CN113298393A - Vehicle loan risk assessment method based on regression algorithm - Google Patents

Vehicle loan risk assessment method based on regression algorithm Download PDF

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CN113298393A
CN113298393A CN202110598686.9A CN202110598686A CN113298393A CN 113298393 A CN113298393 A CN 113298393A CN 202110598686 A CN202110598686 A CN 202110598686A CN 113298393 A CN113298393 A CN 113298393A
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car
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周波
解常惺
郭能
蔡浴泓
余勇辉
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Zhejiang Huifu Network Technology Co ltd
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Abstract

The application provides a vehicle loan risk assessment method based on a regression algorithm, which comprises the following steps: constructing an artificial neural network model for evaluating the car loan risk; evaluating and optimizing the artificial neural network model: judging whether KS values of the training set, the testing set and the time-out sample set are larger than a preset threshold value or not, and if not, optimizing the artificial neural network model; making a rating card for evaluating the car loan risk; and carrying out vehicle credit risk rating on the client based on the rating card and the artificial neural network model. The vehicle credit risk assessment method based on the regression algorithm has the advantages that the vehicle credit customer risk assessment problem is efficiently and accurately solved in an artificial intelligence mode.

Description

Vehicle loan risk assessment method based on regression algorithm
Technical Field
The application relates to a car credit risk assessment method, in particular to a car credit risk assessment method based on a regression algorithm.
Background
The vehicle loan service is that when a borrower purchases a consumption vehicle, the bank directly issues funds to the vehicle dealership for vehicle fund payment due to limited funds and bank loan. With the gradual expansion of the market, whether banks or security companies rely on manpower to control risks, which is far from enough, and we do not have so much manpower and time to control risks, so a wind control solution capable of releasing a large amount of labor and time is urgently needed.
Disclosure of Invention
In order to solve the defects of the prior art, the application provides a vehicle loan risk assessment method based on a regression algorithm, which comprises the following steps: constructing an artificial neural network model for evaluating the car loan risk; evaluating and optimizing the artificial neural network model: judging whether KS values of the training set, the testing set and the time-out sample set are larger than a preset threshold value or not, and if not, optimizing the artificial neural network model; making a rating card for evaluating the car loan risk; and carrying out vehicle credit risk rating on the client based on the rating card and the artificial neural network model.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending comprises: and performing data extraction and data matching on the customer information data.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: the method comprises the steps of defining data labels of client data collected through extraction and matching, defining good and bad samples of samples according to overdue performance of a back door responsible for loan feedback history client, giving a performance period of n months through view and rolling rate analysis, defining that the maximum overdue days of the previous n months are more than or equal to m days as bad samples, the first six months are good samples if no overdue occurs, and otherwise, the samples are gray samples.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: data set partitioning: dividing a data set into a training set, a testing set and an out-of-time sample set, wherein the sample number ratio of the training set, the testing set and the out-of-time sample set is 7: 2: 1.
further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: the features constituting the artificial neural network model are: and at least combing and deriving data of personal basic information, credit assessment scores of users, relative positions of credit assessment scores, marital states, professional information, accumulated fund payment records and debts of users to form characteristics of the artificial neural network model.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: data binning and code numeralization processing: the data binning adopts one of equal frequency, equal distance, decision tree binning and chi-square binning; the coding numeralization adopts one-hot coding or WOE coding.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: screening the characteristics of the artificial neural network model: and screening the characteristics of the artificial neural network model by adopting one or more of IV value screening, characteristic correlation screening and multiple collinearity screening.
Further, the step of constructing an artificial neural network model for assessing the risk of car lending further comprises: training the artificial neural network model: and (3) performing model training by using logistic regression, observing whether the regression coefficient is negative in the training process, deleting the variable if the regression coefficient is negative, and adjusting and optimizing main hyper-parameters of the logistic regression by using a grid search method.
Further, the step of evaluating and optimizing the artificial neural network model further comprises: and judging whether the AUC values of the training set, the testing set and the time-out sample set are larger than a preset threshold value, and if not, optimizing the artificial neural network model.
Further, the making of the scoring card for assessing the risk of car lending comprises the following steps: setting a specific desired branch to the set ratio; determining a fraction PDO of double the ratio; determining scale parameters A and B according to a formula; calculating the score corresponding to each box of each characteristic according to the scale factor B, the logistic regression coefficient and the WOE value corresponding to each box; a scoring card document is generated.
