Risk early Warning is very important to Banks. This dissertation puts forward a Agent-Based Credit Risk Prediction System,the main parts of the system such as model training, prediction, results analyzing are realized...Risk early Warning is very important to Banks. This dissertation puts forward a Agent-Based Credit Risk Prediction System,the main parts of the system such as model training, prediction, results analyzing are realized with agent that have the characteristic of autonomy, social ability, reactivity and pro-activeness etc. And also use agent technology to make the Risk Early Warning more effective.展开更多
采用信息化手段防控信贷违约风险对保障信贷产业健康发展具有重大的现实意义。传统信贷违约预测模型风险防控能力有限,且在应用有效性评价指标缺乏统一标准。为此,分别基于支持向量机,贝叶斯及随机森林方法建立了信贷违约预测模型,并提...采用信息化手段防控信贷违约风险对保障信贷产业健康发展具有重大的现实意义。传统信贷违约预测模型风险防控能力有限,且在应用有效性评价指标缺乏统一标准。为此,分别基于支持向量机,贝叶斯及随机森林方法建立了信贷违约预测模型,并提出了由准确率、AUC(Area Under ROC Curve)及漏警率所组成的模型性能综合评价指标,同时分别应用信贷信息原始数据和经特征提取后的数据,针对以上三个预测模型性能进行的对比实验表明,由于信贷违约数据具有小基数特征,常规的对初始数据进行特征提取的方法会丧失数据间的高维关联,将严重影响模型的预测效果;同时,基于随机森林的违约预测模型,较其它两个模型表现出更为优异的性能,准确率达到91.2%,综合评价指标达到81.7%,更适用于信贷违约预测领域。展开更多
随着互联网金融的发展,对于商业银行来说网络信贷业务变得越来越重要,而随之而来的信贷风险控制也日益凸显其重要性。本文通过对机器学习相关知识的研究和学习,在对金融机构的信贷数据进行相应的预处理以及数据集拆分之后,构建了基于逻...随着互联网金融的发展,对于商业银行来说网络信贷业务变得越来越重要,而随之而来的信贷风险控制也日益凸显其重要性。本文通过对机器学习相关知识的研究和学习,在对金融机构的信贷数据进行相应的预处理以及数据集拆分之后,构建了基于逻辑回归、SVM、随机森林等方法的多个风险量化决策模型。在进行特征指标的选取、模型参数等细节的研究和设置之后,基于训练集数据来构建风险量化决策模型并对信贷客户的违约行为进行判断,然后将测试集数据代入模型中并把预测值与客户实际还款情况进行对比来验证模型的有效性。通过本文的研究和实验结果表明,通过构建风险量化决策模型来预测信贷客户的还款情况,特别是优化后的随机森林模型和SGD Classifier模型拥有较好的预测效果,具有较高的可行性和准确率。在客户申请贷款业务时,只需要输入对应的特征信息到预测模型中,就能立即对客户的违约情况进行预测。这对信贷风险的控制起着较大的促进作用,也对我国金融信贷市场的稳健发展有着积极的意义。With the development of internet finance, online credit business has become increasingly important for commercial banks, and the accompanying risk control of online credit has also become increasingly important. Through the research and learning of machine learning related knowledge, after the corresponding pre-processing of credit data of financial institutions and the splitting of data sets, this paper constructs multiple risk quantitative decision-making models based on logical regression, SVM, random forest and so on. After studying and setting the selection of feature indicators, model parameters, and other details, a risk quantification decision model is constructed based on the training set data to judge the default behavior of credit customers. Then, the test set data is substituted into the model and the predicted values are compared with the actual repayment situation of customers to verify the effectiveness of the model. The research and experimental results of this paper show that the optimized random forest model and SGD Classifier model have good prediction effect, high feasibility and accuracy by building a risk quantitative decision-making model to predict the repayment of credit customers. When a customer applies for loan business, they only need to input the corresponding feature information into the prediction model to immediately predict the customer’s default situation. This plays a significant role in promoting the control of credit risks and has a positive significance for the stable development of China’s financial credit market.展开更多
With the development of individual consumption credit (ICC) in China, commercial banks have been exposed to more and more risks. The loan failure has been an important problem that the banking must face and revolve. T...With the development of individual consumption credit (ICC) in China, commercial banks have been exposed to more and more risks. The loan failure has been an important problem that the banking must face and revolve. This paper develops a factor system to explain how the borrower's risk is affected, and then establishes a risk monitoring model with AHP to pre-warn the banks how much the risk is.展开更多
文摘Risk early Warning is very important to Banks. This dissertation puts forward a Agent-Based Credit Risk Prediction System,the main parts of the system such as model training, prediction, results analyzing are realized with agent that have the characteristic of autonomy, social ability, reactivity and pro-activeness etc. And also use agent technology to make the Risk Early Warning more effective.
