CN112991049B - Loan information processing method and electronic equipment - Google Patents
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Abstract
The embodiment of the application provides a loan information processing method and electronic equipment. The method comprises the following steps: and inputting loan information of the user with overdue behaviors into a complaint probability information prediction model to obtain complaint probability information of the user, determining whether the user is a high complaint risk user based on the complaint probability information, and acquiring a target collection policy when the user is the high complaint risk user, wherein the target collection policy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user, and executing corresponding operation according to the target collection policy, so that the user experience is improved while the refund rate is ensured.
Description
Technical Field
The embodiment of the application relates to the technical field of artificial intelligence, and more particularly relates to a loan information processing method and electronic equipment.
Background
At present, after overdue loan occurs, the user is usually urged to receive the refund through artificial intelligence (Artificial Intelligence, AI), however, because the user has different receiving degrees of the urging, the urging mode without difference will reduce user experience and bring more user complaints, so how to reduce the user complaints and realize effective urging is a problem to be solved urgently at present.
Disclosure of Invention
The embodiment of the application provides a loan information processing method and electronic equipment, which can carry out differentiated collection aiming at the receiving degree of different users on collection so as to improve user experience.
In a first aspect, a method for processing loan information is provided, including:
inputting loan information of a user with overdue behaviors into a complaint probability information prediction model to obtain complaint probability information of the user; determining whether the user is a high complaint risk user based on the complaint probability information; when the user is a high complaint risk user, acquiring a target collection policy, wherein the target collection policy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user; and executing corresponding operation according to the target collection strategy.
In a second aspect, there is provided an electronic device comprising:
the processing unit is used for inputting loan information of the user with overdue behaviors into the complaint probability information prediction model to obtain complaint probability information of the user; the processing unit is also used for determining whether the user is a high complaint risk user or not based on the complaint probability information; the acquiring unit is used for acquiring a target collection strategy when the user is a high complaint risk user, wherein the target collection strategy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user; the processing unit is also used for executing corresponding operations according to the target collecting policy.
In a third aspect, there is provided an electronic device comprising: a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory for performing the method as in the first aspect or implementations thereof.
In a fourth aspect, a computer-readable storage medium is provided for storing a computer program for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a fifth aspect, a computer program product is provided comprising computer program instructions for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a sixth aspect, a computer program is provided, the computer program causing a computer to perform the method as in the first aspect or in various implementations thereof.
According to the method and the device for prompting the user, the complaint risk of the user can be identified according to the loan information of the user, the user with high complaint risk is subjected to customized prompting by adopting the target prompting strategy, the rate of refund is ensured, the user experience is improved, and the complaint of the user is reduced.
Drawings
Fig. 1 is a schematic diagram of an application scenario 100 of loan information processing provided in an embodiment of the present application;
fig. 2 is a flowchart of a method 200 for processing loan information according to an embodiment of the present disclosure;
fig. 3 is a schematic block diagram of an electronic device 300 according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device 400 according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden for the embodiments herein, are intended to be within the scope of the present application.
For ease of understanding, the technical terms mentioned in the embodiments of the present application will be described first:
overdue stage M1: the term "overdue" refers to the period when the user should pay for no payment, and the overdue days are 1-31 days, i.e. within one month.
The user is prompted to: refers to users who need to be rewarded for overdue activity.
With the continuous progress of intelligent robots, intelligent collecting robots can be used for replacing manpower step by step in the collecting process, and manpower is saved. However, the same collecting mode and collecting strength are adopted for collecting different users, so that the experience of a part of users is poor, the complaint rate of the users is continuously increased, and larger cost is required to be input to solve the complaint of the users.
Based on the above problems, the application provides a differentiated collection scheme, which can identify the complaint risk of the user according to the loan information of the user, and collect the user with high complaint risk by adopting a target collection strategy, for example, reducing collection strength, adopting a gentler collection technique and the like, so as to improve user experience and reduce user complaint.
It should be noted that, the loan information in the present application may include any user feature related to the loan business, and may include, for example, at least one of the following: historical loan records, historical collection records, historical customer service communication records, historical behavior records and the like of the user.
The execution subject of the present application is an electronic device, and it should be understood that the electronic device may be a terminal device, such as a Mobile Phone (Mobile Phone), a tablet computer (Pad), a computer, etc. In some embodiments, the electronic device may also be a server.
