CN111179051A - Financial target customer determination method and device and electronic equipment - Google Patents
Financial target customer determination method and device and electronic equipment Download PDFInfo
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- CN111179051A CN111179051A CN201911290914.5A CN201911290914A CN111179051A CN 111179051 A CN111179051 A CN 111179051A CN 201911290914 A CN201911290914 A CN 201911290914A CN 111179051 A CN111179051 A CN 111179051A
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Abstract
The disclosure relates to a financial target customer determination method, device, electronic equipment and computer readable medium. The method comprises the following steps: determining risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score. The financial target customer determining method, the financial target customer determining device, the electronic equipment and the computer readable medium can screen the target customer from multiple angles such as risk and profit, improve the passing rate of the whole financial service and improve the profit of a company.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a financial target customer determination method, apparatus, electronic device, and computer readable medium.
Background
With the development of economy, in order to meet the development requirement of the financial service institution, a personal user or an enterprise user often performs borrowing activities by the financial service institution, and the borrowing activities of the user are likely to bring risks to the financial service institution. Before the repayment deadline expires, the great adverse change of the financial business condition of a borrower (credit user) is likely to influence the performance capability of the borrower, so that risks such as bad account and bad account occur, therefore, in order to reduce the occurrence probability of such risks, a financial service institution needs to perform risk assessment on the borrower, and according to the risk assessment result, a financial service company can refuse to provide services for users with higher financial risk and unable to repay.
Currently, financial risk discrimination is often obtained by analyzing the credit risk of a user. Credit risk is also called counterparty risk or performance risk and refers to the risk that counterparty does not fulfill due debt. Credit risk, which is a major risk faced by the internet financial services industry, has been the core content of credit risk management. However, in practice, it is found that some customers cannot pay timely, but due to financial profits such as default money and interest, when the customers who pay the default money pay, the profits brought by the customers are higher than those of the customers who pay timely. Moreover, among the clients who are refused after the credit risk judgment, the profit brought by some clients can cover the risk, and profit income is brought to the financial service company. How to analyze such users and reasonably perform financial services on the users is a problem to be solved urgently.
Therefore, there is a need for a new financial target customer determination method, apparatus, electronic device, and computer readable medium.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a financial target customer determination method, device, electronic device and computer readable medium, which can filter target customers from multiple angles, such as risk and profit, to improve the throughput of the entire financial service and increase the profit of a company.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a financial target customer determination method, the method including: determining risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score.
Optionally, the method further comprises: training a first machine learning model through repayment information of a historical customer to generate the risk scoring model, wherein the risk scoring model is used for generating a risk score of the customer default repayment.
Optionally, the training of the first machine learning model by the repayment information of the historical customer includes: and training the gradient lifting decision tree model through repayment information of historical customers.
Optionally, the method further comprises: and training a second machine learning model through the income information of the historical client to generate the profit scoring model, wherein the profit scoring model is used for generating profit scores of the annual income brought by the client.
Optionally, training the second machine learning model with revenue information of the historical customer includes: and training the card-side automatic interactive detection decision tree model through the income information of the historical customers.
Optionally, extracting at least one client for which the risk score is greater than a threshold value comprises: determining the threshold value through historical empirical data; and comparing the risk scores of the plurality of customers to the threshold to extract the at least one customer.
Optionally, determining a target customer from the at least one customer based on the profit score includes: ranking the at least one customer according to their corresponding profit scores; and determining a target client from the at least one client based on the sequencing result and a preset condition.
Optionally, determining a target client from the at least one client based on the sorting result and a preset condition includes: determining an internal rate of return based on the risk expenditure and the cost expenditure; and sequentially taking the clients with profit scores larger than the internal profit rate as the target clients based on the sorting result.
Optionally, the method further comprises: and extracting at least one client with the risk score smaller than or equal to a threshold value, and determining the at least one client as a target client.
Optionally, the method further comprises: determining a financial services policy for the target customer based on the risk score and the profit score.
