Loan deposit determination device and method, equipment and storage medium

Abstract

The invention discloses a loan deposit determination device and method, equipment and a storage medium. The method comprises the steps of determining a first batch value of a fund party in a target measurement and calculation period through a batch value determination module according to acquired historical loan data; determining a first return value of the asset party in the target measurement and calculation period through a return value determination module according to the acquired historical return data; a deposit determination scheme is determined through a calculation scheme determination module according to a loan cooperation mode between an asset party and a fund party; a guarantee value determination module determines a target guarantee value of guarantee fund provided by a guarantee to a fund party according to a first check value and a first return value on the basis of a guarantee fund determination scheme, so that building of a fund management system can be completed more quickly and accurately, uncertainty caused by manual intervention is reduced, and the fund management efficiency is improved. The operation efficiency and accuracy of the fund management system are improved, and the enterprise fund cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a loan margin determination device, method, equipment and storage medium. Background Art

[0002] Fintech companies, as platforms, provide funds for customers with loan needs. The institutions providing funds are called fund providers. In business cooperation, a guarantor will be introduced to provide joint liability guarantee to the fund provider and pay a certain percentage of margin to the fund provider. When the margin account is below a certain percentage, the guarantor has to pay liquidated damages, and the fund provider will stop cooperation, affecting the development of loan business. As the number and types of fund providers connected increase, the corresponding margin rules are also different.

[0003] Currently, the common practice is to calculate the overall margin payment amount based on the current outstanding balance of the borrower and the average value of historical data to estimate the future change in the outstanding balance. Professional appraisers classify it according to the customer situation and select appropriate rules and calculate based on experience. It is not only difficult to ensure timeliness, but its accuracy also depends on the level of personnel. The resulting delays and deviations will cause the occupation of margin and cause certain capital cost losses to the enterprise.

[0004] With the development of neural network models, how fintech enterprises effectively utilize big data tools, how to adjust and train the models to adapt to the characteristics of this industry, and build a more efficient and energy-saving fund management system have become urgent problems to be solved. Summary of the Invention

[0005] The present invention provides a loan margin determination device, method, equipment and storage medium, which can more real-time and accurately complete the calculation of the margin of the fund provider, not only can reduce the labor cost of the enterprise, but also can improve the refinement degree of the determination result of the fund provider's margin and save the capital cost of the enterprise.

[0006] According to one aspect of the present invention, a loan margin determination device is provided. The device includes an approval value determination module, a repayment value determination module, a calculation plan determination module and a guarantee value determination module; wherein,

[0007] The approval value determination module is used to obtain the historical loan data of the fund provider, and determine the first approval value of each fund provider's loan to each asset provider in the target calculation period according to the historical loan data;

[0008] The repayment value determination module is used to obtain the historical repayment data of the asset provider, and determine the first repayment value of each asset provider's repayment to each fund provider in the target calculation period according to the historical repayment data;

[0009] The measurement plan determination module is configured to determine, for each of the fund providers, a margin determination plan corresponding to the asset provider according to the loan cooperation mode between the asset provider and the fund provider, where the loan cooperation mode includes a self-operated mode, a joint loan mode, and a guarantee mode;

[0010] The guaranteed value determination module is configured to determine, based on the margin determination plan, a target guaranteed value of the margin provided by the guarantor corresponding to each asset provider to each fund provider within the target measurement period according to the first batch of verified values and the first recovery value.

[0011] According to another aspect of the present invention, a loan margin determination method is provided. The method includes:

[0012] Obtain the historical loan data of the fund provider through the batch verification value determination module, and determine the first batch of verified values of the loans made by each fund provider to each asset provider within the target measurement period according to the historical loan data;

[0013] Obtain the historical repayment data of the asset provider through the recovery value determination module, and determine the first recovery value of the repayments made by each asset provider to each fund provider within the target measurement period according to the historical repayment data;

[0014] For each of the fund providers, determine, through the measurement plan determination module, a margin determination plan corresponding to the asset provider according to the loan cooperation mode between the asset provider and the fund provider, where the loan cooperation mode includes a self-operated mode, a joint loan mode, and a guarantee mode;

[0015] Based on the margin determination plan, determine, through the guaranteed value determination module, a target guaranteed value of the margin provided by the guarantor corresponding to each asset provider to each fund provider within the target measurement period according to the first batch of verified values and the first recovery value.