Fig. 1 is a schematic diagram of an application scenario 100 of loan information processing according to an embodiment of the present application. As shown in fig. 1, the electronic device 110 for processing loan information is connected to a plurality of user terminals 120 in a wired or wireless manner, the electronic device 110 is connected to the service terminal 130 in a wired or wireless manner, and the service terminal 130 is connected to the plurality of user terminals 120 in a wired or wireless manner.
Wherein, each user terminal 120 logs in with an account number of the user entering the incentive; the service terminal 130 logs in with the account number of the party desiring the loan-furthering. Optionally, the number of service terminals 130 is one to a plurality.
Alternatively, the service terminal 130 may be integrated with the electronic device 110.
Based on the above application scenario, the electronic device 110 in the embodiments of the present application may perform the collecting operation on at least one user terminal 120 according to the target collecting policy, and in some embodiments, the electronic device 110 may notify the service terminal 130, so that the service terminal 130 performs the collecting operation on at least one user terminal 120 according to the determined collecting policy.
The technical scheme of the present application is described in detail below through specific embodiments.
Fig. 2 is a flowchart of a loan information processing method 200 according to an embodiment of the present disclosure. As shown in fig. 2, the execution body of the embodiment of the present application may be the electronic device 110, and the method specifically includes:
s201: inputting loan information of a user with overdue behaviors into a complaint probability information prediction model to obtain complaint probability information of the user;
s202: determining whether the user is a high complaint risk user based on the complaint probability information;
s203: when the user is a high complaint risk user, acquiring a target collection policy, wherein the target collection policy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user;
s204: and executing corresponding operation according to the target collection strategy.
It should be noted that the embodiments of the present application may be applied to loan-incentive to overdue users in any overdue stage, for example, overdue stage M1.
Illustratively, the complaint probability information identified by the complaint probability information prediction model may be a complaint probability, or may be a complaint probability score, for example, may be represented by 1-99 scores, and the higher the score, the higher the complaint probability.
The target collecting policy is a collecting policy predetermined for high complaint risk users, and the target collecting policy is used for collecting the complaint rate obviously, and has small influence on the refund rate. Complaints in the general collection process mainly comprise complaint collection attitude, collection mode and the like. For high complaint risk groups, a more relaxed collecting attitude and a proper collecting mode are required. The target revenue-generating strategy may include: at least one of a frequency of the collection, a manner of the collection, and a conversation template of the collection, and by way of example, the frequency of the collection of the target collection policy should be smaller than the frequency of the collection of the general collection policy, the manner of the collection may be a contact manner of not dialing emergency contacts and/or third parties, etc., the conversation in the conversation template of the collection may be a conversation using compliance and service, and optionally, the template collection policy may further include attitude prompt information for prompting to adopt a moderate collection attitude. The specific strategy can be formulated and adjusted according to the specific form of the product and the historical complaint information in the process of prompting and collecting.
In some embodiments, the electronic device may send a call request to the user terminal according to the target revenue-generation policy, so that the loan revenue-generation requiring party may collect the user according to the revenue-generation technique in the target revenue-generation policy; alternatively, the electronic device may send an audit message to the user terminal according to the target audit policy, optionally generated based on audit trails in the target audit policy.
In other embodiments, the electronic device may send the target revenue-generating policy to the service terminal, causing the service terminal to send the call request and/or the revenue-generating message to the user terminal according to the target revenue-generating policy.
It should be noted that, the call request and the collect message are both used to prompt the user to pay.
In some embodiments, the electronic device may determine a complaint probability information rank to which the complaint probability information belongs, and determine that the user is a high complaint risk user when a complaint rate corresponding to the complaint probability information rank is greater than a preset value.
The complaint rate is the proportion of the number of users with complaint behaviors in the complaint probability information grading to the total number of users.
In some embodiments, multiple complaint probability information profiles may be determined based on a set of user samples and a complaint risk profile corresponding to each complaint probability profile may be determined, the complaint risk profile including a high risk and a low risk, or multiple complaint probability information profiles may be determined based on a set of user samples and a preset value may be determined based on a complaint rate of each complaint probability information profile and a complaint rate corresponding to a set of user samples, a complaint probability profile greater than the preset value may be corresponding to the high risk, and a complaint probability profile less than the preset value may be corresponding to the low risk.
The above-described user sample set is contracted with the third sample set in the embodiment of the present application.
The method includes the steps of determining a complaint probability information grade to which complaint probability information belongs, and determining that a user is a high complaint risk user when a complaint rate corresponding to the complaint probability information grade is larger than a preset value, wherein the complaint rate is the proportion of the number of users with complaint behaviors in the complaint probability information grade to the total number of users.
