CN113011752A - Enterprise credit evaluation index system based on big data - Google Patents
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Abstract
An enterprise credit evaluation index system based on big data belongs to the technical field of data processing and comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring enterprise guest group data information; the data processing module is used for carrying out layered sampling on the acquired customer group data information of the credit enterprise to acquire a modeling analysis sample; the data cleaning module is used for finding and correcting recognizable data errors in the modeling analysis sample to obtain first data; the characteristic engineering module is used for processing the first data to obtain second data; the application module is used for receiving the second data and verifying the model indexes of the enterprise credit evaluation according to a logistic regression algorithm; the method can effectively predict the loan behavior of the small and medium enterprises, generate obvious discrimination on default passenger groups and non-default passenger groups, solve the obstacles of the existing method in the aspects of solving the admission of the first loan of the small and medium enterprises and the acquisition of the first loan of banks, and have considerable feasibility and landing application value.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an enterprise credit evaluation index system based on big data.
Background
The currently mainstream credit evaluation model technology for small and medium-sized micro enterprises in China mainly adopts a way of adding new index variables into an original credit rating system for the nationally owned enterprises and large-sized enterprises to establish a model, for example, a comfort and administration (2015) analyzes the causal relationship between default conditions of the small and medium-sized micro enterprises and various factors influencing the default conditions of the small and medium-sized micro enterprises through Logistic model regression, and establishes an index system which reflects credit conditions of the small and medium-sized micro enterprises through seven categories of debt repayment capacity, profit capacity operation capacity, innovation capacity, growth capacity, corporate governance, credit conditions and the like. In addition, the prior art is limited by data blockade and the like of small and medium-sized micro enterprises in China in data selection, a model is built mainly by using evaluation indexes such as asset liability rate and the like which are traditionally used for large and medium-sized enterprises, the obtained result is difficult to truly reflect the production and operation conditions and the credit level of the small and medium-sized micro enterprises, and the practical value is low in solving the problem of difficult head loan of the small and medium-sized micro enterprises by using a big data technology.
Disclosure of Invention
The invention aims to provide an enterprise credit evaluation index system based on big data to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an enterprise credit evaluation index system based on big data comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring enterprise customer group data information, and the enterprise customer group data information comprises credit-providing enterprise customer group data information and non-credit-providing enterprise customer group data information; the data processing module is used for carrying out layered sampling on the acquired customer group data information of the credit enterprise to acquire a modeling analysis sample; the data cleaning module is used for finding and correcting identifiable data errors in the modeling analysis sample/non-trust enterprise customer group data information to obtain first data/prediction data; the characteristic engineering module is used for processing the first data/predicted data to obtain a second data/model index; and the application module is used for receiving the second data/model index and verifying the characteristic index of the credit evaluation of the credit information of the credit-granting enterprise customer group/predicting the credit score of the non-credit-granting enterprise customer group according to a logistic regression algorithm.
Further, the characteristic engineering module comprises a characteristic derivation module, a characteristic binning module and a characteristic screening module; the system comprises a characteristic derivation module, a characteristic classification module and a characteristic screening module, wherein the characteristic derivation module is used for acquiring more new characteristics representing enterprise behaviors from all dimensional data of the enterprise in modes of polynomial transformation calculation, time window statistics and summary and the like, the characteristic classification module is used for discretizing continuous characteristics, and the characteristic screening module is used for selecting more important characteristics which can reflect the repayment capability and the repayment willingness of the enterprise more according to indexes such as characteristic importance degree and linear correlation, so that the calculation complexity is reduced while the model generalization capability is improved.
Further, the feature binning module measures importance and contribution of features in the system through a feature IV value, and adopts the following formula:
wherein, BallRepresents the total number of default samples, B, in all samplesiRepresenting the number of default samples, G, in the ith binallRepresents the total number of non-default samples, G, in all samplesiRepresenting the number of non-violating samples in the ith bin.
Further, the model indexes cover one or more of four data dimensions of enterprise basic information, asset behavior conditions, business income indexes and social credit expression.
Further, the data cleaning module specifically comprises the following working steps:
s1: processing invalid values in the data;
s2: processing missing values in the data; deletion supplement is carried out by calculating the deletion rate of each field and combining the importance of the fields;
s3: checking the consistency of the data according to the reasonable value range and the correlation of the variables in the checked data;
s4: other defects are corrected.
Further, other defects in step S4 include format content processing, deduplication recording processing, and the like.
Further, the modeling analysis samples comprise training samples and verification samples, and the specific division work steps are as follows:
SS 1: dividing the credit granting enterprise data acquired by the data acquisition module into a good sample G and a bad sample B according to the credit granting condition;
SS 2: respectively extracting 50% of good samples G and bad samples B to form training samples, namely 0.5G +0.5B, and dividing the rest of the total number of samples into verification samples.