[0016] According to another aspect of the present invention, an electronic device is provided. The electronic device includes:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; where

[0019] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the loan margin determination method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the loan margin determination method according to any embodiment of the present invention when executed.

[0021] In the technical solution of the embodiment of the present invention, the batch approval value determination module obtains the historical loan data of the fund provider, and determines the first batch approval value of each fund provider for lending to each asset provider during the target measurement period according to the historical loan data; the recovery value determination module obtains the historical repayment data of the asset provider, and determines the first recovery value of each asset provider for repaying each fund provider during the target measurement period according to the historical repayment data; the measurement plan determination module determines, for each fund provider, a margin determination plan corresponding to the asset provider according to the loan cooperation mode between the asset provider and the fund provider; the guarantee value determination module determines, based on the margin determination plan, the target guarantee value of the guarantor corresponding to each asset provider for providing margin to each fund provider during the target measurement period according to the first batch approval value and the first recovery value. This solves the technical problems of low accuracy and poor timeliness caused by traditional manual measurement, and can more efficiently and accurately match a model adapted to different customers for the measurement tool, so as to improve the refinement degree of the fund management system and save the enterprise's capital cost.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0024] Figure 1 is a flowchart of a loan margin determination device according to Embodiment 1 of the present invention;

[0025] Figure 2 is a structural diagram of a loan margin determination method according to Embodiment 2 of the present invention;

[0026] Figure 3 is a schematic structural diagram of an electronic device for implementing the loan margin determination method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data used in appropriate cases can be interchanged so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 It is a flowchart of a loan margin determination device provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of improving the accuracy and timeliness of loan margin determination. The device includes an approval value determination module, a repayment value determination module, a calculation plan determination module, and a guarantee value determination module. The loan margin determination device can be implemented in the form of hardware and / or software, and the loan margin determination device can be configured in an electronic device. As Figure 1 shown, the device includes:

[0031] The approval value determination module 101 is configured to obtain the historical loan data of the fund provider, and determine the first approval value of each fund provider for lending to each asset provider within the target calculation period according to the historical loan data;

[0032] The repayment value determination module 102 is configured to obtain the historical repayment data of the asset provider, and determine the first repayment value of each asset provider for repaying each fund provider within the target calculation period according to the historical repayment data;

[0033] The measurement plan determination module 103 is configured to determine, for each of the fund providers, a margin determination plan corresponding to the asset provider according to the loan cooperation model between the asset provider and the fund provider, where the loan cooperation model includes a self-operated model, a joint loan model, and a guarantee model;

[0034] The target guarantee value determination module 104 is configured to determine, based on the margin determination plan, a target guarantee value of the margin provided by the guarantor corresponding to each asset provider to each fund provider during the target measurement period according to the first batch of underwriting values and the first repayment values.

[0035] It should be noted that the technical solution of the present invention can be applied to a fintech platform. As an intermediary platform, the fintech platform connects customers with loan needs and fund providers, and provides services such as technology, risk control, and operation. Among them, the fund provider may refer to an institution that provides loan funds, such as a bank, a trust company, a consumer finance company, a microfinance company, etc. The asset provider may be a borrower who applies for a loan through the platform, and may include individuals, institutions, and companies. In the loan business cooperation, a guarantor will be introduced. The role of the guarantor is to provide joint liability guarantee for the fund provider, bear the default risk of the asset provider, and pay a margin to the fund provider. In the present invention, through the fintech platform, the guarantee values of the margins that multiple asset providers need to pay to each fund provider respectively can be determined.

[0036] It should be explained that the historical lending data may refer to the historical data of the fund provider lending to the asset provider, and may include various data within each lending period. Correspondingly, the historical repayment data may refer to the historical data of the asset provider repaying the fund provider, and may include various data within each repayment period. The target measurement period may refer to the calculation period for which the margin amount needs to be calculated. It should be noted that the period lengths of the lending period, the repayment period, and the target measurement period may be the same, and the specific period values may be jointly determined by the asset provider and the fund provider.