For example, in this embodiment, complaint probability information corresponding to each sample in the third sample set may be determined, a plurality of complaint probability information steps may be obtained based on the complaint probability information corresponding to each sample in the third sample set, a complaint rate corresponding to each complaint probability information step may be obtained, and the preset value may be determined based on the complaint rate corresponding to each complaint probability information step and the complaint rate corresponding to the third sample set.
Alternatively, each complaint probability score may correspond to one or more complaint probability information, for example, the complaint probability information is a score of 1 for a complaint probability, and the complaint probability information is a score of 1 for a complaint probability score, and for example, the complaint probability information is a score of 0.5% to 1.5% for a complaint probability, and the complaint probability score is a score of 1.
Illustratively, as shown in Table 1, the complaint probability steps include steps 1 to 99, where steps 1 to 85 each have a complaint rate of less than 1%, not shown in the table. Illustratively, complaint probability steps 93-99 correspond to high risk, and steps of complaint probability step 92 and below correspond to low risk. For example, a complaint rate corresponding to a complaint probability stage in which the ratio of the complaint rate to the complaint rate corresponding to the third sample set is greater than 5 may be determined as a preset value, that is, a complaint probability stage 93, that is, a complaint probability stage above, corresponds to a high risk.
In some embodiments, for a user corresponding to each test sample in the test set, sending a call request and/or a collection urging message to a terminal of the user according to a preset initial collection urging policy, obtaining a complaint rate and a refund rate corresponding to the test set, and adjusting the initial collection urging policy based on the complaint rate and the refund rate corresponding to the test set, a predetermined complaint rate threshold value and a predetermined refund rate threshold value, so as to obtain a target collection urging policy.
Note that the complaint rate threshold and the refund rate threshold are determined based on the control set. The comparison set is subjected to the collection according to a general collection policy, for example, any existing collection method can be adopted to obtain collection results, the complaint rate in the collection results is used as a complaint rate threshold, and the refund rate in the collection results is used as a refund rate threshold.
In some embodiments, the first sample set is input into a complaint probability information prediction model to obtain a second sample set, complaint rates of complaint probability steps corresponding to complaint probability information of samples in the second sample set are all larger than the preset value, and the second sample set is divided into a test set and a comparison set according to a preset proportion.
Illustratively, the first sample set includes a plurality of filtered user samples, such as M1 overdue users who select the last 2 months of history of experiencing 1 month of the full performance period of the enrolled. Each user sample carries a complaint parameter, e.g., a user has a complaint of 1 for 1 month after expiration of M1, and no complaint of 0.
Alternatively, the complaint probability prediction model may be a machine learning model, such as an ensemble learning algorithm xgboost.
Exemplary, test set: the control set may be 2:8 or 5: and 5, ensuring that the number of test sets reaches an evaluable number (more than hundred levels). And collecting the test set with a target collection promoting strategy, and performing no other intervention on the control set. Complaint rates and refund rates of the test and control sets were continuously observed. The adjustment of the refund policy was performed in combination with the expression of the complaint rate and the refund rate, as shown in table 2. The expected goal is finally achieved by continuous optimization: the complaint rate is obviously reduced, and the refund rate is unchanged or improved.
Therefore, the embodiment of the application can identify the complaint risk of the user according to the loan information of the user, and carry out customized collection on the user with high complaint risk by adopting the target collection policy so as to improve the user experience and reduce the complaint of the user while ensuring the refund rate.
The method embodiment of the present application is described in detail above in connection with fig. 2, and the apparatus embodiment of the present application is described in detail below in connection with fig. 3 to 4, it being understood that the apparatus embodiment corresponds to the method embodiment, and similar descriptions may refer to the method embodiment.
Fig. 3 is a schematic block diagram of an electronic device 300 according to an embodiment of the present application. As shown in fig. 3, the electronic device 300 includes:
a processing unit 310, configured to input loan information of a user with overdue behavior into a complaint probability information prediction model, and obtain complaint probability information of the user;
processing unit 310 is further configured to determine, based on the complaint probability information, whether the user is a high complaint risk user;
an obtaining unit 320, configured to obtain a target revenue-promoting policy when the user is a high complaint risk user, where the target revenue-promoting policy is used to indicate at least one of a frequency, a mode, and a speaking operation of promoting the user;
the processing unit 310 is further configured to perform a corresponding operation according to the target revenue-generating policy.
In some embodiments, the processing unit is specifically configured to:
determining a complaint probability information grading to which the complaint probability information belongs;
when the complaint rate corresponding to the complaint probability information grading is larger than a preset value, determining that the user is a high complaint risk user, wherein the complaint rate is the proportion of the number of users with complaint behaviors in the complaint probability information grading to the total number of users.
