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Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.

Confucianism was born in a particular historical atmosphere of the Spring and Autumn period and the Warring States Period [

In order to effectively evaluate the customer’s credit, we extract valuable indicators and data from the 200 credit reports, analyzing the data, determining the data objects and their attributes, and then analyzing the use of appropriate methods for each indicator to give weight. Finally, through each person’s credit score and different credit scores divided according to the credit rating, we can clearly determine the credit status of each customer with a certain practical application value. In summary, the general picture is in

Through statistical analysis of 200 credit reports, we analyze the social roles and social status of the people, according to the basic condition of their personal information and subdivide and find out the credit status of the people in different classes. We establish the index system in

Through the understanding of the basic situation of individuals, we can judge the person’s ability of repayment, consumption concept and direction. Through these can reflect a person’s credit status [

Different gender groups of people have different consumer attitude, there is a big difference in the cost, and there are also some differences in their salaries. Differences in consumption concept directly affect the deadline of repayment.

There are different uses of funds for the people who are in different marital status. As for unmarried people, their funds are used primarily in the preparation of

future marriage; married people spend money to support their families, but divorce is more complex. There is a close relationship between the direction of the use of funds and credit.

Educational status reflects a person’s education level. To a large extent, education and remuneration has a certain positive correlation. The sufficiency of funds is closely related to credit [

Compared with the non-leadership group, the leadership group has a higher salary, and they have a high degree of concern and attention to credit.

We analyze the extraction data and find out that the four factors that have an impact on the overdue are gender ( X 1 ), marital status ( X 2 ), education ( X 3 ), job level ( X 4 ). We analyzed the four factors separately.

We screened out overdue information of customers of different gender through Excel, and selected each customer overdue numbers and the maximum number of overdue months two indicators to do correlation analysis [

Through the analysis, we get the proportion of different gender effects on overdue

Through the above table we made the histogram in

From

We screened out the overdue information of the customers in different marital status in Excel and selected the overdue number of each client to do the dependent variable to get the proportion of different marital status in

Make the proportional histogram as

We can find that the overdue proportion of married people is much higher.

Gender | Overdue amount | Overdue proportion | Non overdue quantity | Non overdue proportion |
---|---|---|---|---|

Male | 60 | 0.3 | 56 | 0.28 |

Female | 46 | 0.23 | 38 | 0.19 |

Marital status | Overdue amount | Overdue proportion | Non overdue quantity | Non overdue proportion |
---|---|---|---|---|

Unmarried | 22 | 0.11 | 16 | 0.08 |

Married | 76 | 0.38 | 65 | 0.325 |

Divorce | 15 | 0.075 | 6 | 0.03 |

than that of unmarried and divorced people, indicating that married people have poor credit, which may due to the enormous cost of their families. Non-overdue proportion of married people is greater than unmarried people and divorced people, which indicate that married people are the main customers of credit loans.

As before we screened the overdue information of customers with different educational background in Excel, and selected the overdue number of each customer to do the dependent variable, obtain the proportion of overdue repayment of different education which is showed in the table

According to the proportion in the table which is shown in the

From

We screened out the overdue information of customers in different positions in Excel, and selected the overdue number of each customer to do the dependent variable, obtaining the proportion of overdue repayment of leadership and non- leadership which is showed in the following table

According to the table, the following proportion is shown in the bar chart

Education | Overdue amount | Overdue proportion | Non overdue quantity | Non overdue proportion |
---|---|---|---|---|

Undergraduate | 18 | 0.09 | 17 | 0.085 |

Junior College | 42 | 0.21 | 30 | 0.15 |

High school | 32 | 0.16 | 27 | 0.135 |

Junior middle school | 11 | 0.055 | 23 | 0.115 |

Post | Overdue amount | Overdue proportion | Non overdue quantity | Non overdue proportion |
---|---|---|---|---|

leadership | 56 | 0.28 | 40 | 0.2 |

non-leadership | 55 | 0.275 | 49 | 0.245 |

From

Next we will build the logistic regression model for credit evaluation.

