CN110659985A - Method and device for fishing back false rejection potential user and electronic equipment - Google Patents
Method and device for fishing back false rejection potential user and electronic equipment Download PDFInfo
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Abstract
When the potential user is retrieved for the credit worthiness service corresponding to the second service rule, the user meeting the first service rule and not meeting the second service rule is utilized to obtain user information with an earlier index and a newly-added index, and a model is constructed by combining the user information with the performance of the user in the first service, so that the potential influence of the newly-added index on the repayment performance of the users can be obtained, and the user information of the user can be processed by utilizing the model so as to retrieve the potential user for the second service rule. Because the method utilizes the existing sample, the period of fishing back potential users is shortened by fully utilizing the existing data.
Description
Technical Field
The application relates to the field of computers, in particular to a method, a device and electronic equipment for fishing back a potential user rejected by mistake.
Background
The credit worthiness assessment of the user is carried out substantially according to indexes which can affect the repayment performance of the user, a strategy and a rule for assessing the user are set, the repayment performance of the user is estimated by assessing whether the user meets preset indexes or not, and then how to grant credit worthiness to the user is selected.
Disclosure of Invention
The embodiment of the specification provides a method, a device and electronic equipment for fishing back a potential user rejected by mistake. The method is used for solving the problem that the fishing-back period of a potential user is long.
The application provides a method for fishing back a potential user rejected by mistake, which comprises the following steps:
acquiring user information, wherein the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first service rule and does not meet a second service rule;
constructing a salvage model by using the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
Optionally, the prior evaluation index is: an indicator considered by the first business rule or the second business rule;
the newly added evaluation indexes are as follows: an index that is not considered by the first business rule and the second business rule.
Optionally, the assessment result is a score;
the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result comprises the following steps:
and selecting potential users according to the order of the scores from high to low based on the preset head passenger group proportion.
Optionally, the method further comprises:
potential users with multi-head loan performance are rejected.
Optionally, the second business rule is a credit granting evaluation rule of a shared business relative to the first business rule, and the credit worthiness amount corresponding to the second business rule is higher than the credit worthiness amount corresponding to the first business rule.
Optionally, the constructing a salvage model by using the user information and the first business performance data of the user includes:
setting a tag value for the user information of the user according to the first service performance data;
and training the salvage model by using the user information of the user and the corresponding label value.
Optionally, the first business performance data includes at least one of overdue performance data and revenue performance data.
Optionally, the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result includes:
and if the evaluation result is that the service rule passes, fishing out the potential user for the service of the second service rule, and taking the fished-back potential user as a fishing-back user.
The application also provides a device for fishing back a potential user rejected by mistake, comprising:
the user information acquisition module is used for acquiring user information, wherein the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first service rule and does not meet a second service rule;
the salvage module is used for constructing a salvage model by utilizing the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
Optionally, the prior evaluation index is: an indicator considered by the first business rule or the second business rule;
the newly added evaluation indexes are as follows: an index that is not considered by the first business rule and the second business rule.
Optionally, the assessment result is a score;
the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result comprises the following steps:
and selecting potential users according to the order of the scores from high to low based on the preset head passenger group proportion.
Optionally, the bailing module is further configured to:
potential users with multi-head loan performance are rejected.
Optionally, the second business rule is a credit granting evaluation rule of a shared business relative to the first business rule, and the credit worthiness amount corresponding to the second business rule is higher than the credit worthiness amount corresponding to the first business rule.
Optionally, the constructing a salvage model by using the user information and the first business performance data of the user includes:
setting a tag value for the user information of the user according to the first service performance data;
and training the salvage model by using the user information of the user and the corresponding label value.
Optionally, the first business performance data includes at least one of overdue performance data and revenue performance data.
Optionally, the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result includes:
and if the evaluation result is that the service rule passes, fishing out the potential user for the service of the second service rule, and taking the fished-back potential user as a fishing-back user.