In some embodiments, the processing unit 310 is further configured to:
aiming at a user corresponding to each test sample in a test set, sending a call request and/or an acceptance message to a terminal of the user according to a preset initial acceptance policy to obtain a complaint rate and a refund rate corresponding to the test set;
based on the complaint rate and the refund rate corresponding to the test set, and a predetermined complaint rate threshold value and a predetermined refund rate threshold value, the initial collection policy is adjusted, and the target collection policy is obtained;
wherein the complaint rate threshold and the refund rate threshold are determined based on a control set.
In some embodiments, the processing unit 310 is specifically configured to:
inputting the first sample set into the complaint probability information prediction model to obtain a second sample set, wherein the complaint probability of the complaint probability classification corresponding to the complaint probability information of the samples in the second sample set is larger than the preset value;
dividing the second sample set into a test set and a control set according to a preset proportion.
In some embodiments, the processing unit 310 is further configured to:
determining complaint probability information corresponding to each sample in the third sample set;
dividing to obtain a plurality of complaint probability information grades based on complaint probability information corresponding to each sample in the third sample set, and obtaining complaint rate corresponding to each complaint probability information grade;
and determining the preset value based on the complaint rate corresponding to each complaint probability information grading and the complaint rate corresponding to the third sample set.
In some embodiments, the processing unit 310 is specifically configured to:
according to the target revenue-promoting strategy, sending a call request and/or a revenue-promoting message to the user terminal; or alternatively, the first and second heat exchangers may be,
the target receiving policy is sent to a service terminal, so that the service terminal sends a call request and/or a receiving urging message to a user terminal according to the target receiving policy;
the call request and the collect-urging message are used for prompting the user to pay.
The electronic device provided in the foregoing embodiment may implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and are not repeated herein.
Fig. 4 is a schematic structural diagram of an electronic device 400 according to an embodiment of the present application. The electronic device 400 as shown in fig. 4 comprises a processor 410, from which the processor 410 may call and run a computer program to implement the method in the embodiments of the present application.
Optionally, as shown in fig. 4, the electronic device 400 may also include a memory 420. Wherein the processor 410 may call and run a computer program from the memory 420 to implement the methods in embodiments of the present application.
Wherein the memory 420 may be a separate device from the processor 410 or may be integrated into the processor 410.
Optionally, as shown in fig. 4, the electronic device 400 may further include a transceiver 430, and the processor 410 may control the transceiver 430 to communicate with other devices, and in particular, may send information or data to other devices, or receive information or data sent by other devices.
Among other things, transceiver 430 may include a transmitter and a receiver. Transceiver 430 may further include antennas, the number of which may be one or more.
Optionally, the electronic device 400 may implement corresponding flows in the methods of the embodiments of the present application, which are not described herein for brevity.
It should be appreciated that the processor of an embodiment of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It will be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that the above memory is exemplary but not limiting, and for example, the memory in the embodiments of the present application may be Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), direct RAM (DR RAM), and the like. That is, the memory in embodiments of the present application is intended to comprise, without being limited to, these and any other suitable types of memory.
Embodiments of the present application also provide a computer-readable storage medium for storing a computer program.
Optionally, the computer readable storage medium may be applied to an electronic device in the embodiments of the present application, and the computer program causes a computer to execute corresponding processes in each method in the embodiments of the present application, which are not described herein for brevity.
Embodiments of the present application also provide a computer program product comprising computer program instructions.
Optionally, the computer program product may be applied to an electronic device in the embodiments of the present application, and the computer program instructions cause the computer to perform corresponding processes in the methods in the embodiments of the present application, which are not described herein for brevity.
The embodiment of the application also provides a computer program.