The application has the advantages that: the vehicle credit risk assessment method based on the regression algorithm is provided, and the problem of vehicle credit customer risk assessment is efficiently and accurately solved by adopting an artificial intelligence mode.
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The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic diagram of the general steps of a regression algorithm based car loan risk assessment method according to one embodiment of the present application;
fig. 2 is a block diagram illustrating specific steps of a regression algorithm based car loan risk assessment method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1 and 2, the purpose of the application is to predict default risk level of a vehicle classification scene by using Logistic regression algorithm through big data, and an effective means is provided for risk of vehicle classification business.
The technical scheme is as follows: collecting relevant information of vehicle classification, such as user's public accumulation fund/social security data, data of union pay, data of operator, data of E-commerce transaction, and data of industry and commerce, court and information; secondly, collecting sample data and marking and sample enhancing; thirdly, data cleaning and data set division; fourthly, characteristic engineering (including construction and dimension reduction); fifthly, constructing a model; sixthly, model evaluation and optimization; finally, carrying out final risk rating; the algorithm used is Logistic regression algorithm.
As a specific scheme, the method comprises the following steps:
the method comprises the following steps: and (6) data extraction.
Deriving age and the like according to personal information, carrying out data matching on three-party data in API docking, such as FICO score, Unionpay data and the like, and data of e-commerce transactions, operators and the like.
Step two: and (4) defining a data tag.
And (3) defining a sample good-bad sample according to the overdue performance after the loan of the backdoor feedback history client, giving a performance period of n months through view and rolling rate analysis, defining that the maximum overdue days of the previous n months are not less than m days and are bad clients, defining that the overdue days are not over the first six months and are good samples, and otherwise, defining that the overdue days are grey samples. The values of the specific m and n may be determined according to the view and the scroll rate.
Step three: the sample is increased.
If there are fewer bad samples, the samples need to be upsampled, i.e., the samples are increased.
Step four: and (5) processing missing values.
The deletion ratio is higher than an index of 80-90%, preferably 80%, and a direct deletion mode is adopted; when the missing value has special meaning, for example, data is not collected yet, the missing can be classified into one category independently, and the rest can be filled by interpolation methods, such as mean value filling, mode filling and the like.
Step five: and (4) dividing the data set.
The data set is divided into a training set, a test set, and an out-of-time sample set, which are typically 7: 2: 1, although the ratio can be adjusted as appropriate.
Step six: and (5) feature construction.
Based on business expert experience, the main dimensions influencing overdue after client credit are as follows: personal basic information, credit assessment scores of users, relative positions of credit assessment scores, marital states, occupational information, accumulated fund payment records, user liabilities and the like, and according to the dimensions, indexes are combed and a series of dimensions, such as combinations of ages and marital states and the like, are derived.
Step seven: the method comprises the steps of binning and coding numeralization, wherein binning can adopt equal frequency, equal distance, decision tree binning, chi-square binning and the like, coding and single hot coding, WOE coding and the like are adopted, binning needs to meet the requirement that each bin has good and bad samples, monotonicity needs to be presented, the business logic is met, the number of the bins is not too large, and coding numeralization is carried out after binning.
Step eight: and (4) feature screening. The number of features is not too large for generalization capability and robustness of the model, and feature screening is required. Feature screens include IV value screens and feature correlation screens as well as multiple collinearity screens.
1. Screening based on IV value. From the data point of view, the higher the IV value, the more information the feature contains, the stronger the predictive ability, and the feature with an IV of 0.02 or more is selected.
2. And screening based on the characteristic relevance. Since the stability and the interpretability of the model are influenced by too high characteristic correlation, the characteristic correlation coefficient matrix is calculated, and generally, for the characteristics of which the absolute value of the correlation coefficient is greater than 0.5, one of the characteristics with high IV value is selected for reservation.
3. Based on multiple collinearity screening: in general, multicollinearity, i.e., vif, is preferably not greater than 10.
Step nine: and (5) constructing a model. And after feature screening is finished, performing model training by using logistic regression, observing whether a regression coefficient is negative or not in the training process, deleting the variable if the regression coefficient is negative, and optimizing main hyper-parameters of the logistic regression by using a grid search method.
Step ten: and (5) evaluating and optimizing the model. And respectively calculating KS and AUC of the training set, the test set and the time-out sample set, if KS > =0.3 and AUC > =0.7, and the KS and AUC of the three sample sets are not different, indicating that the model is stable and effective, otherwise, optimizing is needed, and repeating the step eight to perform model training again.
