CN111861738A - Wind control rule screening method and device, computer equipment and storage medium - Google Patents
Wind control rule screening method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, computer equipment and a storage medium for screening wind control rules, wherein the method comprises the steps of naming variable names of all rules of a credit evaluation model one by one to be consistent with the names of all rules, taking all rule variables as independent variables, taking lended expression variables as dependent variables, and generating a one-dimensional vector; generating a new one-dimensional vector for each entry of the credit evaluation model; if one or more rules are hit in the incoming condition of the credit evaluation model, assigning 1 to the hit rule variable and assigning 0 to the missed variable in the new one-dimensional vector; if the loan is overdue after the piece is input, assigning 1 to the post-loan expression variable in the new one-dimensional vector, and otherwise, assigning 0; and calculating the weight parameters of all rules of the credit evaluation model by using a logistic regression model, and removing the rules of which the weight parameters are smaller than a preset value from the credit evaluation model. The invention can more effectively screen out the really effective wind control rules.
Description
Technical Field
The present invention relates to a rule screening method, an apparatus, a computer device and a storage medium, and more particularly, to a method, an apparatus, a computer device and a storage medium for screening a wind control rule.
Background
The wind control capability is the core competitiveness of credit industry practitioner agencies. The credit industry wind control system generally consists of a rule engine and a rating card. The rule engine consists of dozens to hundreds of unequal wind control rules and is used for evaluating the repayment willingness of the applicant. The scoring card outputs a scoring rating for evaluating the repayment ability of the applicant. After the applicant submits the application, the rules engine screens the incoming pieces, the incoming pieces of any rule (such as a blacklist) in the hit engine are directly rejected, and the incoming pieces of the missed rule are processed by the scoring card. The scoring card outputs different scoring grades, the low-grade represents that the repayment capacity is weak, the system directly rejects, the high-grade represents that the repayment capacity is strong, the system directly passes, the middle-grade represents that the repayment capacity is uncertain, the credit examiner performs manual credit examination, and whether the credit examiner passes or not is determined in a mode of checking the information of the applicant.
In actual work, the wind control rules are generally set by risk policy personnel, and in order to cover cheating people as much as possible (i.e. people without repayment will be screened), the rules are more and more, and the maintenance cost is higher and higher. More importantly, the subjective stacking rules can cause mistaken killing of incoming articles which are guided with high cost, and a method for scientifically evaluating the effectiveness of the rules is urgently needed by a wind control system.
At present, no simple and efficient method for rule screening exists in the whole industry. A tedious and costly approach is to decompose the rule engine into multiple subsystems, all of which embed a different rule. Different incoming pieces are randomly led into each subsystem, the incoming pieces hit the embedded rule of a certain subsystem and are not rejected, and the incoming pieces are processed by a wind control scoring card or a manual credit and review of the next process. And evaluating the effectiveness of the rules by counting the performance of the evaluation card or the post-credit performance of the documents approved by the manual credit and audit, namely judging that the relevant rules are effective rules if the documents entering a certain subsystem show a supernormal overdue proportion, and otherwise, judging that the relevant rules are ineffective. In the method, after a subsystem passes enough incoming parts, the judgment of the embedded rule of the subsystem has statistical significance, the demand for testing the incoming parts is large, and the more the rules, the larger the demand for testing the incoming parts is. In summary, this approach requires a large number of test entries, and may pass many potentially fraudulent entries that could be rejected by the rules engine, with a large impact on performance.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a more effective wind control rule screening method, a more effective wind control rule screening device, a computer device and a storage medium are provided.
In order to solve the technical problems, the invention adopts the technical scheme that: a method for screening wind control rules comprises the following steps,
s10, naming the variable names of each rule of the credit evaluation model one by one to be consistent with the names of the rules, taking each rule variable as an independent variable, taking the credit expression variable as a dependent variable, and generating a one-dimensional vector;
s20, generating a new one-dimensional vector for each entry of the credit evaluation model;
s30, if one or more rules are hit in the incoming condition of the credit evaluation model, assigning 1 to the hit rule variable and assigning 0 to the miss variable in the new one-dimensional vector;
s40, if the loan is overdue, assigning 1 to the post-loan expression variable in the new one-dimensional vector, and otherwise, assigning 0;
s50, calculating the weight parameters of each rule of the credit evaluation model by using a logistic regression model;
and S60, according to the weight parameters of each rule of the credit evaluation model, eliminating the rule with the weight parameter smaller than the preset value from the credit evaluation model.
Further, in step S50, the logistic regression model is preset with a loss function of the L1 regularization term.