Logistic regression model belongs to probabilistic nonlinear regression; it is a multivariate analysis method to examine the relationship between the results of the two categories and the influence factors [

The standard linear regression model is:

Y = ∂ + β 1 × X 1 + ⋯ + β m × X m (1)

And by the standard linear regression model we can replace Y with probability P, and get:

P = ∂ + β 1 × X 1 + ⋯ + β m × X m (2)

But this model has a lot of restrictions in the application. Statisticians use logistic transformation to solve this problem. Logistic transformation introduction: The ratio of the probability of occurrence of a result and the probability that the result does not occur is usually called the odd number. This is

Odds = π 1 − π . Take the logarithm λ = ln ( Odds ) = ln ( π 1 − π ) . This is the logistic

transformation. By transforming, the range of values Logit ( π ) is extended to the entire real field centered on 0. This makes it possible to predict the π value at any value of the independent variable. Therefore, we build Logit ( π ) as the dependent variable, establishment of logistic regression model with P independent variables:

Logistic ( P ) = β 0 + β 1 × X 1 + ⋯ + β P × X P (3)

Among them, Logistic ( P ) = ln ( P 1 − P ) . The logistic regression model that

fits the two classifiers is transformed into the parameters of the fitted linear model, Among them, β 1 , β 2 , β 3 , ⋯ , β P is the regression coefficient, which shows the contribution of each influencing factor X i to P , and β 0 is a constant term.

According to the above equation , we can get the following formular.

P = exp ( β 0 + β 1 × X 1 + ⋯ + β P × X P ) 1 + exp ( β 0 + β 1 × X 1 + ⋯ + β P × X P ) (4)

1 − P = 1 1 + exp ( β 0 + β 1 × X 1 + ⋯ + β P × X P ) (5)

We choose regression analysis of the effects of independent variables ( X 1 ), marital status ( X 2 ), education ( X 3 ) and job level ( X 4 ) on predictive variables overdue ( Y ). Which Y = 0 when the repayment is not overdue, Y = 1 when the repayment overdue X 1 X 2 X 3 and X 4 are deterministic variables associated with Y . So as to obtain the contribution of each factor to the predictor [

From the results we can see that the four independent variables are significant ( X 1 ), marital status ( X 2 ), education ( X 3 ), job height ( X 4 ), and the final regression equation can be obtained as follows:

P = e Y 1 − e Y (6)

Y = − 3.08526394277 + 1.11941730358 × X 1 + 0.149338115176 × X 2 + 0.215431901503 × X 3 + 13.0143637733 × X 4 (7)

Transformed:

Dependent Variable: Y Method: ML - Binary Logistic (Quadratic hill climbing) Date: 02/19/17 Time: 13:35 Sample: 1 96 Included observations: 96 Convergence achieved after 3 iterations | ||||
---|---|---|---|---|

Coefficient | Std. Error | z-Statistic | Prob. | |

C(1) | −3.085264 | 0.231389 | −13.3336 | 0.0000 |

C(2) | 1.119417 | 0.629502 | 1.778259 | 0.0387 |

C(3) | 0.149338 | 0.193974 | 0.769887 | 0.0434 |

C(4) | 0.215432 | 0.561374 | 0.383758 | 0.0271 |

C(5) | 13.01436 | 0.859665 | 15.13888 | 0.0000 |

McFadden R-squared | 1.000000 | Mean dependent var | 0.500000 | |

S.D. dependent var | 0.502625 | S.E. of regression | 6.44E−41 | |

Akaike info criterion | 0.104167 | Sum squared resid | 3.78E−79 | |

Schwarz criterion | 0.237726 | Log likelihood | 0.000000 | |

Hannan-Quinn criter. | 0.158154 | Deviance | 0.000000 | |

Restr. deviance | 133.0843 | Restr. log likelihood | −66.54213 | |

LR statistic | 133.0843 | Avg. log likelihood | 0.000000 | |

Prob(LR statistic) | 0.000000 | |||

Obs with Dep = 0 | 48 | Total obs | 96 |

Logistic ( P ) = − 3.08526394277 + 1.11941730358 × X 1 + 0.149338115176 × X 2 + 0.215431901503 × X 3 + 13.0143637733 × X 4 (8)

We can use the Logistic model for credit evaluation. We just need to be gender ( X 1 ), marital status ( X 2 ), education ( X 3 ), position ( X 4 ) score into the model to get the final credit score.