According to an aspect of the present disclosure, there is provided a financial target customer determination apparatus, the apparatus including: a risk module to determine risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers; an extraction module to extract at least one customer for which the risk score is greater than a threshold; a profit score for determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit score model; and a targeting module for determining a target customer from the at least one customer based on the profit score.
Optionally, the method further comprises: the first training module is used for training the first machine learning model through repayment information of historical customers to generate the risk scoring model, and the risk scoring model is used for generating risk scores of the customer default repayment.
Optionally, the first training module is further configured to train the gradient boosting decision tree model through repayment information of a historical customer.
Optionally, the method further comprises: and the second training module is used for training the second machine learning model through the income information of the historical client to generate the profit scoring model, and the profit scoring model is used for generating profit scores of annual income brought by the client.
Optionally, the second training module is further configured to train the chi-square automatic interaction detection decision tree model through revenue information of a historical customer.
Optionally, the extraction module includes: a threshold unit for determining the threshold value through historical empirical data; and a comparing unit for comparing the risk scores of the plurality of customers with the threshold value to extract the at least one customer.
Optionally, the target module includes: the sorting unit is used for sorting the at least one client according to the corresponding profit scores; and the target unit is used for determining a target client from the at least one client based on the sequencing result and a preset condition.
Optionally, the target unit is further configured to determine an internal rate of return based on the risk expenditure and the cost expenditure; and sequentially taking the clients with profit scores larger than the internal profit rate as the target clients based on the sorting result.
Optionally, the method further comprises: and the client module is used for extracting at least one client with the risk score smaller than or equal to a threshold value and determining the at least one client as a target client.
Optionally, the method further comprises: a policy module to determine a financial services policy for the target customer based on the risk score and the profit score.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the financial target customer determination method, the device, the electronic equipment and the computer readable medium, risk scores of a plurality of customers are determined based on financial information and a risk score model of the customers; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score, and screening the target customer from a plurality of angles such as risk and profit, thereby improving the passing rate of the whole financial service and increasing the profit of the company.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a system block diagram illustrating a financial target customer determination method and apparatus in accordance with an exemplary embodiment.
FIG. 2 is a flow chart illustrating a method of financial target customer determination according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating a method of financial target customer determination according to another exemplary embodiment.
FIG. 4 is a schematic diagram illustrating a financial target customer determination method according to another exemplary embodiment.
FIG. 5 is a schematic diagram illustrating a financial target customer determination method according to another exemplary embodiment.
FIG. 6 is a block diagram illustrating a financial target customer determination device, according to an example embodiment.
Fig. 7 is a block diagram illustrating a financial target customer determination device according to another example embodiment.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 9 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
In customer risk management, the establishment of a loan rejection rule generally only takes into account risk considerations. It has been found that the profit from some of the lending customers may cover the risk and generate revenue for the company. The inventor of the present disclosure considers that the establishment of the loan rejection rule not only considers the risk but also considers the profit brought by the client, and therefore, the establishment of the policy of providing financial services to the client should also take the profit brought by the client in the future as an important measure. The financial target client determination method and apparatus of the present disclosure are described in detail below with reference to specific embodiments.
FIG. 1 is a system block diagram illustrating a financial target customer determination method and apparatus in accordance with an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports financial services websites browsed by the user using the terminal apparatuses 101, 102, and 103. The background management server may analyze the received user data, and feed back a processing result (e.g., whether the user is a target user or not, whether the user is provided with a financial service) to an administrator of the financial service website.
The server 105 may also train the first machine learning model, for example, with historical customer repayment information, to generate the risk scoring model, which is used to generate a risk score for the customer's disqualified repayment.
The server 105 may also train a second machine learning model, such as through historical customer revenue information, to generate the profit scoring model used to generate profit scores for the annualized revenue brought by the customer.