[0037] The first batch of underwriting values may refer to the underwriting values determined for the fund provider lending to the asset provider during the target measurement period. The first repayment values may refer to the repayment values determined for the asset provider repaying the fund provider during the target measurement period. The target guarantee value may refer to the guarantee value of the margin that the guarantor should pay to the fund provider during the target measurement period.

[0038] It is worth noting that in the present invention, the target guarantee value needs to be calculated through the margin determination plan. And the margin determination plan is determined according to the loan cooperation model of the asset provider. Exemplarily, the loan cooperation model includes a self-operated model, a joint loan model, a guarantee model, and a buyout model. Among them, the self-operated model is applicable to bank lending, the joint loan model is applicable to trust lending institutions, the guarantee model is applicable to guarantee companies, and the buyout model is applicable to consumer finance companies.

[0039] The margin calculation rules vary for different fund providers and different cooperation methods. First, classify the margin calculation rules, and then configure different classification rules and parameter values for the cooperation methods of different fund providers. For example, the margin for the consumer finance self-operated model = all outstanding loans * margin ratio, and the margin for the consumer finance joint loan model = outstanding loans of the self-operated lending part under the joint loan model * margin ratio. Among them, outstanding loans = total loans under different cooperation methods of the fund provider - estimated customer repayment amount of the fund provider - principal of the compensation buyout period part (total loans under different cooperation methods of the fund provider - estimated customer repayment amount of the fund provider) + estimated loan amount of the fund provider + current outstanding loans. The compensation buyout period varies for different fund providers and different cooperation methods. For example, a certain consumer finance company compensates for overdue loans at D+3 and buys out consecutive three and cumulative six overdue loans, and another consumer finance company compensates for overdue loans at D+4 and buys out at D+50, etc. The margin ratio varies for different fund providers and different cooperation models, such as 3% and 5%. Among them, the loan amount of the fund provider and the repayment amount of the fund provider are obtained from the previous steps. Different rules are configured for the compensation buyout period and the margin ratio for different fund providers and cooperation methods. The total loans and current outstanding loans under different cooperation methods of the fund provider are calculated in real time.

[0040] Specifically, through the approval value determination module, the first batch of approval values for the fund provider to lend to the asset side within the target measurement period is calculated using historical loan data. Through the recovery value determination module, the first recovery value for the asset side to return funds to the fund provider within the target measurement period is calculated using historical repayment data. Through the measurement plan determination module, a margin determination plan corresponding to the loan cooperation model with the asset side is determined. Furthermore, through the guarantee value determination module, using the first batch of approval values, the first recovery value, and the determined margin determination plan, the target guarantee value of the guarantee party to provide margin to the fund provider within the target measurement period is calculated. The technical solution of the embodiment of the present invention solves the technical problems of low accuracy and poor timeliness caused by traditional manual measurement, and can complete the measurement of the fund provider's margin more real-time and accurately, so as to improve the refinement degree of the determination result of the fund provider's margin and save the enterprise's capital cost.

[0041] Based on the above embodiments, the approval value determination module can be specifically used for:

[0042] For each of the fund providers, the historical loan data is parsed and processed to obtain the first average loan ratio of the fund provider to lend to the asset side in the historical measurement period; the initial approval value is determined according to the first average loan ratio and the loan application value of the asset side; and the first batch of approval values corresponding to each of the fund providers are determined according to the initial approval value and a preset loan change factor.

[0043] Among them, the first average loan disbursement ratio may refer to the average loan disbursement ratio when the funds are disbursed to the asset side in each historical measurement period in the past. The loan disbursement change factors include market volatility factors, policy adjustment factors, fund provider policies factors, and seasonal factors. Exemplarily, the market volatility factor may be that due to the increase in the cost of interbank funds, the willingness of the fund provider to disburse loans decreases. The policy adjustment factor may be that the regulatory unit requires an increase in the loan disbursement ratio for a certain customer group, and the loan limit for the specific customer group is increased. The fund provider policies factor may be that a certain bank tightens the credit disbursement ratio for the blue-collar customer group and the disbursement ratio is lowered. The seasonal factor may be that during Double 11 and 618, the demand for consumer loans surges, and the number of loan applications increases, resulting in an increase in the disbursement volume.