The present application further provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
In various embodiments described in this specification, when the potential user is fished back for the credit worthiness service corresponding to the second service rule, the user who satisfies the first service rule and does not satisfy the second service rule is used to obtain the user information with the advanced index and the new index, and the model is constructed by combining the performance of the user in the first service, so that the potential influence of the new index on the repayment performance of the users can be obtained, and then, the user information of the user is processed by using the model, so that the potential user can be fished back for the second service rule. Because the method utilizes the existing sample, the period of fishing back potential users is shortened by fully utilizing the existing data.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram illustrating a method for retrieving a false rejection potential user according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an apparatus for retrieving a false rejection potential user according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram illustrating a method for managing credit line based on a callback user according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a device for managing credit line based on a fish-back user according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 6 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
However, for the rejected users under the policy, even if the users are granted with the quota, some users capable of repayment still exist in the users, and a certain income is brought, but the users are not credited for the users in the prior art because the crediting has a certain risk cost, so that the users who do not pass the evaluation are directly regarded as the users rejecting the crediting in the prior art, even though some potential users capable of bringing the income still exist in the users.
In consideration of the other aspect, as the knowledge is developed and the evaluation means is deepened, some new evaluation indexes are generated, and the user is evaluated by using the new evaluation indexes.
However, these new indicators are only qualitative criteria. Quantitative criteria are needed for credit assessment of users, and when each new index is just started to be taken into consideration, the criteria are usually gradually released for loan to perform tentative repayment performance so as to obtain a balance point of risk and income under the index, and this way, the collection of samples is performed in a longer period, and the efficiency is low.
The new indexes are generated after a strategy is constructed, so that when the new indexes appear, some potential users can be fished back from the rejected users by combining the new indexes, but if the potential users are fished back by only gradually relaxing the standard form of the new indexes through probing, a longer period is still needed.
The applicant thinks that, for the refusing users, although the users are refused by the current policies and rules, the users still may be credited by other policies and rules, therefore, the users still have repayment performances which are actually influenced by a plurality of implicit indexes, wherein the repayment performances also include new indexes, and only the repayment performances are not realized to influence by the indexes during the loan, therefore, the repayment performances can still reflect the influence of the new indexes on the repayment performances of the users to a certain extent, if part of the potential users can be salvaged by the repayment performances and the new indexes, the profit is improved by increasing the number of the loan, and the period of the salvage of the potential users is shortened by fully utilizing the existing samples.
Therefore, an embodiment of the present specification provides a method for fishing back a potential user rejected by mistake, including:
acquiring user information, wherein the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first service rule and does not meet a second service rule;
constructing a salvage model by using the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
When the potential user is fished back for the credit worthiness service corresponding to the second service rule, the user information with the prior index and the newly added index is obtained by using the user which meets the first service rule and does not meet the second service rule, and a model is constructed by combining the performance of the user in the first service, so that the potential influence of the newly added index on the repayment performance of the users can be obtained, and the potential user can be fished back for the second service rule by using the model to process the user information of the user. Because the method utilizes the existing sample, the period of fishing back potential users is shortened by fully utilizing the existing data.
After the potential user is fished back, credit worthiness is often given to the potential user and the potential user is credited with the credit worthiness to make profit. The existing mode of granting the quota for the user mostly judges whether the user grants the corresponding quota for the user, or selects one from the two modes to grant the quota for the user according to a preset fixed quota, but the mode does not simultaneously satisfy the balance between the user requirement (larger quota) and the risk control (smaller quota), and actually does not consider how many quotas are granted for the user to achieve better profit.
Therefore, an embodiment of the present specification provides a method for managing credit lines based on a salvage user, including:
selecting a salvage user from the users who are evaluated to pass through the first business rule evaluation and rejected by the second business rule evaluation based on the constructed salvage model;
acquiring user information of the user;
different credit worthiness limits are granted to different users;
acquiring credit worthiness performance data of the user under different credit worthiness limits;
and constructing an quota model based on the user information of the user and the credit worthiness expression data, so that the quota model grants a quota for the user based on the user information.
The method comprises the steps of obtaining user information of a salvage user, granting different credit worthiness for different users, and showing credit worthiness performances under different credit worthiness for the users, so that a credit model is constructed based on the user information of the users and the credit worthiness performance data, after the model processes the user information, the number of credits granted for the users can be determined to generate better credit worthiness performances, the limit of fixed preset credits is avoided, and the consideration to income and risk control is good.
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
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.