Compared with the prior art, the invention has the beneficial effects that: the method can effectively predict the loan behavior of small and medium enterprises, generate obvious discrimination on default passenger groups and non-default passenger groups, solve the problem of the existing method in terms of the admission of the first loan of the small and medium enterprises and the acquisition of the bank first loan, and has considerable feasibility and application value on the ground.
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FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a schematic diagram of the architecture of a feature engineering module of the present invention;
fig. 3 is a supplementary policy combination diagram in the present invention.
In the figure: 1-a data acquisition module; 2-a data processing module; 3-a data cleaning module; 4-a feature engineering module; 40-a feature derivation module; 41-characteristic box separation module; 42-feature screening module; 5-application module.
Detailed Description
The technical solutions in the embodiments of the present invention will be described below in detail and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Example (b):
in this embodiment, the main body of the enterprise customer group data information is, for example, a small and medium-sized micro enterprise, and as shown in fig. 1, the enterprise credit evaluation index system based on big data includes:
the system comprises a data acquisition module 1, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring enterprise customer group data information, and the enterprise customer group data information comprises credit enterprise customer group data information and non-credit enterprise customer group data information;
the data processing module 2 is used for carrying out layered sampling on the acquired customer group data information of the credit enterprise to acquire a modeling analysis sample;
the data cleaning module 3 is used for finding and correcting recognizable data errors in the modeling analysis sample/non-trust enterprise customer group data information to obtain first data/prediction data;
the characteristic engineering module 4 is used for processing the first data/predicted data to obtain a second data/model index;
the application module 5 is used for receiving the second data/model index and verifying the characteristic index of credit evaluation of the credit enterprise customer group data information/predicting the credit score of the non-credit enterprise customer group according to a logistic regression algorithm;
the modeling analysis samples comprise training samples and verification samples, and the specific division work steps are as follows:
SS 1: dividing the credit granting enterprise data acquired by the data acquisition module 1 into a good sample G and a bad sample B according to the credit granting condition;
SS 2: respectively extracting 50% of good samples G and bad samples B to form training samples, namely 0.5G +0.5B, and dividing the rest of the total number of the samples into verification samples;
and secondly, the model indexes cover one or more of four data dimensions of enterprise basic information, asset behavior conditions, business income indexes and social credit expression.
By the scheme, the loan behavior of the small and medium enterprises can be effectively predicted, the obvious discrimination is generated between the default passenger groups and the non-default passenger groups, the block of the existing method is broken in the aspects of solving the admission of the first loan of the small and medium enterprises and the acquisition of the bank first loan, and the method has considerable feasibility and application value on the ground.
In this embodiment, the training step of the application module 5 includes the following steps:
(1) putting the screened features into a logistic model for training, and adjusting the model by debugging regularized penalty terms and good and bad sample weights to enable the model to output default probabilities ln (odds);
ln(odds)＝β0+β1x1+β2x2+…+βnxn，n＝1,2，3…；
wherein, [ x ]1，x2，…，xn]Showing the characteristics of the screening, [ beta ]0，β1，β2，…，βn]In order to be the weight coefficient,
(2) then carrying out linear mapping on default probabilities ln (odds) output by the logistic model; the following equation:
score＝A-B×ln(odds)；
where a represents the benchmark credit score and B represents the credit score scaling weight.
In this embodiment, the specific working steps of the data cleansing module 4 are as follows:
s1: processing invalid values in the data; invalid values, i.e. fields that are not needed, are, for example: useless self-increment serial numbers, administrative agencies, record personnel information and the like are directly deleted in the step;
s2: processing missing values in the data; deletion supplement is carried out by calculating the deletion rate of each field and combining the importance of the field, and the supplement strategy refers to FIG. 3;
s3: checking the consistency of the data according to the reasonable value range and the correlation of the variables in the checked data; for example, the age and sales income of the legal representative in the data source of this embodiment are negative and should be considered to be beyond the normal value range, and in addition, the enterprise registered capital and the actual payment capital in the data source of this embodiment are from the two tables of the enterprise basic information table and the registration information table at the same time, and values in the two tables are different, and after analysis, the update time of the enterprise basic information table is found to be longer than that of the registration information table, and the update frequency of the registration information table is confirmed to be delayed with the business personnel, so the system takes the registered capital and the actual payment capital data of the enterprise basic information table as the latest data;
s4: other defects are corrected.
Specifically, the working process of S3, for example, the age of the legal representative in the data source and the sales income have negative values, should be considered as exceeding the normal range of the value range, because such samples in the database are few, the direct deletion mode is adopted, and the relevance verification is performed for the case that there are multiple data sources in the same variable: for example, the registered capital and the actual payment capital of the enterprise in the technical data source come from two tables of an enterprise basic information table and a registration information table at the same time, values in the two tables are different, the updating time of the enterprise basic information table is found to be longer than that of the registration information table through analysis, and the updating frequency of the registration information table is delayed from that of business personnel, so that the registered capital and the actual payment capital data of the enterprise basic information table are taken as the latest data.