According to Logistic model, we will make each factor corresponding logistic coefficient as credit influence weight. As sex analysis of the weight of men is 1.119417, the same way to get the other three factors is showed in the

Then we finally come to the formula of the credit index Y:

Y = − 3.085263 + 1.119417 × X 1 + 0.149338 × X 2 + 0.215431 × X 3 + 13.014363 × X 4 (9)

According to the basic information in the credit report (gender, education, marital status, job level) on the proportion of repayment overdue, we made a detailed regression statistics, then, we put the data into the calculation formula of the credit index Y:

Gender | 1.119417 |
---|---|

Marital status | 0.149338 |

Education | 0.215432 |

Post | 13.01436 |

Y = − 3.085263 + 1.119417 × X 1 + 0.149338 × X 2 + 0.215431 × X 3 + 13.014363 × X 4 (10)

We get the corresponding credit index trend graph in

Based on the user credit index derived from the regression data, we use the credit index of 0.8, 0.6, and 0.4 as the standard; the customer level is divided into excellent good moderate poor four grades.

Excellent (0.8 - 1): Consumer’s credit condition is the best, there are almost no default risk.; financial institutions bear the least risk as the optimal lending standard [

Good (0.6 - 0.8): Customer credit situation is better; occasionally there will be a breach of contract; financial institutions need to bear some risks, but still within reasonable limits.

Moderate (0.4 - 0.6): Customer credit status in general, the probability of default is higher than the previous two levels, but it requires financial institutions to take reasonable measures to make up for risk, making the proceed are still greater than the risk. This level is the minimum lending standard.

Poor (0.4 or less): Customer credit is poor; breach of contract often occurs; the pecuniary institutions face enormous risks; they shouldn’t be lent.

In the course of the study, we investigated the personal credit information of 200 credit users. They can be viewed in a single factor analysis. The proportion of men overdue>the pro-portion of women overdue; non overdue proportion of male > non overdue proportion of women. Because we are highest in the course of the survey than the female boss, so the men overdue and non-overdue rate is higher than women’s situation. In the case of marital status, overdue rankings are married > unmarried > divorce. Non overdue rankings are married > unmarried > divorce. We can note that the credit situation in the married population is biased towards polarization. We can think that as people enter into marriage, people’s mental state has gradually become distinct. People in sound financial condition may have no pressure on repayment. While some people are in poor economic situation, repayment ability is mediocre. In terms of qualifications, overdue rate ranked college > high school > undergraduate > junior high school. Non-overdue rate ranked college > high school > junior high school > undergraduate. We can see high school and college education overdue rate is relatively high. While students in junior high school may not acknowledge the using of credit, so users in this part are small and the rate is low. Undergraduate people may pay more attention to their credit records. In terms of posts, overdue rankings lead greater than non-leadership, non-overdue rankings of non-leaders than leaders. A certain extent reflects the leadership may spend more. In terms of repayment is not good enough.

Throughout the full text, the overdue number is greater than the number of non- overdue. To some extent, it reflects the Chinese consumers do not attach importance to personal credit situation. The society has more and more credit consumption, which formed the credit risk system, is more and more complex. The government needs to create a complete set of credit regulatory system in order to maintain the overall credit. We set up a set of evaluation index system in the study of credit status and a logistic regression model to analyze the data; the analysis process is rigorous. We feel that these methods can be used as a reference to establish a credit regulatory system.

For the risk regulatory authorities, according to the individual’s credit rating, we set a risk range. Once beyond this risk range, the risk of lending will be a magnifying trend. We should not lend at this time, at the same time, we should make the individuals’ credit recording and update it timely. For the emergence of credit overdue individuals, we should adjust their credit level to prevent the emergence overdue behavior again.

Although the management is strict, it just an external factor for constructing the whole credit system; the internal factor is still people's attention to themselves. This requires our respective efforts to maintain our credit. We should pay attention to our personal credit situation, establish the correct concept of consumption and values. When the individual’s credit level can be improved, the community’s overall credit can be improved. It will be more harmonious between people.

This research was carried out with support of National Natural Science Foundation of People’s Republic of China (project 71661025 and 11602115).

Lv, X.M., Li, J.B., Zhang, S.K., Li, Y. and Wang, C. (2017) Research on the Influencing Factors of Personal Credit Based on a Risk Management Model in the Background of Big Data. Journal of Applied Mathematics and Physics, 5, 722-733. https://doi.org/10.4236/jamp.2017.53061