Why The Constitution Should Protect Personal Credit Information?——An Approach of Right Argumentation

Protecting personal credit information through constitutional rights is not only essemtial for individuals to defend against infringements on their personal credit information rights and interests by public power in the social credit system,but also a requirement for unified legislation on social credit to explore the basis for constitutional norms.In the era of the credit economy,personal credit information has become a vital resource for realizing personal autonomy.Along with the increase in the state’s supervision and control of personal credit,the realization of the autonomous value in the interests related to personal credit information has also set more obligations for the state.Therefore,interests related to personal credit information should be regarded as a constitutional right.Because of its significant economic interest and value,the right to personal credit information should be classified as a constitutional property right.As a constitutional property right,the right to personal credit information can not only help protect people’s economic interests,but also achieve the goal of safeguarding their personality interests.

A new method of AHP applied to personal credit evaluation

This paper presents a new negative judgment matrix that combines the advantages of the reciprocal judgment matrix and the fuzzy complementary judgment matrix, and then puts forth the properties of this new matrix. In view of these properties, this paper derives a clear sequencing formula for the new negative judgment matrix, which improves the sequencing principle of AHP. Finally, this new method is applied to personal credit evaluation to show its advantages of conciseness and swiftness.

Research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer Learning

Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. In view of these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, xgboost and so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.

A Cuideto Personal Credit

The construction of a social credit system is a social systematic pro-ject.China’s social credit system includes four aspects:social credit sys-tem,credit management and service system,credit practices。

An Interpretable Personal Credit Evaluation Model

How to establish a personal credit evaluation model with both interpretability and high prediction accuracy is an essential task in the credit risk management of commercial banks.To realize interpretable personal credit evaluation with high accuracy,it proposes an interpretable personal credit evaluation model DTONN(i.e.,Decision Tree extracted from Neural Network)that combines the interpretability of decision tree and the high prediction accuracy of neural network.Significant features were selected from raw features by a decision tree,and a four-layer neural network was constructed to predict personal credit by using the selected features.Therefore,the accurate credit evaluation was made through the neural network and associated decision process was intelligibly displayed in the form of a decision tree.In the experiments,DTONN was compared with four personal credit evaluation models:decision tree,neural network,support vector machine,and logistic regression,on giveme-some-credit credit dataset.The experimental results show that our proposed model is state-of-the-art both on the accuracy and interpretability.