The server 105 may be a server of one entity, and may also be composed of a plurality of servers, for example, a part of the servers 105 may be configured to train a first machine learning model through the repayment information of the historical customer, and generate the risk score model, where the risk score model is used to generate a risk score for the customer's disqualified repayment; a part of the server 105 can be used for training a second machine learning model through the income information of the historical client to generate the profit scoring model, and the profit scoring model is used for generating profit scores of annual income brought by the client; and a portion of the server 105 may be further operable to determine a target customer from the at least one customer based on the risk score and the profit score.
It should be noted that the financial target client determination method provided by the embodiment of the present disclosure may be executed by the server 105, and accordingly, the financial target client determination device may be provided in the server 105. And the web page end provided for the user to browse the financial service platform is generally positioned in the terminal equipment 101, 102 and 103.
FIG. 2 is a flow chart illustrating a method of financial target customer determination according to an exemplary embodiment. The financial target customer determination method 20 includes at least steps S202 to S208.
As shown in fig. 2, in S202, risk scores of a plurality of customers are determined based on financial information and risk score models of the plurality of customers. The first machine learning model can be trained through repayment information of historical customers to generate the risk scoring model, and the risk scoring model is used for generating risk scores of the customer default repayment.
In S204, at least one customer with a risk score greater than a threshold is extracted. The method comprises the following steps: determining the threshold value through historical empirical data; and comparing the risk scores of the plurality of customers to the threshold to extract the at least one customer.
In S206, a profit score is determined for the at least one customer based on the financial information and the profit scoring model for the at least one customer. The second machine learning model can be trained through income information of historical customers to generate the profit scoring model, and the profit scoring model is used for generating profit scores of annual income brought by the customers.
Further, the profit scoring model uses the actual IRR (internal rate of return), which is a continuous variable that not only provides more accurate information about the borrower but also includes information about whether the customer will default or not, as an index for measuring the profit that the customer will bring in the future, and is also the output data of the model. And selecting the financial information related to the user as an input variable, scoring the input user by using a profit scoring model, and outputting a predicted IRR corresponding to the predicted user.
In S208, a target customer is determined from the at least one customer based on the profit score. The method comprises the following steps: ranking the at least one customer according to their corresponding profit scores; and determining a target client from the at least one client based on the sequencing result and a preset condition.
In one embodiment, further comprising: and extracting at least one client with the risk score smaller than or equal to a threshold value, and determining the at least one client as a target client.
In one embodiment, further comprising: determining a financial services policy for the target customer based on the risk score and the profit score. The profit scoring model assists the risk scoring model, a rejection fishing strategy is generated for rejected customers, so that customers capable of performing financial services can be screened out from the rejected customers, and the transaction passing rate and the profit of a company can be improved in this way.
In one embodiment, the estimated annual risk of a client that may be repurposed by the risk model is 2 times that of a normally passing client, and assuming a normal passing client annual risk of 6%, the annual risk threshold for the repurposed client may be 12%. In this part of the loan rejection clients, if there is a client whose actual IRR is 33%, minus the annual risk 12% and the cost, this part of the client can still bring profit to the company operation.
According to the financial target customer determination method of the present disclosure, risk scores of a plurality of customers are determined based on financial information of the plurality of customers and a risk score model; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score, and screening the target customer from a plurality of angles such as risk and profit, thereby improving the passing rate of the whole financial service and increasing the profit of the company.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
FIG. 3 is a flow chart illustrating a method of financial target customer determination according to another exemplary embodiment. The flow shown in FIG. 3 is a detailed description of the step S208 "determining a target customer from the at least one customer based on the profit score" in the flow shown in FIG. 2.
As shown in FIG. 3, in S302, the at least one customer is ranked according to their corresponding profit scores.
In S304, an internal rate of return is determined based on the risk and cost expenditures.
And in S306, sequentially taking the clients with profit scores larger than the internal profit rate as the target clients based on the sorting result.