[0044] Specifically, the approval value determination module calculates the first average loan disbursement ratio by using historical loan disbursement data, and then combines it with the loan application value of the asset side to initially determine the initial approval value. By multiplying the determined loan disbursement change factor by the initial approval value, the first approval value that each fund provider should disburse is determined.

[0045] It should be noted that the determination method of the first approval value can also be determined by other technical solutions. For example, based on the published invention "A Method, Device, Equipment, and Storage Medium for Estimating the Loan Disbursement Quota of a Fund Provider", it includes: receiving a quota determination request, obtaining the historical loan data of the asset side based on the quota determination request, and obtaining the expected loan volume of each customer group corresponding to the asset side in the target period based on the historical loan data; determining the historical loan disbursement data of the fund provider for each customer group, and obtaining the expected loan disbursement volume of the fund provider for each customer group in the target period based on the historical loan disbursement data; obtaining the change factor, and based on the expected loan volume, the expected loan disbursement volume, and the change factor, determining the target fund provider and the target loan disbursement quota corresponding to each customer group in the target period and feeding them back to the requester of the quota determination request. By estimating the loan expectation and the loan disbursement expectation based on a large amount of historical data, and simultaneously obtaining the change value of the change factor in real time to dynamically correct the estimated value, it realizes a more real-time and accurate estimation of the actual loan disbursement quota of the fund provider.

[0046] Exemplarily, the collection value determination module includes:

[0047] A trend model construction unit for constructing a collection trend model for the asset side to collect funds from the fund provider according to the historical collection data; a collection value determination unit for determining the first collection value corresponding to each fund provider according to the collection trend model.

[0048] Among them, the collection trend model is used to determine the collection data in the target measurement period according to the collection trend of the asset side, and it is obtained by training based on the historical collection data of the asset side.

[0049] Specifically, the trend model construction unit obtains the repayment trend by analyzing historical repayment data, and then constructs a recovery trend model. The recovery value determination unit calculates the first recovery value corresponding to the asset side through the recovery trend model. The technical solution of the embodiment of the present invention determines the repayment funds of the asset side through the recovery trend model, thereby improving the accuracy of the first recovery value and further improving the accuracy of determining the margin.

[0050] Exemplarily, the trend model construction unit is specifically configured to: perform parsing processing on the historical repayment data to determine the historical recovery data of each asset side's repayment to each fund side in the time series dimension and the first attribute data of each asset side; based on the first attribute data, divide the asset sides into groups to obtain multiple group asset sides; for each group of asset sides, construct a recovery trend model for each group of asset sides to repay the fund side according to the historical recovery data corresponding to the group of asset sides in the time series trend.

[0051] Among them, the first attribute data includes data such as customer acquisition channels, age ranges, occupations, industries, regions, risk levels, product terms, product pricing, first / renewed loans, repayment amount ranges, repayment times, repayment methods, whether overdue, and overdue days.

[0052] Specifically, by analyzing the first attribute data, the asset sides are divided into different customer groups to obtain multiple group asset sides. For each customer group, summarize the historical recovery data on a monthly / weekly basis to form a time series trend line in the time series. Construct a recovery trend model for each group of asset sides to repay the fund side according to the actual data situation.

[0053] Exemplarily, the recovery value determination unit is specifically configured to: for each asset side, determine the recovery trend model corresponding to the group of asset sides to which the fund side belongs; according to the recovery trend model, determine the initial recovery value corresponding to each fund side; according to the initial recovery value and a preset recovery variation factor, determine the first recovery value for repaying each fund side.

[0054] Among them, the recovery variation factors include a collection strategy factor, a repayment incentive factor, and a policy change factor. Exemplarily, the collection strategy factor may refer to the collection efficiency after introducing collection robots. The repayment incentive factor can be a repayment full reduction discount, such as a reduction of 100 for every 5000 repaid, to enhance the asset side's willingness to repay in advance. The policy change factor can be that the regulatory unit requires shortening the overdue compensation time limit, increasing the compensation pressure.