As can be seen in fig. 2, the feature engineering module 4 includes a feature derivation module 40, a feature binning module 41, and a feature screening module 42; the feature derivation module 40, the feature binning module 41 and the feature screening module 42 are sequentially in signal connection.
The feature derivation module 40 is configured to obtain more new features representing enterprise behaviors from the dimensional data of the enterprise through polynomial transformation calculation, time window statistics and summarization, and the feature binning module 41 is configured to discretize continuous features and the feature screening module 42 is configured to select features which are more important and can reflect the repayment capability and the repayment willingness of the enterprise more according to indexes such as feature importance and linear correlation, so that the calculation complexity is reduced while the model generalization capability is improved.
It is to be noted that, in this embodiment, a chi-square binning algorithm is used to discretize the features, and the feature binning module 41 measures the importance and contribution of the features in the system through the feature IV value, and uses the following formula:
among them, WOEiRepresents the weight of evidence for the ith sample, BallRepresents the total number of default samples, B, in all samplesiRepresenting the number of default samples, G, in the ith binallRepresents the total number of non-default samples, G, in all samplesiRepresenting the number of non-violating samples in the ith bin.
In the present embodiment, other defects in step S4 further include format content processing, deduplication recording processing, and the like; for example: the storage dates and formats of the multiple original tables are not consistent, and all date formats are unified through the time conversion format.
In the description of the present invention, it is to be understood that the terms "center", "lateral", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified. Furthermore, the term "comprises" and any variations thereof is intended to cover non-exclusive inclusions.
The present invention has been described in terms of embodiments, and variations and modifications can be made without departing from the principles of the invention. It should be noted that all the technical solutions obtained by means of equivalent substitution or equivalent transformation, etc., fall within the protection scope of the present invention.
Claims (7)
1. The utility model provides an enterprise credit evaluation index system based on big data which characterized in that: the system comprises a data acquisition module (1) for acquiring enterprise customer group data information, wherein the enterprise customer group data information comprises credit-granting enterprise customer group data information and non-credit-granting enterprise customer group data information; the data processing module (2) is used for carrying out layered sampling on the acquired customer group data information of the credit enterprise to acquire a modeling analysis sample; the data cleaning module (3) is used for finding and correcting identifiable data errors in the modeling analysis sample/non-trust enterprise customer group data information to obtain first data/prediction data; the characteristic engineering module (4) is used for processing the first data/predicted data to obtain a second data/model index; and the application module (5) is used for receiving the second data/model index and verifying the characteristic index of credit evaluation of the credit information of the credit-granting enterprise customer group/predicting the credit score of the non-credit-granting enterprise customer group according to a logistic regression algorithm.
2. The big-data based enterprise credit evaluation index system according to claim 1, wherein: the characteristic engineering module (4) comprises a characteristic derivation module (40), a characteristic binning module (41) and a characteristic screening module (42); the system comprises a characteristic derivation module (40) and a characteristic screening module (42), wherein the characteristic derivation module (40) is used for acquiring more new characteristics representing enterprise behaviors from all dimensional data of the enterprise in modes of polynomial transformation calculation, time window statistics summary and the like, the characteristic binning module (41) is used for discretizing continuous characteristics, and the characteristic screening module (42) is used for selecting more important characteristics capable of reflecting the repayment capability and the repayment willingness of the enterprise more according to indexes such as characteristic importance degree and linear correlation and the like, so that the calculation complexity is reduced while the generalization capability of the model is improved.
3. The big-data based enterprise credit evaluation index system of claim 2, wherein: the feature binning module (41) measures the importance and contribution of features in the system through a feature IV value, and adopts the following formula:
wherein, BallRepresents the total number of default samples, B, in all samplesiRepresenting the number of default samples, G, in the ith binallRepresents the total number of non-default samples, G, in all samplesiRepresenting the number of non-violating samples in the ith bin.
4. The big-data based enterprise credit evaluation index system according to any one of claims 1-3, wherein: the model indexes cover one or more of four data dimensions of enterprise basic information, asset behavior conditions, business income indexes and social credit expression.
5. The big-data based enterprise credit evaluation index system of claim 4, wherein: the data cleaning module (3) comprises the following specific working steps:
s1: processing invalid values in the data;
s2: processing missing values in the data; deletion supplement is carried out by calculating the deletion rate of each field and combining the importance of the fields;
s3: checking the consistency of the data according to the reasonable value range and the correlation of the variables in the checked data;
s4: other defects are corrected.
6. The big-data based enterprise credit evaluation index system of claim 5, wherein: other defects in step S4 include format content processing, deduplication recording processing, and the like.
7. The big-data based enterprise credit evaluation index system of claim 6, wherein: the modeling analysis samples comprise training samples and verification samples, and the specific division work steps are as follows:
SS 1: dividing the credit granting enterprise data acquired by the data acquisition module (1) into a good sample G and a bad sample B according to the credit granting condition;
SS 2: respectively extracting 50% of good samples G and bad samples B to form training samples, namely 0.5G +0.5B, and dividing the rest of the total number of samples into verification samples.
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