Optionally, the computer program may be applied to the electronic device in the embodiments of the present application, and when the computer program runs on a computer, the computer is caused to execute corresponding flows in the methods in the embodiments of the present application, which are not described herein for brevity.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus, device, and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. For such understanding, the technical solutions of the present application may be embodied in essence or in a part contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method of processing loan information, comprising:
inputting loan information of a user with overdue behaviors into a complaint probability information prediction model to obtain complaint probability information of the user;
determining whether the user is a high complaint risk user based on the complaint probability information;
when the user is a high complaint risk user, acquiring a target collection policy, wherein the target collection policy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user;
executing corresponding operation according to the target collecting strategy,
before sending a call request and/or a prompting message to a terminal of a user according to a preset initial prompting policy aiming at a user corresponding to each test sample in a test set, the method further comprises:
inputting the first sample set into the complaint probability information prediction model to obtain a second sample set, wherein the complaint probability of the complaint probability classification corresponding to the complaint probability information of the samples in the second sample set is larger than a preset value;
dividing the second sample set into a test set and a control set according to a preset proportion,
the method further comprises the steps of:
determining complaint probability information corresponding to each sample in the third sample set;
dividing to obtain a plurality of complaint probability information grades based on the complaint probability information corresponding to each sample in the third sample set, and obtaining the complaint rate corresponding to each complaint probability information grade;
determining the preset value based on the complaint rate corresponding to each complaint probability information grading and the complaint rate corresponding to the third sample set,
furthermore, the template collecting policy comprises attitude prompt information, the attitude prompt information is used for prompting the adoption of a moderate collecting attitude, and the concrete policy is formulated and adjusted according to the concrete form of the product and the historical complaint information in the collecting process.
2. The method of claim 1, wherein the determining whether the user is a high complaint risk user based on the complaint probability information comprises:
determining a complaint probability information grading to which the complaint probability information belongs;
and when the complaint rate corresponding to the complaint probability information grading is larger than a preset value, determining that the user is a high complaint risk user, wherein the complaint rate is the proportion of the number of users with complaint behaviors in the complaint probability information grading to the total number of users.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
aiming at a user corresponding to each test sample in a test set, sending a call request and/or an acceptance message to a terminal of the user according to a preset initial acceptance policy to obtain a complaint rate and a refund rate corresponding to the test set;
based on the complaint rate and the refund rate corresponding to the test set, and a predetermined complaint rate threshold value and a predetermined refund rate threshold value, the initial collection policy is adjusted, and the target collection policy is obtained;
wherein the complaint rate threshold and the refund rate threshold are determined based on a control set.
4. The method according to claim 1 or 2, wherein said performing the corresponding operation according to said target harvest-promoting policy comprises:
sending a call request and/or a charge-accelerating message to a user terminal according to the target charge-accelerating strategy; or alternatively, the first and second heat exchangers may be,
the target receiving policy is sent to a service terminal, so that the service terminal sends a call request and/or a receiving urging message to a user terminal according to the target receiving policy;
the call request and the collect-urging message are used for prompting the user to pay-off operation.
5. An electronic device, comprising:
the processing unit is used for inputting loan information of a user with overdue behaviors into the complaint probability information prediction model to obtain complaint probability information of the user;
the processing unit is further used for determining whether the user is a high complaint risk user or not based on the complaint probability information;
the acquiring unit is used for acquiring a target collection policy when the user is a high complaint risk user, wherein the target collection policy is used for indicating at least one of the frequency, the mode and the speaking operation of collecting the user;
the processing unit is further configured to perform a corresponding operation according to the target harvest policy,
before sending a call request and/or a prompting message to a terminal of a user according to a preset initial prompting policy aiming at a user corresponding to each test sample in a test set, the method further comprises the following steps:
inputting the first sample set into the complaint probability information prediction model to obtain a second sample set, wherein the complaint probability of the complaint probability classification corresponding to the complaint probability information of the samples in the second sample set is larger than a preset value;
dividing the second sample set into a test set and a control set according to a preset proportion,
determining complaint probability information corresponding to each sample in the third sample set;
dividing to obtain a plurality of complaint probability information grades based on the complaint probability information corresponding to each sample in the third sample set, and obtaining the complaint rate corresponding to each complaint probability information grade;
determining the preset value based on the complaint rate corresponding to each complaint probability information grading and the complaint rate corresponding to the third sample set,
furthermore, the template collecting policy comprises attitude prompt information, the attitude prompt information is used for prompting the adoption of a moderate collecting attitude, and the concrete policy is formulated and adjusted according to the concrete form of the product and the historical complaint information in the collecting process.
6. The device according to claim 5, characterized in that said processing unit is specifically configured to:
determining a complaint probability information grading to which the complaint probability information belongs;
and when the complaint rate corresponding to the complaint probability information grading is larger than a preset value, determining that the user is a high complaint risk user, wherein the complaint rate is the proportion of the number of users with complaint behaviors in the complaint probability information grading to the total number of users.
7. An electronic device, comprising: a processor and a memory for storing a computer program, the processor being adapted to invoke and run the computer program stored in the memory for performing the method according to any of claims 1 to 4.
8. A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110395748.6A CN112991049B (en) | 2021-04-13 | 2021-04-13 | Loan information processing method and electronic equipment |
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