文摘采用信息化手段防控信贷违约风险对保障信贷产业健康发展具有重大的现实意义。传统信贷违约预测模型风险防控能力有限,且在应用有效性评价指标缺乏统一标准。为此,分别基于支持向量机,贝叶斯及随机森林方法建立了信贷违约预测模型,并提出了由准确率、AUC(Area Under ROC Curve)及漏警率所组成的模型性能综合评价指标,同时分别应用信贷信息原始数据和经特征提取后的数据,针对以上三个预测模型性能进行的对比实验表明,由于信贷违约数据具有小基数特征,常规的对初始数据进行特征提取的方法会丧失数据间的高维关联,将严重影响模型的预测效果;同时,基于随机森林的违约预测模型,较其它两个模型表现出更为优异的性能,准确率达到91.2%,综合评价指标达到81.7%,更适用于信贷违约预测领域。
文摘随着互联网金融的发展,对于商业银行来说网络信贷业务变得越来越重要,而随之而来的信贷风险控制也日益凸显其重要性。本文通过对机器学习相关知识的研究和学习,在对金融机构的信贷数据进行相应的预处理以及数据集拆分之后,构建了基于逻辑回归、SVM、随机森林等方法的多个风险量化决策模型。在进行特征指标的选取、模型参数等细节的研究和设置之后,基于训练集数据来构建风险量化决策模型并对信贷客户的违约行为进行判断,然后将测试集数据代入模型中并把预测值与客户实际还款情况进行对比来验证模型的有效性。通过本文的研究和实验结果表明,通过构建风险量化决策模型来预测信贷客户的还款情况,特别是优化后的随机森林模型和SGD Classifier模型拥有较好的预测效果,具有较高的可行性和准确率。在客户申请贷款业务时,只需要输入对应的特征信息到预测模型中,就能立即对客户的违约情况进行预测。这对信贷风险的控制起着较大的促进作用,也对我国金融信贷市场的稳健发展有着积极的意义。With the development of internet finance, online credit business has become increasingly important for commercial banks, and the accompanying risk control of online credit has also become increasingly important. Through the research and learning of machine learning related knowledge, after the corresponding pre-processing of credit data of financial institutions and the splitting of data sets, this paper constructs multiple risk quantitative decision-making models based on logical regression, SVM, random forest and so on. After studying and setting the selection of feature indicators, model parameters, and other details, a risk quantification decision model is constructed based on the training set data to judge the default behavior of credit customers. Then, the test set data is substituted into the model and the predicted values are compared with the actual repayment situation of customers to verify the effectiveness of the model. The research and experimental results of this paper show that the optimized random forest model and SGD Classifier model have good prediction effect, high feasibility and accuracy by building a risk quantitative decision-making model to predict the repayment of credit customers. When a customer applies for loan business, they only need to input the corresponding feature information into the prediction model to immediately predict the customer’s default situation. This plays a significant role in promoting the control of credit risks and has a positive significance for the stable development of China’s financial credit market.
基金This wort wag supported by National Natural Science Fund of China ( 70102007/G0202).
文摘With the development of individual consumption credit (ICC) in China, commercial banks have been exposed to more and more risks. The loan failure has been an important problem that the banking must face and revolve. This paper develops a factor system to explain how the borrower's risk is affected, and then establishes a risk monitoring model with AHP to pre-warn the banks how much the risk is.