[0055] Specifically, by determining the recovery trend model corresponding to the fund side, the initial recovery value of the asset side is determined, and then in combination with the recovery variation factor, the initial recovery value of each group of asset sides is corrected, and then the first recovery value for repaying each fund side is obtained.

[0056] Exemplarily, the guaranteed value determination module includes:

[0057] A target recovery value determination unit, configured to, for each of the asset parties, determine a target recovery value for the asset party to participate in the calculation of the guaranteed value according to the first recovery value and a preset recovery value previously determined with the capital party; a target guaranteed value determination unit, configured to substitute the first batch of valuation values corresponding to the asset party and the target recovery value into the guarantee deposit determination scheme to determine a target guaranteed value for the guarantor corresponding to each asset party to provide a guarantee deposit to each capital party during the target measurement period.

[0058] Wherein, the preset recovery value may refer to the recovery value agreed in advance between the asset party and the capital party for repayment within the target measurement period.

[0059] Specifically, for each of the asset parties, the target recovery value determination unit compares the first recovery value with the preset recovery value and determines a target recovery value for the asset party to participate in the calculation of the guaranteed value according to the comparison result.

[0060] Based on the technical solution of the above embodiment, the target recovery value determination unit is specifically configured to: when the first recovery value is greater than or equal to the preset recovery value, determine the first recovery value as the target recovery value; when the first recovery value is less than the preset recovery value, determine the preset recovery value as the target recovery value.

[0061] Furthermore, through the target guaranteed value determination unit, substitute the first batch of valuation values and the target recovery value into the guarantee deposit determination scheme to determine a target guaranteed value that the guarantor corresponding to each asset party should provide for the guarantee deposit.

[0062] For the capital party, the technical solution of the embodiment of the present invention enables precise coverage of the actual risk by adjusting the estimated first recovery value and the compensation risk calculation in real time, avoiding the capital idle caused by the traditional fixed - ratio guarantee deposit. In addition, the present invention adjusts the guarantee deposit in real time through various variable factors, avoiding the artificial response delay. Finally, the engine automatically executes the calculation of the target guaranteed value of the guarantee deposit, reducing the errors or frauds caused by manual intervention. The technical solution of the present invention can achieve the upgrade from "static prediction" to "dynamic perception - real - time adjustment", and benefit simultaneously in three dimensions of risk, cost, and efficiency, significantly improving the scientificity and reliability of the repayment management.

[0063] Embodiment Two

[0064] Figure 2 It is a flowchart of a method for determining a loan guarantee deposit provided by the second embodiment of the present invention. As Figure 2 shown, the method includes:

[0065] S201. Obtain the historical loan data of the fund providers through the approval value determination module, and determine the first batch of approval values for each fund provider to lend to each asset provider within the target measurement period based on the historical loan data;

[0066] S202. Obtain the historical repayment data of the asset providers through the repayment value determination module, and determine the first repayment values for each asset provider to repay each fund provider within the target measurement period based on the historical repayment data;

[0067] S203. For each fund provider, determine the guarantee deposit determination plan corresponding to the asset provider through the measurement plan determination module according to the loan cooperation model between the asset provider and the fund provider, where the loan cooperation model includes the self-operated model, the joint loan model, and the guarantee model;

[0068] S204. Based on the guarantee deposit determination plan, determine the target guarantee values for the guarantors corresponding to each asset provider to provide guarantee deposits to each fund provider within the target measurement period according to the first batch of approval values and the first repayment values through the guarantee value determination module.

[0069] The technical solution of the embodiment of the present invention obtains the historical loan data of the fund providers through the approval value determination module, and determines the first batch of approval values for each fund provider to lend to each asset provider within the target measurement period based on the historical loan data; obtains the historical repayment data of the asset providers through the repayment value determination module, and determines the first repayment values for each asset provider to repay each fund provider within the target measurement period based on the historical repayment data; for each fund provider, determines the guarantee deposit determination plan corresponding to the asset provider according to the loan cooperation model between the asset provider and the fund provider through the measurement plan determination module; based on the guarantee deposit determination plan, determines the target guarantee values for the guarantors corresponding to each asset provider to provide guarantee deposits to each fund provider within the target measurement period according to the first batch of approval values and the first repayment values through the guarantee value determination module, solving the technical problems of low accuracy and poor timeliness caused by traditional manual measurement, and being able to complete the measurement of the guarantee deposits of the fund providers more real-time and accurately, so as to improve the refinement degree of the determination results of the guarantee deposits of the fund providers and save the enterprise's capital cost.