Further, the step S50 specifically includes,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
Further, the step S60 specifically includes,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
The invention also provides a wind control rule screening device, which comprises,
the variable generation module is used for naming the variable names of the rules of the credit evaluation model one by one to be consistent with the names of the rules, taking the rule variables as independent variables, taking the credited expression variables as dependent variables and generating a one-dimensional vector;
the system comprises a component feeding module, a component extracting module and a component analyzing module, wherein the component feeding module is used for generating a new one-dimensional vector for each component fed by a credit evaluation model;
the rule variable assignment module is used for assigning 1 to a hit rule variable and assigning 0 to a missed rule variable in a new one-dimensional vector if one or more rules are hit in the incoming condition of the credit evaluation model;
the post-loan performance variable assignment module is used for assigning 1 to the post-loan performance variable in a new one-dimensional vector if the post-loan is overdue, and assigning 0 if the post-loan is overdue;
the logistic regression module is used for calculating the weight parameters of all rules of the credit evaluation model by using the logistic regression model;
and the rule removing module is used for removing the rules with the weight parameters smaller than the preset value from the credit evaluation model according to the weight parameters of all the rules of the credit evaluation model.
Further, in the logistic regression module, a logistic regression model is preset with a loss function of an L1 regularization term.
Further, the logistic regression module is specifically configured to,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
Further, the rule culling module is specifically configured to,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
The invention also provides computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes the wind control rule screening method when executing the computer program.
The invention also provides a storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for screening the wind control rule can be realized.
The invention has the beneficial effects that: the evaluation and screening task of the wind control rule is considered as a logistic regression model, the L1 regularization sparsification characteristic of the model objective function is utilized, the unimportant rule weight is automatically iterated to be infinitely close to zero in the process of solving the weight of the rule variable, the rule evaluation process is simplified, the rule with the weight parameter smaller than the preset value is removed from the credit evaluation model, the really acting rule is left, the condition that the input piece hits the unimportant rule and is rejected by the credit evaluation model is avoided, the method greatly reduces the input piece quantity of the required test, and the potential overdue cost is reduced.
Drawings
The following detailed description of the invention refers to the accompanying drawings.
FIG. 1 is a flow chart of a method for screening wind control rules according to an embodiment of the present invention;
FIG. 2 is a block diagram of a wind control rule screening apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a computer apparatus of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, a first embodiment of the present invention is: a method for screening wind control rules comprises the following steps,
s10, naming the variable names of each rule of the credit evaluation model one by one to be consistent with the names of the rules, taking each rule variable as an independent variable, taking the credit expression variable as a dependent variable, and generating a one-dimensional vector;
s20, generating a new one-dimensional vector for each entry of the credit evaluation model;
s30, if one or more rules are hit in the incoming condition of the credit evaluation model, assigning 1 to the hit rule variable and assigning 0 to the miss variable in the new one-dimensional vector;
s40, if the loan is overdue, assigning 1 to the post-loan expression variable in the new one-dimensional vector, and otherwise, assigning 0;
s50, calculating the weight parameters of each rule of the credit evaluation model by using a logistic regression model;
and S60, according to the weight parameters of each rule of the credit evaluation model, eliminating the rule with the weight parameter smaller than the preset value from the credit evaluation model.
Further, in step S50, the logistic regression model is preset with a loss function of the L1 regularization term.
Further, the step S50 specifically includes,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
Further, the step S60 specifically includes,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
In the embodiment, the evaluation and screening task of the wind control rule is considered as a logistic regression model, the L1 regularization sparsification characteristic of the model objective function is utilized, the unimportant rule weight is automatically iterated to be infinitely close to zero in the process of solving the weight of the rule variable, the rule evaluation process is simplified, the rule with the weight parameter smaller than the preset value is removed from the credit evaluation model, the really acting rule is left, the condition that the unimportant rule is rejected by the credit evaluation model due to the fact that the incoming piece hits the unimportant rule is avoided, the required test piece entering amount is greatly reduced, and the potential overdue cost is reduced.
By the embodiment, the problem of evaluating the effectiveness of various rules in the credit evaluation model in the industry can be solved, for example, a rule in a credit evaluation model rejects a piece, and since the piece is not shown after being credited, whether the rule is effective or not cannot be known, that is, whether the piece is killed by mistake or not cannot be judged. The more rules of a credit evaluation model, the more rejected incoming articles are possible, the more rules are determined to reject the true rejected incoming articles, and the false killing proportion of the rules is too high. Specifically, the argument x _ i of a wind control rule (i represents the ith rule) represents the dependent variable y after the loan is overdue. When overdue is not overdue, y takes a value of 0, and when overdue is overdue, y takes a value of 1. In order to quantify rule evaluation, the rule evaluation problem is converted into the size problem among independent variable weights in a logistic regression model, namely, the larger the absolute value of an independent variable weight is, the higher the importance or effectiveness of the corresponding related rule is, after a period of logistic regression machine learning iteration, the weight parameters of each rule can be obtained, rules with weights less than 0.01 are removed from the model, and the purpose of rule screening optimization is achieved.
Referring to fig. 2, another embodiment of the present invention is: a wind control rule screening device comprises a wind control rule screening device,
the variable generation module 10 is configured to name the variable names of the rules of the credit evaluation model one by one to be consistent with the names of the rules, use the rule variables as independent variables, use the credited expression variables as dependent variables, and generate a one-dimensional vector;
a component entering module 20, configured to generate a new one-dimensional vector for each component entering the credit evaluation model;
a rule variable assignment module 30, configured to assign 1 to a hit rule variable and assign 0 to a missed rule variable in a new one-dimensional vector if one or more rules are hit in the incoming condition of the credit evaluation model;
the post-loan performance variable assignment module 40 is used for assigning 1 to the post-loan performance variable in a new one-dimensional vector if the post-loan is overdue, and assigning 0 if the post-loan is overdue;
a logistic regression module 50, configured to calculate a weight parameter of each rule of the credit evaluation model by using a logistic regression model;
and a rule removing module 60, configured to remove, from the credit evaluation model, a rule whose weight parameter is smaller than a preset value according to the weight parameter of each rule of the credit evaluation model.