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 term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for retrieving a false rejection potential user according to an embodiment of the present disclosure, where the method may include:
s101, obtaining user information, wherein the user information comprises an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first business rule and does not meet a second business rule.
In this embodiment, the user is a user who does not satisfy the second business rule and the first business rule is a rule that the user is rejected, which is equivalent to rejecting a business corresponding to the rule granted to the user, and therefore the second business is a business that we will return to a potential user.
The service may refer to a certain credit service line, or an application providing a credit product, or one of a plurality of credit products provided on a service platform, and the form of the second service is not specifically limited, and the first service may also be referred to.
The reason why the selected users are users who satisfy the first business rule but do not satisfy the second business rule is that although there is not a lot of payment data in the business line, some samples can still be obtained in other business lines (the first business), and therefore, potential users can be fished back by using the samples.
In the embodiment of the present specification, the second business rule is a credit granting evaluation rule for optimizing business with respect to the first business rule, and the credit worthiness corresponding to the second business rule is higher than the credit worthiness corresponding to the first business rule.
In practical applications, the premium service is generally a large-scale service, and the large-scale service is referred to as a general service. Since the premium service itself is set for a small number of people, the evaluation criteria is more strict and the number of users who can be credited is also small.
In this case, compared with the common service, collecting a large number of samples of the shared service to construct a salvage model of a potential user requires a long period, and particularly, when a new index is found, the index standard needs to be gradually relaxed, so that the salvage model of the potential user needs to be adjusted for many times. Therefore, in order to improve the sufficiency of user mining, a bailing model can be constructed by temporarily using samples and newly added indexes of common services.
In order to distinguish the newly added index from the index when the first service rule or the second service rule is constructed before, the earlier evaluation index and the newly added index can be used for distinguishing, and the earlier evaluation index is as follows: the new evaluation index of the considered index of the first business rule or the second business rule is as follows: an index that is not considered by the first business rule and the second business rule.
In the embodiment of the present specification, the form of the user information may be various, and because the user information implies factors that affect the repayment performance, the repayment performance may be estimated by analyzing the implicit factors, and because the form and attributes of the user information are various, in a conventional case, the user information is obtained in an index class corresponding to a certain rule, and in the embodiment of the present specification, we obtain not only the user information with an earlier index but also the user information with a newly added index, and the user information allows the subsequent evaluation to take into account the influence of the newly added index, so that the evaluation result is more comprehensive. Therefore, although the name of the user information is not changed in the embodiment of the present specification, the essence of the user information is different, and at least the basis for acquiring the user information is different, and the effect exhibited by the user information is also different.
S102: and constructing a fishing-back model by using the user information and the first service performance data of the user.
In this embodiment of the present specification, the first service performance data may be a performance of the user who satisfies the first service rule and does not satisfy the second service rule in S101 after being granted credit by the first service, which may be credit worthiness of the user, or a profit calculated by the platform according to the credit worthiness, such as an action and support rate, a profit rate, a repayment rate, a overdue rate, and the like, and is not specifically described herein.
Thus, optionally, the first business performance data includes at least one of overdue performance data, revenue performance data.
In the embodiment of the description, a fishing-back model can be constructed in a mode of training the model by using a supervised learning mode.
In order to enable the salvage model of the potential user to automatically learn the association between different values of the newly added index and different service performance data, a label value can be set for the user information of the user according to the first service performance data, and the user information with the previous index and the newly added index is used as a sample for training, so that the model can accurately estimate the service performance generated by the user when the user information is input into the model through training of a large number of samples.
Therefore, constructing a salvage model using the user information and the first business performance data of the user may include:
setting a tag value for the user information of the user according to the first service performance data;
and training the salvage model by using the user information of the user and the corresponding label value.
And S103, evaluating the user by utilizing the fishing-back model.
After obtaining the salvage model, the salvage can be carried out by using the model, the essence is to evaluate potential users, screen users meeting the conditions as salvaged users, and then grant credit worthiness related to the second service for the users.
S104: and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
When the potential user is retrieved for the credit worthiness service corresponding to the second service rule, the user information with the prior index and the newly-added index is obtained by using the user meeting the first service rule and not meeting the second service rule, and the model is established by combining the representation of the user in the first service, so that the potential influence of the newly-added index on the repayment representation of the users can be obtained, and the user information of the user can be processed by using the model to retrieve the potential user for the second service rule. Because the method utilizes the existing sample, the period of fishing back potential users is shortened by fully utilizing the existing data.