[0070] Optionally, the method further includes:

[0071] Through the approval value determination module, for each fund provider, parse and process the historical loan data to obtain the first average loan ratio of the fund provider to lend to the asset provider within the historical measurement period;

[0072] Determine an initial approval value according to the first average loan disbursement ratio and the loan application value of the asset side;

[0073] Determine the first approval value corresponding to each of the fund providers according to the initial approval value and a preset loan disbursement variation factor, where the loan disbursement variation factor includes a market fluctuation factor, a policy adjustment factor, a fund provider policy factor, and a seasonal factor.

[0074] Optionally, the collection value determination module further includes: a trend model construction unit and a collection value determination unit. The method includes: constructing, by the trend model construction unit, a collection trend model for the asset side to make repayments to the fund provider according to the historical repayment data;

[0075] Determining, by the collection value determination unit, the first collection value corresponding to each of the fund providers according to the collection trend model.

[0076] Optionally, the method further includes:

[0077] Parsing and processing the historical repayment data by the trend model construction unit to determine the historical collection data of each asset side's repayment to each fund provider in the time series dimension and the first attribute data of each asset side, where the first attribute data at least includes a risk level, an overdue days, a customer acquisition channel, and an identity attribute;

[0078] Based on the first attribute data, dividing the asset sides into groups to obtain multiple group asset sides;

[0079] For each group of asset sides, constructing a collection trend model for each group of asset sides to make repayments to the fund provider according to the historical collection data corresponding to the group of asset sides in the time series trend.

[0080] Optionally, the method further includes:

[0081] Determining, by the collection value determination unit, the collection trend model corresponding to the group of asset sides to which the fund provider belongs for each asset side;

[0082] Determining the initial collection value corresponding to each of the fund providers according to the collection trend model;

[0083] Determine the first collection value for repaying each of the fund providers according to the initial collection value and a preset collection variation factor, where the collection variation factor includes a collection strategy factor, a collection incentive factor, and a policy change factor.

[0084] Optionally, the guarantee value determination module further includes a target collection value determination unit and a target guarantee value determination unit. The method further includes:

[0085] For each of the asset parties, a target recovery value determination unit determines a target recovery value for the asset party to participate in the guarantee value calculation according to the first recovery value and a preset recovery value previously determined with the capital party.

[0086] A target guarantee value determination unit substitutes the first batch of valuation values corresponding to the asset party and the target recovery value into the guarantee margin determination scheme to determine a target guarantee value for the guarantor corresponding to each asset party to provide a guarantee margin to each capital party during the target measurement period.

[0087] Optionally, the method further includes:

[0088] The target recovery value determination unit, when the first recovery value is greater than or equal to the preset recovery value, determines the first recovery value as the target recovery value; when the first recovery value is less than the preset recovery value, determines the preset recovery value as the target recovery value.

[0089] The loan guarantee margin determination method provided by the embodiments of the present invention has the same beneficial effects as the loan guarantee margin determination device.

[0090] Embodiment III

[0091] Figure 3 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0092] As Figure 3As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0093] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0094] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the loan margin determination method.

[0095] In some embodiments, the loan margin determination method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the loan margin determination method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the loan margin determination method in any other appropriate way (for example, by means of firmware).

[0096] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0097] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0098] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input received from the user can be in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0101] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0103] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A loan margin determination device, characterized in that, It includes an approval value determination module, a repayment value determination module, a measurement plan determination module, and a guarantee value determination module; among them, The approval value determination module is used to obtain the historical lending data of the fund provider, and determine the first approval value of each fund provider's lending to each asset provider within the target measurement period based on the historical lending data; The repayment value determination module is used to obtain the historical repayment data of the asset provider, and determine the first repayment value of each asset provider's repayment to each fund provider within the target measurement period based on the historical repayment data; The measurement plan determination module is used to determine the guarantee determination plan corresponding to the asset provider for each fund provider according to the loan cooperation mode between the asset provider and the fund provider, where the loan cooperation mode includes the self-operation mode, the joint loan mode, and the guarantee mode; The guarantee value determination module is used to determine the target guarantee value of the guarantor corresponding to each asset provider providing a guarantee to each fund provider within the target measurement period based on the guarantee determination plan, the first approval value, and the first repayment value.