Further, in the logistic regression module 50, the logistic regression model is preset with a loss function of the L1 regularization term.
Further, the logistic regression module 50 is specifically configured to,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
Further, the rule culling module 60 is specifically configured to,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation process of the wind control rule screening apparatus may refer to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, no further description is provided herein.
The above-mentioned wind control rule screening means may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a terminal or a server, where the terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 3, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 comprises program instructions that, when executed, cause the processor 502 to perform a method of wind control rule screening.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be enabled to execute a method for filtering the wind control rule.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run a computer program 5032 stored in the memory to implement the above method for screening the wind control rule.
It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program includes program instructions, and the computer program may be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program comprises program instructions. The program instructions, when executed by the processor, cause the processor to perform the method of wind control rule screening as above.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A wind control rule screening method is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s10, naming the variable names of each rule of the credit evaluation model one by one to be consistent with the names of the rules, taking each rule variable as an independent variable, taking the credit expression variable as a dependent variable, and generating a one-dimensional vector;
s20, generating a new one-dimensional vector for each entry of the credit evaluation model;
s30, if one or more rules are hit in the incoming condition of the credit evaluation model, assigning 1 to the hit rule variable and assigning 0 to the miss variable in the new one-dimensional vector;
s40, if the loan is overdue, assigning 1 to the post-loan expression variable in the new one-dimensional vector, and otherwise, assigning 0;
s50, calculating the weight parameters of each rule of the credit evaluation model by using a logistic regression model;
and S60, according to the weight parameters of each rule of the credit evaluation model, eliminating the rule with the weight parameter smaller than the preset value from the credit evaluation model.
2. The method for screening wind control rules according to claim 1, wherein: in step S50, the logistic regression model is preset with a loss function of the L1 regularization term.
3. The wind control rule screening method according to claim 2, wherein: the step S50 specifically includes the steps of,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
4. The wind control rule screening method according to claim 3, wherein: the step S60 specifically includes the steps of,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
5. The utility model provides a wind accuse rule sieving mechanism which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the variable generation module is used for naming the variable names of the rules of the credit evaluation model one by one to be consistent with the names of the rules, taking the rule variables as independent variables, taking the credited expression variables as dependent variables and generating a one-dimensional vector;
the system comprises a component feeding module, a component extracting module and a component analyzing module, wherein the component feeding module is used for generating a new one-dimensional vector for each component fed by a credit evaluation model;
the rule variable assignment module is used for assigning 1 to a hit rule variable and assigning 0 to a missed rule variable in a new one-dimensional vector if one or more rules are hit in the incoming condition of the credit evaluation model;
the post-loan performance variable assignment module is used for assigning 1 to the post-loan performance variable in a new one-dimensional vector if the post-loan is overdue, and assigning 0 if the post-loan is overdue;
the logistic regression module is used for calculating the weight parameters of all rules of the credit evaluation model by using the logistic regression model;
and the rule removing module is used for removing the rules with the weight parameters smaller than the preset value from the credit evaluation model according to the weight parameters of all the rules of the credit evaluation model.
6. The wind control rule screening apparatus of claim 5, wherein: in the logistic regression module, a logistic regression model is preset with a loss function of an L1 regularization term.
7. The wind control rule screening apparatus of claim 6, wherein: the logistic regression module is specifically configured to,
and calculating the weight parameters of each rule of the credit evaluation model by using the sparse characteristics of the L1 regularization term of the logistic regression model.
8. The wind control rule screening apparatus of claim 7, wherein: the rule culling module is specifically configured to,
and according to the weight parameters of all the rules of the credit evaluation model, removing the rules with the weight parameters less than 0.01 from the credit evaluation model.
9. A computer device, characterized by: the computer device comprises a memory having a computer program stored thereon and a processor that, when executing the computer program, implements the method of wind control rule screening according to any one of claims 1 to 4.
10. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, implements the method of wind control rule screening according to any one of claims 1 to 4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991034A (en) * | 2020-11-30 | 2021-06-18 | 重庆誉存大数据科技有限公司 | Model-based mini-enterprise credit assessment method, equipment and storage medium |
| CN113240259A (en) * | 2021-04-30 | 2021-08-10 | 顶象科技有限公司 | Method and system for generating rule policy group and electronic equipment |
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2020
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991034A (en) * | 2020-11-30 | 2021-06-18 | 重庆誉存大数据科技有限公司 | Model-based mini-enterprise credit assessment method, equipment and storage medium |
| CN113240259A (en) * | 2021-04-30 | 2021-08-10 | 顶象科技有限公司 | Method and system for generating rule policy group and electronic equipment |
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