In the embodiments of the present specification, the evaluation result may be a score;
thus, after a large number of users are evaluated to obtain scores, the scores have a magnitude order, and the former users can be called head guest group users, and a quota ratio can be set to filter the head guest group users.
Therefore, in the case that the evaluation result may be a score, fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result may include:
and selecting potential users according to the order of the scores from high to low based on the preset head passenger group proportion.
In this embodiment of the present specification, the evaluation result may also be a qualitative result, for example, whether the user passes the evaluation, and therefore, fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result may include:
and if the evaluation result is that the service rule passes, fishing out the potential user for the service of the second service rule, and taking the fished-back potential user as a fishing-back user.
In an embodiment of the present specification, in consideration of fraudulent behavior of blacklists and multi-head loans, in an embodiment of the present specification, the method may further include:
potential users with multi-head loan performance are rejected.
Based on the same inventive concept, the embodiment of the specification further provides a device for fishing back a potential user rejected by mistake.
Fig. 2 is a schematic structural diagram of an apparatus for retrieving a false rejection potential user according to an embodiment of the present disclosure, where the apparatus may include:
a user information obtaining module 201, configured to obtain user information, where the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who satisfies a first service rule and does not satisfy a second service rule;
a salvage module 202, which constructs a salvage model by using the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
For the concrete way, principle and effect of the steps performed by the device, reference may be made to the discussion of the embodiment in fig. 1.
As an implementation manner, the prior evaluation index is: an indicator considered by the first business rule or the second business rule;
the newly added evaluation indexes are as follows: an index that is not considered by the first business rule and the second business rule.
Wherein, as an embodiment, the evaluation result is a score;
the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result comprises the following steps:
and selecting potential users according to the order of the scores from high to low based on the preset head passenger group proportion.
Wherein, as an implementation manner, the bailing module 202 is further configured to:
potential users with multi-head loan performance are rejected.
As an implementation manner, the second business rule is a credit granting evaluation rule of a preferred business relative to the first business rule, and the credit worthiness amount corresponding to the second business rule is higher than the credit worthiness amount corresponding to the first business rule.
As an embodiment, the constructing a salvage model by using the user information and the first service performance data of the user may include:
setting a tag value for the user information of the user according to the first service performance data;
and training the salvage model by using the user information of the user and the corresponding label value.
Wherein, as an implementation manner, the first business performance data comprises at least one of overdue performance data and income performance data.
As an embodiment, the fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result may include:
and if the evaluation result is that the service rule passes, fishing out the potential user for the service of the second service rule, and taking the fished-back potential user as a fishing-back user.
When the device shown in fig. 2 salvages back a potential user by mistake, when the potential user is salvaged back for the credit worthiness service corresponding to the second service rule, the user who meets the first service rule but does not meet the second service rule is used to obtain the user information with the prior index and the new index, and the model is constructed by combining the representation of the user in the first service, so that the potential influence of the new index on the repayment representation of the users can be obtained, and the user information of the user can be processed by using the model, so that the potential user can be salvaged back for the second service rule. Because the method utilizes the existing sample, the period of fishing back potential users is shortened by fully utilizing the existing data.
After the potential users are fished back, on one hand, the users are users meeting the first business rule and still performing better after being evaluated by combining the newly added indexes, and on the other hand, the users are rejected by the second business rule when the newly added indexes are considered, so that the credit worthiness corresponding to the second business rule directly granted to the users still has certain risk.
However, how many credit lines are granted to the user is needed, which needs to determine the credit line granted to the user and design a method for managing the credit line.
The existing mode of granting the quota for the user mostly judges whether the user grants the corresponding quota for the user, or selects one from the two modes to grant the quota for the user according to a preset fixed quota, but the mode does not simultaneously satisfy the balance between the user requirement (larger quota) and the risk control (smaller quota), and actually does not consider how many quotas are granted for the user to achieve better profit.