For the lending-refusing customer group, the actual IRR of the lending-refusing customer group is estimated by using a profit scoring model, the customers are ranked from large to small, the head customer group is selected to be fished back to pass, the fishing-back principle is that the actual IRR of the fishing-back customer group can cover risks and costs, for example, the actual IRR of the fishing-back customer group is 33%, the annual risk is reduced by 12% and the costs, and the part of customers can also bring profits to the operation of the company.
FIG. 4 is a schematic diagram illustrating a financial target customer determination method according to another exemplary embodiment. FIG. 4 is a detailed description of "training the first machine learning model with historical customer repayment information to generate the risk scoring model, which is used to generate a risk score for the customer's disqualified repayment".
In one embodiment, the gradient boosting decision tree model may be trained, for example, through historical customer's repayment information.
The GBDT (Gradient Boosting Decision Tree) is an iterative Decision Tree algorithm, which is composed of a plurality of Decision trees, and the conclusions of all the trees are accumulated to be used as a final answer. In one embodiment, GBDT is an algorithm that classifies or regresses data by analyzing and calculating repayment information of historical customers using an additive model (i.e., a linear combination of basis functions) and continuously reducing residuals generated by a training process. The GBDT finds an optimal function F, and the penalty function is biased to F each time to obtain a negative gradient, and the penalty function is minimized by fitting the negative gradient. The GBDT generates a weak classifier through multiple rounds of iteration, each round of iteration generates a weak classifier, each classifier is trained on the basis of the residual error of the last round of classifier, the weak classifier generally selects a CART tree, but the depth of each classification regression tree is not very deep, and the final risk score is obtained by weighting and summing the results of the weak classifiers obtained through each round of training.
FIG. 5 is a schematic diagram illustrating a financial target customer determination method according to another exemplary embodiment. FIG. 5 is a detailed description of "training the second machine learning model with the revenue information of the historical customers to generate the profit scoring model for generating profit scores for the annual revenue brought by the customers".
In one embodiment, the card-side automated interaction detection decision tree model may be trained, for example, through revenue information of historical customers.
The chi-squared automatic interaction detector (CHAID) is a tool used for finding the relationship between the basic information of a history customer and the corresponding income information. CHAID can be used for prediction as well as classification, and for detecting interactions between variables. In one embodiment, the sample is optimally divided according to the income information of a given historical user and the corresponding basic information thereof, and the automatic judgment grouping of the multivariate list is carried out according to the significance of chi-square test so as to divide the historical users into different groups, wherein each group corresponds to an income rate. The method can rapidly and effectively excavate main influencing factors by utilizing a chi-square automatic interaction detection method, can process nonlinear and highly-related data, can take missing values into account, and can overcome the limitations of the traditional parameter detection method in the aspects.
The classification process of the card-side automatic interaction detection method comprises the following steps: firstly, selecting classified reaction variables, then carrying out cross classification by using interpretation variables and the reaction variables to generate a series of two-dimensional classification tables, respectively calculating characteristic values of the two-dimensional classification tables, taking the two-dimensional table with the minimum characteristic value as an optimal initial classification table, continuously using the interpretation variables to classify the reaction variables on the basis of the optimal two-dimensional classification table, and repeating the process until the characteristic values are larger than a set threshold value with statistical significance.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
FIG. 6 is a block diagram illustrating a financial target customer determination device, according to an example embodiment. As shown in fig. 6, the financial target customer determination device 60 includes: risk module 602, extraction module 604, profit module 606, goal module 608.
The risk module 602 is configured to determine risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers;
an extraction module 604 for extracting at least one customer for which the risk score is greater than a threshold; the extraction module 604 includes: a threshold unit for determining the threshold value through historical empirical data; and a comparing unit for comparing the risk scores of the plurality of customers with the threshold value to extract the at least one customer.
The profit module 606 is for determining a profit score for the at least one customer based on the financial information of the at least one customer and the profit scoring model; and
the targeting module 608 is operable to determine a target customer from the at least one customer based on the profit score. The goal module 608 includes: the sorting unit is used for sorting the at least one client according to the corresponding profit scores; and the target unit is used for determining a target client from the at least one client based on the sequencing result and a preset condition. The target unit is further configured to determine an internal rate of return based on the risk expenditure and the cost expenditure; and sequentially taking the clients with profit scores larger than the internal profit rate as the target clients based on the sorting result.
