improves the accuracy of data recognition, expands the scope of business opportunity discovery, and enhances business service efficiency. It also enhances risk prevention and control capabilities and strengthens the security of financial transactions.

[0068] Please refer to Figure 2 , Figure 2This is a flow chart of the second embodiment of a signal recognition method based on debt registration data provided by this application.

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

[0070] S201. Obtaining debt item access data for each registered debt item corresponding to the target customer based on the movable property registration information, wherein the debt item access data includes at least first access data for each registered debt item in a current evaluation cycle and second access data for each registered debt item 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 number of debt item accesses.

[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, the number of queries by external agencies and the querying agencies, i.e., the debt accessing parties and the number of debt accesses, are obtained.

[0073] S202: Calculate an increase in access frequency 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] For example, the calculation of the increase in access frequency can 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 to 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 falls, 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 customer's purchase behavior data over a certain period of time, including purchase frequency, purchase volume, etc. The purchase data may be obtained from the target customer's purchase system or supply chain management platform, or extracted from debt registration data.

[0083] In one embodiment, based on the principle that the higher the debt registration frequency is, the less significant the increase or decrease in the purchase scale is, the higher the customer risk is, and if 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. 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 This is a structural 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 target customer's debt registration data;

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

[0089] A business identification module 303 is 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 configured to generate a risk signal when the dynamic registration information and / or the debt item mean 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 configured 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, configured to calculate the debt item average values ​​of at least two preset evaluation periods when the debt item average value curve shows an upward trend, and obtain a first debt item average value of the current evaluation period and a second debt item average value of the previous evaluation period, wherein the preset time range includes at least the two preset evaluation periods;

[0096] an average value increase calculation unit, configured to calculate the average value increase of the first debt item's average value relative to the second debt item's 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 shows a downward trend;

[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, configured to calculate the debt value reduction of the first average debt value relative to the second average debt 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, based on the movable property registration information, debt item access data for each registered debt item corresponding to the target customer, wherein the debt item access data includes at least first access data for each registered debt item in a current evaluation cycle and second access data for 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 configured 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 number of debt item accesses.

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

[0109] a purchase data acquisition unit, configured to acquire 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 configured 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 falls.

[0111] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned signal recognition method embodiment based on debt registration data, and will not be repeated here.

[0112] The apparatus provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in 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 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, which, when executed, can cause the processor to 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0119] It should be understood that the processor may be a central processing unit (CPU), or 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. The general-purpose processor may be a microprocessor or any conventional processor, etc.

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

[0121] Obtain target customers' 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 a 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 service signal when the movable property registration information and / or the debt mean curve meets the service identification condition, it is configured 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 service signal when the movable property registration information and / or the debt mean curve meets the service identification condition, it is further configured to:

[0129] When the debt item average value curve shows an upward trend, calculating the average value of the debt items in at least two preset evaluation periods to obtain a first average value of the debt items in the current evaluation period and a second average value of the debt items in the previous evaluation period, wherein the preset time range includes at least the two preset evaluation periods;

[0130] Calculating 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 further configured to:

[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 in the previous evaluation cycle;

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

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

[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 obtaining the movable property registration information of the target customer based on the debt registration data of the target customer and calculating the average debt curve of the target customer within a preset time range, the processor is further configured to:

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

[0139] Calculating an increase in access frequency 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 number of debt item accesses.

[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] The risk signal is generated when the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or falls.

[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 aforementioned 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 SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0147] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A signal recognition method based on debt registration data, characterized in that: The method comprises: Obtain target customers' 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 a business identification condition, generating a business signal; When the movable property registration information and / or the debt item average curve meets the early warning identification conditions, a risk signal is generated; Wherein, when the movable property registration information and / or the debt item mean value curve meets the business identification condition, generating a business signal includes: When unrecorded first movable property information exists 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, generating the service signal to mark the target customer as a potential customer according to the service signal; or When the debt item average value curve shows an upward trend, calculating the average value of the debt items for at least two preset evaluation periods to obtain a first average value of the debt items in the current evaluation period and a second average value of the debt items in the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods; calculating the average value increase of the first average value of the debt items relative to the second average value of the debt items; and generating the business signal when the average value increase is greater than a first increase threshold; Wherein, when the movable property registration information and / or the debt mean curve meets the early warning identification conditions, generating a risk signal includes: When the debt item average 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; calculating the first debt item average value of each registered debt item corresponding to the target customer in the current evaluation cycle and the second debt item average value of each registered debt item in the previous evaluation cycle based on the first debt item registration data and the second debt item average value; calculating the debt item value decline of the first debt item average value relative to the second debt item average value; and generating the risk signal when the debt item value decline is greater than a preset decline threshold. 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, debt access data of each registered debt corresponding to the target customer is obtained, wherein the debt access data at least includes the first access data of each registered debt in the current evaluation cycle and the second access data of each registered debt in the previous evaluation cycle; based on the first access data and the second access data, the access frequency increase is calculated; when the access frequency increase is greater than a preset second increase threshold, the risk signal is generated.

2. The signal recognition method based on debt registration data according to claim 1, characterized in that: The debt item access data includes the debt item access party that queries each registered debt item of the target customer and the number of debt item accesses.

3. The signal recognition method based on debt registration data according to claim 1, 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; The risk signal is generated when the trend of the debt registration frequency curve rises and the trend of the procurement data curve stagnates or falls.

4. 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 target customers' debt registration data; a data statistics module, configured to obtain movable property registration information of the target customer based on the target customer's debt registration data, and calculate a debt mean curve of the target customer within a preset time range; A business identification module, 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; The business identification module is further 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; and 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; or When the debt item average value curve shows an upward trend, calculating the average value of the debt items for at least two preset evaluation periods to obtain a first average value of the debt items in the current evaluation period and a second average value of the debt items in the previous evaluation period, wherein the preset time range includes at least two of the preset evaluation periods; calculating the average value increase of the first average value of the debt items relative to the second average value of the debt items; and generating the business signal when the average value increase is greater than a first increase threshold; a risk identification module, configured to generate a risk signal when the movable property registration information and / or the debt item mean value curve meets an early warning identification condition; The risk identification module is further configured to, when the debt item average value curve shows a downward trend, obtain the target customer's first debt item registration data in the current evaluation cycle and the second debt item registration data in the previous evaluation cycle; calculate, based on the first debt item registration data and the second debt item registration data, the first debt item average value of each registered debt item corresponding to the target customer in the current evaluation cycle and the second debt item average value of each registered debt item in the previous evaluation cycle; calculate the debt item value decline of the first debt item average value relative to the second debt item average value; and generate the risk signal when the debt item value decline is greater than a preset decline threshold. The signal recognition device based on debt registration data further includes a first risk recognition module, including: a debt item access number acquisition unit, configured to acquire, based on the movable property registration information, debt item access data for each registered debt item corresponding to the target customer, wherein the debt item access data includes at least first access data for each registered debt item in a current evaluation cycle and second access data for each registered debt item in a previous evaluation cycle; an access frequency increase calculation unit, configured to calculate the access frequency increase based on the first access data and the second access data; The second risk signal generating unit is configured to generate the risk signal when the increase in the access frequency is greater than a preset second increase threshold.

5. 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 3 are implemented.

6. 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 according to any one of claims 1 to 3 are implemented.

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