For ease of understanding, it may be assumed that prior art evaluations found that a user could have better performance of funds if granted 4 million credit to the user, but this approach had no consideration for increasing credit, and if at a higher credit, the user could still have better credit standing performance, which could increase revenue, even if credit standing performance was slightly worse, as long as the additional revenue could be greater than the additional loss caused by poor credit standing performance, in which case granting this larger credit to the user could still increase revenue as a whole. This is distinguished from conventional approaches, essentially: whether a user can be granted a certain pre-set amount, and how much more beneficial, or even most beneficial, the amount granted to the user (contemplated by the illustrated embodiment).
For the purpose of elaboration, the method is illustrated by way of example in fig. 3.
Fig. 3 is a schematic diagram illustrating a method for managing credit line based on a salvage back user according to an embodiment of the present disclosure, where the method may include:
s301: and selecting a fishing-back user from the users which are passed by the first business rule evaluation and rejected by the second business rule evaluation based on the constructed fishing-back model.
In this embodiment, the salvage model may be constructed according to user information having an advanced index and a new index, where the advanced evaluation index is: the new evaluation index of the considered index of the first business rule or the second business rule is as follows: an index that is not considered by the first business rule and the second business rule.
In the embodiment of the present specification, the second business rule is a credit granting evaluation rule for optimizing business with respect to the first business rule, and the credit worthiness corresponding to the second business rule is higher than the credit worthiness corresponding to the first business rule.
In this case, a large number of samples can be obtained by using the users who are passed by the first business rule evaluation and rejected by the second business rule evaluation, and the problem of long period existing in the mode of collecting the users meeting the second business rule as the samples is solved.
S302: and acquiring the user information of the user.
In this embodiment of the present specification, the obtaining of the user information of the user may be obtaining of user information having an advanced index and a newly added index, so that the consideration factor is more comprehensive.
After obtaining the user information of the users, the credit line can be granted for collecting the sample.
S303: different credit worthiness is granted to different users.
The credit standing amount is granted to the user, and the credit standing expressions under different amounts can be obtained through collecting data subsequently, so that the difference of the credit standing amount is reflected on the credit standing expressions.
In this embodiment, the granting different credit worthiness limits for different users may include:
and randomly grouping the users, and respectively granting credit worthiness to the users in each group, so that the users in different groups have different credit worthiness, and the users in the same group have the same credit worthiness.
The users in each group are approximately the same through random grouping, so the difference of credit worthiness representation data of the users in different groups is influenced by the difference of credit worthiness to a greater extent.
In the embodiment of the specification, considering the condition that the amount corresponding to the second service is higher than the amount corresponding to the first service and the passing condition of the salvage user in the first service rule and the second service rule, the condition can be considered
The credit line granted to the users in each group is between the line corresponding to the second service and the line corresponding to the first service, so that the granted line is more reasonable.
Thus, optionally, granting different credit worthiness to different users may include:
and the credit worthiness corresponding to the first business rule is taken as a lower limit credit, the credit worthiness corresponding to the second business rule is taken as an upper limit credit, and different credit worthiness are granted to different users to be credited and returned.
And under the condition of randomly grouping the users, respectively granting credit worthiness to the users in each group, which may include:
and the credit worthiness corresponding to the first business rule is taken as a lower limit credit, the credit worthiness corresponding to the second business rule is taken as an upper limit credit, different credit worthiness are granted to the users to be credited back in different groups, so that the credit worthiness of the users in the same group is the same, and the credit worthiness of the users in different groups is different.
Under the condition that the fishing-back users are divided into a plurality of groups, credit line granted by the users in each group have a line gradient, so that the difference between the line corresponding to the second service and the line corresponding to the first service is refined through the line gradient and a plurality of lines, so that the plurality of groups of users express credit standing data under a plurality of credit standing lines, the relevance between the credit standing data and the credit standing lines is stronger, and better credit standing lines corresponding to the credit standing data can be obtained.
S304: and acquiring credit worthiness performance data of the user under different credit worthiness limits.
In embodiments herein, the credit worthiness performance data may include a rate of return.
This is due to the practical application scenario: the sensitivity of profit and risk credits are considered differently. That is, the credit amount is increased, the interest amount and the risk are increased, but the risk is less increased within a certain range, and at this time, the credit amount is increased, so that better total income can be brought.