Fig. 7 is a block diagram illustrating a financial target customer determination device according to another example embodiment. As shown in fig. 7, the financial target client determining means 70 includes: a first training module 702, a second training module 704, a client module 706, and a policy module 708.
The first training module 702 is configured to train the first machine learning model through the repayment information of the historical customer, and generate the risk scoring model, where the risk scoring model is used to generate a risk score of the customer's default repayment. The first training module 702 is further configured to train the gradient boosting decision tree model according to repayment information of the historical customer.
The second training module 704 is configured to train the second machine learning model according to the revenue information of the historical customer, and generate the profit scoring model, where the profit scoring model is configured to generate profit scores of the annual revenue brought by the customer. The second training module 704 is further configured to train the chi-square automatic interaction detection decision tree model according to the revenue information of the historical customer.
The client module 706 is configured to extract at least one client with the risk score less than or equal to the threshold, and determine the at least one client as a target client.
The policy module 708 is configured to determine a financial services policy for the target customer based on the risk score and the profit score.
According to the financial target customer determination apparatus of the present disclosure, risk scores of a plurality of customers are determined based on financial information of the plurality of customers and a risk score model; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score, and screening the target customer from a plurality of angles such as risk and profit, thereby improving the passing rate of the whole financial service and increasing the profit of the company.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 800 according to this embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code executable by the processing unit 810 to cause the processing unit 810 to perform steps according to various exemplary embodiments of the present disclosure described in the electronic prescription flow processing method section described above in this specification. For example, the processing unit 810 may perform the steps shown in fig. 2 and 3.
The memory unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM)8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The memory unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. The network adapter 860 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 9, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: determining risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers; extracting at least one customer for which the risk score is greater than a threshold; determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and determining a target customer from the at least one customer based on the profit score.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A financial target customer determination method, comprising:
determining risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers;
extracting at least one customer for which the risk score is greater than a threshold;
determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit scoring model; and
a target customer is determined from the at least one customer based on the profit score.
2. The method of claim 1, further comprising:
training a first machine learning model through repayment information of a historical customer to generate the risk scoring model, wherein the risk scoring model is used for generating a risk score of the customer default repayment.
3. The method of claims 1-2, wherein training the first machine learning model with historical customer payment information comprises:
and training the gradient lifting decision tree model through repayment information of historical customers.
4. The method of claims 1-3, further comprising:
and training a second machine learning model through the income information of the historical client to generate the profit scoring model, wherein the profit scoring model is used for generating profit scores of the annual income brought by the client.
5. The method of claims 1-4, wherein training the second machine learning model with historical customer revenue information comprises:
and training the card-side automatic interactive detection decision tree model through the income information of the historical customers.
6. The method of claims 1-5, wherein extracting at least one client for which the risk score is greater than a threshold value comprises:
determining the threshold value through historical empirical data; and
comparing the risk scores of the plurality of customers to the threshold to extract the at least one customer.
7. The method of claims 1-6, wherein determining a target customer from the at least one customer based on the profit score comprises:
ranking the at least one customer according to their corresponding profit scores;
and determining a target client from the at least one client based on the sequencing result and a preset condition.
8. A financial target customer determination device, comprising:
a risk module to determine risk scores for a plurality of customers based on financial information and a risk score model for the plurality of customers;
an extraction module to extract at least one customer for which the risk score is greater than a threshold;
a profit module for determining a profit score for the at least one customer based on the financial information of the at least one customer and a profit score model; and
a targeting module for determining a targeted customer from the at least one customer based on the profit score.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN201911290914.5A CN111179051A (en) | 2019-12-16 | 2019-12-16 | Financial target customer determination method and device and electronic equipment |
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