2. The device according to claim 1, characterized in that The repayment value determination module includes: A trend model construction unit, which is used to construct a repayment trend model of the asset provider's repayment to the fund provider according to the historical repayment data; A repayment value determination unit, which is used to determine the first repayment value corresponding to each fund provider according to the repayment trend model.

3. The device according to claim 2, characterized in that The trend model construction unit is specifically used for: Analyze and process the historical repayment data to determine the historical repayment data of each asset provider's repayment to each fund provider in the time series dimension and the first attribute data of each asset provider, where the first attribute data at least includes the risk level, the overdue days, the customer acquisition channel, and the identity attribute; Based on the first attribute data, divide the asset providers into groups to obtain multiple group asset providers; For each group of asset providers, construct a repayment trend model of each group of asset providers' repayment to the fund provider according to the historical repayment data corresponding to the group of asset providers in the time series trend.

4. The device according to claim 2, characterized in that The repayment value determination unit is specifically used for: For each asset provider, determine the repayment trend model corresponding to the group of asset providers to which the fund provider belongs; Determine the initial repayment value corresponding to each fund provider according to the repayment trend model; Determine the first repayment value of the repayment to each fund provider according to the initial repayment value and the preset repayment change factor, where the repayment change factor includes the collection strategy factor, the repayment incentive factor, and the policy change factor.

5. The device according to claim 1, characterized in that, The guarantee value determination module includes: A target repayment value determination unit, which is used to determine the target repayment value of the asset provider participating in the guarantee value calculation for each asset provider according to the first repayment value and the preset repayment value previously determined with the fund provider; A target guarantee value determination unit, configured to substitute the first batch of valuation values corresponding to the asset party and the target recovery value into the guarantee margin determination scheme, and determine the target guarantee value of the guarantee party corresponding to each asset party providing a guarantee margin to each fund party during the target measurement period.

6. The device according to claim 5, characterized in that, The target recovery value determination unit is specifically configured to: When the first recovery value is greater than or equal to the preset recovery value, determine the first recovery value as the target recovery value; When the first recovery value is less than the preset recovery value, determine the preset recovery value as the target recovery value.

7. The device according to claim 1, characterized in that, The batch valuation value determination module is specifically configured to: For each fund party, analyze and process the historical loan data to obtain the first average loan ratio of the fund party lending to the asset party during the historical measurement period; Determine the initial batch valuation value according to the first average loan ratio and the loan application value of the asset party; Determine the first batch of valuation values corresponding to each fund party according to the initial batch valuation value and a preset loan change factor, where the loan change factor includes a market fluctuation factor, a policy adjustment factor, a fund party policy factor, and a seasonal factor.

8. A method for determining a loan margin, characterized in that It includes: Obtain the historical loan data of the fund party through the batch valuation value determination module, and determine the first batch of valuation values of each fund party lending to each asset party during the target measurement period according to the historical loan data; Obtain the historical repayment data of the asset party through the recovery value determination module, and determine the first recovery value of each asset party repaying to each fund party during the target measurement period according to the historical repayment data; For each fund party through the measurement scheme determination module, determine the guarantee margin determination scheme corresponding to the asset party according to the loan cooperation mode between the asset party and the fund party, where the loan cooperation mode includes a self-operated mode, a joint loan mode, and a guarantee mode; Based on the guarantee margin determination scheme through the guarantee value determination module, determine the target guarantee value of the guarantee party corresponding to each asset party providing a guarantee margin to each fund party during the target measurement period according to the first batch of valuation values and the first recovery value.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the loan guarantee margin determination method according to claim 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the loan guarantee margin determination method according to claim 7 is implemented.

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