In the embodiment of the present specification, the credit worthiness representation data may also include overdue risk data and the like, which represent a relationship between the overdue condition and the credit worthiness, and of course, the credit worthiness representation data may also be data in other forms, which can be considered from the perspective of overdue risk and income, and is not specifically described and limited herein.
S304: and constructing an quota model based on the user information of the user and the credit worthiness expression data, so that the quota model grants a quota for the user based on the user information.
The user information of the salvaged user is obtained, different credit worthiness are granted to different users, and the user presents credit worthiness performances under different credit worthiness, so that a credit model is constructed based on the user information of the user and the credit worthiness performance data, after the model processes the user information, the number of credits granted to the user can be determined to generate better credit worthiness performances, the limit of a fixed preset credit is avoided, and the compromise of income and risk control is improved.
In the embodiment of the present specification, in order to enable the limit model to automatically learn the association between the user information of the new indicator and the credit worthiness performance data under different credit worthiness, and then obtain the credit worthiness associated with better credit worthiness performance data according to the user information, a label may be set according to the credit worthiness performance data under different credit worthiness, and training may be performed with the user information as a sample.
Therefore, optionally, the constructing an quota model based on the user information of the user and the credit worthiness data may include:
setting labels according to credit standing data under different credit standing limits, and training a limit model by taking the user information of the user as a sample.
In this embodiment of the present specification, in order to continuously modify the credit line model so that it is more accurate when performing credit line granting, after obtaining the credit line model, the granting of different credit line credits to different users may include:
and using the limit credit granting model to grant different credit worthiness for different users.
Therefore, credit standing data of the user under different credit standing limits can be obtained again, and the limit model is corrected.
In an application scenario, the profit margin is used as a label to construct a margin model, so that the credit worthiness which enables the whole operation profit to be maximized can be obtained for the user evaluation, even if the value of the margin is not artificially set in the process of constructing the margin model, the method does not use a fixed margin as the user to evaluate and grant the credit worthiness any more, so that the margin grant can give consideration to both the profit and the risk at the same time, and the overdue risk is not paid attention.
Based on the same concept, the embodiment of the specification further provides a device for managing the credit line based on the salvage user.
Fig. 4 is a schematic structural diagram of an apparatus for managing credit line based on a fish-back user according to an embodiment of the present disclosure, where the apparatus may include:
the performance data obtaining module 401 selects a salvage user from the users who are assessed to pass the first business rule and are assessed to be rejected by the second business rule based on the constructed salvage model, obtains the user information of the user, grants different credit worthiness for different users, and obtains credit worthiness performance data of the user under different credit worthiness.
And the line credit granting module 402 is used for constructing a line model based on the user information of the user and the credit standing data, so that the line model grants a line for the user based on the user information.
For the concrete way, principle and effect of the steps performed by the device, reference may be made to the discussion of the embodiment in fig. 3.
Wherein, as an embodiment, the credit worthiness performance data comprises a rate of return.
As an implementation manner, the salvage model is constructed according to user information with an advanced index and a newly added index, and the advanced evaluation index is as follows: an indicator considered by the first business rule or the second business rule;
the newly added evaluation indexes are as follows: an index that is not considered by the first business rule and the second business rule.
As an implementation manner, the credit worthiness amount corresponding to the second business rule is higher than the credit worthiness amount corresponding to the first business rule.
As an implementation manner, the granting different credit worthiness limits for different users includes:
and randomly grouping the users, and respectively granting credit worthiness to the users in each group, so that the users in different groups have different credit worthiness, and the users in the same group have the same credit worthiness.
As an implementation manner, granting different credit worthiness to different users may include: and the credit worthiness corresponding to the first business rule is taken as a lower limit credit, the credit worthiness corresponding to the second business rule is taken as an upper limit credit, and different credit worthiness are granted to different users to be credited and returned.
And under the condition of randomly grouping the users, respectively granting credit worthiness to the users in each group, which may include:
and the credit worthiness corresponding to the first business rule is taken as a lower limit credit, the credit worthiness corresponding to the second business rule is taken as an upper limit credit, different credit worthiness are granted to the users to be credited back in different groups, so that the credit worthiness of the users in the same group is the same, and the credit worthiness of the users in different groups is different.