Step eleven: and (5) making a rating card. The method comprises the following steps:
1. setting a specific desired branch for a specific ratio;
2. determining a fraction PDO of double the ratio;
3. determining scale parameters A and B according to a formula;
4. calculating the score corresponding to each box of each characteristic according to the scale factor B, the logistic regression coefficient and the WOE value corresponding to each box;
5. a scoring card document is generated.
Step twelve: and grading the credit-allowed customers of the bank vehicle based on the grades calculated by the grading card.
By adopting the method, a large amount of labor force is released, a large amount of time is saved, funds are used in places needing to be used, and meanwhile various products in the big data era are fully utilized, so that the prediction result is more convincing.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A vehicle loan risk assessment method based on a regression algorithm is characterized by comprising the following steps:
the vehicle loan risk assessment method based on the regression algorithm comprises the following steps:
constructing an artificial neural network model for evaluating the car loan risk;
evaluating and optimizing the artificial neural network model: judging whether KS values of the training set, the testing set and the time-out sample set are larger than a preset threshold value or not, and if not, optimizing the artificial neural network model;
making a rating card for evaluating the car loan risk;
and carrying out vehicle credit risk rating on the client based on the rating card and the artificial neural network model.
2. The regression algorithm-based car loan risk assessment method according to claim 1, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending comprises the following steps:
and performing data extraction and data matching on the customer information data.
3. The regression algorithm-based car loan risk assessment method according to claim 2, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
the method comprises the steps of defining data labels of client data collected through extraction and matching, defining good and bad samples of samples according to overdue performance of a back door responsible for loan feedback history client, giving a performance period of n months through view and rolling rate analysis, defining that the maximum overdue days of the previous n months are more than or equal to m days as bad samples, the first six months are good samples if no overdue occurs, and otherwise, the samples are gray samples.
4. The regression algorithm based car credit risk assessment method according to claim 3, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
data set partitioning: dividing a data set into a training set, a testing set and an out-of-time sample set, wherein the sample number ratio of the training set, the testing set and the out-of-time sample set is 7: 2: 1.
5. the regression algorithm-based car loan risk assessment method according to claim 4, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
the features constituting the artificial neural network model are: and at least combing and deriving data of personal basic information, credit assessment scores of users, relative positions of credit assessment scores, marital states, professional information, accumulated fund payment records and debts of users to form characteristics of the artificial neural network model.
6. The regression algorithm-based car loan risk assessment method according to claim 5, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
data binning and code numeralization processing: the data binning adopts one of equal frequency, equal distance, decision tree binning and chi-square binning; the coding numeralization adopts one-hot coding or WOE coding.
7. The regression algorithm-based car loan risk assessment method according to claim 6, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
screening the characteristics of the artificial neural network model: and screening the characteristics of the artificial neural network model by adopting one or more of IV value screening, characteristic correlation screening and multiple collinearity screening.
8. The regression algorithm-based car loan risk assessment method according to claim 7, wherein:
the step of constructing an artificial neural network model for assessing the risk of car lending further comprises:
training the artificial neural network model: and (3) performing model training by using logistic regression, observing whether the regression coefficient is negative in the training process, deleting the variable if the regression coefficient is negative, and adjusting and optimizing main hyper-parameters of the logistic regression by using a grid search method.
9. The regression algorithm based car credit risk assessment method according to claim 8, wherein:
the step of evaluating and optimizing the artificial neural network model further comprises:
and judging whether the AUC values of the training set, the testing set and the time-out sample set are larger than a preset threshold value, and if not, optimizing the artificial neural network model.
10. The regression algorithm based car credit risk assessment method according to any one of claims 1 to 9, characterized in that:
the making of the scoring card for evaluating the car credit risk comprises the following steps:
setting a specific desired branch to the set ratio;
determining a fraction PDO of double the ratio;
determining scale parameters A and B according to a formula;
calculating the score corresponding to each box of each characteristic according to the scale factor B, the logistic regression coefficient and the WOE value corresponding to each box;
a scoring card document is generated.
CN202110598686.9A 2021-05-31 2021-05-31 Vehicle loan risk assessment method based on regression algorithm Pending CN113298393A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115907956A (en) * 2022-09-27 2023-04-04 睿智合创(北京)科技有限公司 Simulation early warning method and system for asset risk

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115907956A (en) * 2022-09-27 2023-04-04 睿智合创(北京)科技有限公司 Simulation early warning method and system for asset risk

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