In the embodiment of the present specification, in order to enable the limit model to automatically learn the association between the user information of the new indicator and the credit worthiness performance data under different credit worthiness, and then obtain the credit worthiness associated with better credit worthiness performance data according to the user information, a label may be set according to the credit worthiness performance data under different credit worthiness, and training may be performed with the user information as a sample.
As an implementation manner, the constructing a credit model based on the user information of the user and the credit worthiness data includes:
setting labels according to credit standing data under different credit standing limits, and training a limit model by taking the user information of the user as a sample.
As an implementation manner, after obtaining the credit line model, the granting different credit line for different users includes:
and using the limit credit granting model to grant different credit worthiness for different users.
When the device shown in fig. 4 manages the credit line, different credit line amounts are granted to different users by acquiring the user information which is retrieved back to the user, so that the users present credit standing under different credit line amounts, and thus, a line model is constructed based on the user information of the users and the credit standing data.
In an application scenario, the profit margin is used as a label to construct a margin model, so that the credit worthiness which enables the whole operation profit to be maximized can be obtained for the user evaluation, even if the value of the margin is not artificially set in the process of constructing the margin model, the method does not use a fixed margin as the user to evaluate and grant the credit worthiness any more, so that the margin grant can give consideration to both the profit and the risk at the same time, and the overdue risk is not paid attention.
Based on the same inventive concept as the method described in any of the schematic diagrams of fig. 1 or fig. 3, the embodiment of the present specification further provides an electronic device.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 that couples various system components including the memory unit 520 and the processing unit 510, a display unit 540, and the like.
Wherein the storage unit stores program code that can be executed by the processing unit 510 such that the processing unit 510 performs the steps according to various exemplary embodiments of the present invention described in the above-mentioned processing method section of the present specification. For example, the processing unit 510 may perform the steps as shown in any of the schematic diagrams of fig. 1 or fig. 3.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The memory unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 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 500 may also communicate with one or more external devices 600 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 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 560. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with the electronic device 500, 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 of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: such as the method shown in fig. 1 or fig. 3.
Fig. 6 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
A computer program implementing the method shown in fig. 1 or fig. 3 may be stored on one or more computer readable media. The computer 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 aspects of the present invention 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).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for fishing back a potential user rejected by mistake is characterized by comprising the following steps:
acquiring user information, wherein the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first service rule and does not meet a second service rule;
constructing a salvage model by using the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
2. The method of claim 1, wherein the prior evaluation index is: an indicator considered by the first business rule or the second business rule;
the newly added evaluation indexes are as follows: an index that is not considered by the first business rule and the second business rule.
3. The method of any one of claims 1-2, wherein the assessment result is a score;
the dragging back of the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result comprises the following steps:
and selecting potential users according to the order of the scores from high to low based on the preset head passenger group proportion.
4. The method according to any one of claims 1 to 3, wherein the second business rule is a credit granting evaluation rule for a preferred business relative to the first business rule, and the credit worthiness corresponding to the second business rule is higher than the credit worthiness corresponding to the first business rule.
5. The method according to any one of claims 1-4, wherein the constructing a salvage model using the user information and the first business performance data of the user comprises:
setting a tag value for the user information of the user according to the first service performance data;
and training the salvage model by using the user information of the user and the corresponding label value.
6. The method of any of claims 1-5, wherein the first business performance data comprises at least one of overdue performance data and revenue performance data.
7. The method according to any one of claims 1-2, wherein the salvaging potential users for the credit worthiness service corresponding to the second service rule based on the evaluation result comprises:
and if the evaluation result is that the service rule passes, fishing out the potential user for the service of the second service rule, and taking the fished-back potential user as a fishing-back user.
8. An apparatus for salvaging a false reject potential user, comprising:
the user information acquisition module is used for acquiring user information, wherein the user information has an advanced evaluation index and a newly-added evaluation index, and the user is a user who meets a first service rule and does not meet a second service rule;
the salvage module is used for constructing a salvage model by utilizing the user information and the first service performance data of the user;
evaluating a user using the bailing model;
and fishing back the potential user for the credit worthiness service corresponding to the second service rule based on the evaluation result.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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