基于聚类的个人信用评分模型研究

信用卡公司是一个服务性的金融企业,如何提高在服务过程中的服务质量,改进服务方法,使公司的决策更为准确及时,是信用卡公司追求的一个目标。本文介绍了神经网络方法及数据挖掘技术在信用卡公司对用户评分中的应用,对比分析了几种个人信用评分模型建模方法的特点,建立了一种决策树-神经网络个人信用评分模型,并针对该模型提出了一种近邻聚类算法,该算法在信用评分应用中可以得到较理想的结果。     

BP神经网络在电力企业客户信用评价的应用

针对电力企业客户的信用评价问题,提出一个简单实用的BP神经网络信用评价建模方法.该方法首先采用主成分因子分析方法进行信用评价指标的选取,然后应用k-means聚类方法确定信用评价的等级,以此建立BP神经网络信用评价模型.通过实验证明,该BP神经网络信用评价建模方法具有简单实用和高效可靠等优点.     

数据挖掘技术在个人信用评估模型中的应用

为了能够及时、恰当地进行个人信用评估分析，加快信用卡发卡机构的决策速度，介绍了数据挖掘技术在信用卡公司对用户评估中的应用，对比分析了数理统计模型、分类-聚类个人信用评估模型等几种个人信用评估模型建模方法的优缺点。建立了一种决策树-神经网络个人信用评估模型，针对该模型提出了一种近邻聚类算法。该算法不需要事先给定聚类的类别数，可以进行无监督学习。通过对比分析可知，该算法在个人信用评估应用中可以得到较理想的结果。     

数据挖掘技术在个人信用评估模型中的应用

为了能够及时、恰当地进行个人信用评估分析,加快信用卡发卡机构的决策速度,介绍了数据挖掘技术在信用卡公司对用户评估中的应用,对比分析了数理统计模型、分类-聚类个人信用评估模型等几种个人信用评估模型建模方法的优缺点。建立了一种决策树-神经网络个人信用评估模型,针对该模型提出了一种近邻聚类算法。该算法不需要事先给定聚类的类别数,可以进行无监督学习。通过对比分析可知,该算法在个人信用评估应用中可以得到较理想的结果。     

一种区间值聚类的数据挖掘模型

为了使区间值算法的理论与研究成果应用到具体实践中，在企业数据信息的挖掘和分析中发现更有价值的知识，提出了区间值聚类的数据挖掘模型。与传统的数据挖掘方法相比较，区间值聚类的数据挖掘模型更加高效、准确、符合实际。     

基于非平衡数据的LDA-BPNN信用评分模型

随着互联网金融的蓬勃发展，信用评分已成为评判是否为贷款人放款的重要依据，传统的信用评分方法的单一性，不能有效地减少不良贷款带来的巨大损失。提出一种基于非平衡数据的LDA-BPNN方法构建信用评分模型，通过Borderline-SMOTE算法进行非平衡数据过采样，达到平衡样本分布，进一步构建LDA-BPNN模型，显著提升了模型的预测准确率。在不同数据集上，利用多类机器学习算法进行定性和定量对比，并通过pr曲线、AUC等评价指标进行验证，验证结果表明上述模型的分类效果更佳。     

决策树算法在农户小额贷款中的应用研究

在讨论数据挖掘技术的基本概念、决策树方法的基础上,针对近年来农村信用社不良贷款的增加,提出了决策树算法在农户小额信用贷款评价中的应用。利用数据挖掘的预测功能,建立了一种较为科学明了,简单易行的农户信用评价模型,来应用于农村信用社对农户信用的评分,以作为贷款与否的依据。     

基于多项式加工树模型的列联表数据挖掘

针对表格数据挖掘中优势点信息缺失的问题，提出一种列联表自动数据挖掘方法。依据原表中优势点位置，应用多项式加工树模型相关理论对原始数据进行自动树状模型拟合与聚类分组，生成待选假设关系集合，并最终完成参数估计以及拟合优度检验。通过实例证明该算法能够有效提取出优势点的隐含信息与特异规则。     

一个用于空间聚类分析的遗传K-均值算法

空间数据挖掘是数据挖掘的一个新的分支,空间聚类分析是空间数据挖掘中的一个重要研究课题。本文在分析遗传算法及K-均值算法的优越性和不足的基础上,设计了一种遗传K-均值空间聚类分析算法,该算法兼顾了局部收敛和全局收敛性能。实验表明,其结果优于传统K-均值聚类方法及单纯的遗传算法聚类。     

一种基于n-gram短语的文本聚类方法研究

由于文本自身特点使得传统的文档表示模型VSM不能很好地反映文本信息,也让传统数据挖掘聚类算法得不到很好的性能表现。针对传统文本聚类方法中文本表示模型VSM和聚类算法的不足,提出一种基于n-gram短语的文本聚类方法,该方法利用n-gram短语构建短语文档相关模型,将其转换成相关文档模型,在相关文档模型基础上进行文档聚类。实验结果显示,此方法是一种能获得较好聚类结果的有效方法。摘要:     

Hybrid mining approach in the design of credit scoring models

Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a hybrid mining approach in the design of an effective credit scoring model, based on clustering and neural network techniques. We used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and inconsistent clusters, and used neural networks to construct the credit scoring model. The clustering stage involved a class-wise classification process. A self-organizing map clustering algorithm was used to automatically determine the number of clusters and the starting points of each cluster. Then, the K-means clustering algorithm was used to generate clusters of samples belonging to new classes and eliminate the unrepresentative samples from each class. In the neural network stage, samples with new class labels were used in the design of the credit scoring model. The proposed method demonstrates by two real world credit data sets that the hybrid mining approach can be used to build effective credit scoring models.     

商业银行信用风险评估实证分析及方法比较

数据挖掘技术为商业银行信用风险管理问题提供了新的思路和方法。本文运用三种常用的数据挖掘方法——多元判别分析、聚类分析及贝叶斯网络模型,以商业银行的客户信用风险评级指标数据为样本,对信用风险评估方法进行实证分析,对三种方法的验证结果进行比较。结论表明,在信用风险各项属性指标之间条件相互依赖的情况下,贝叶斯网络模型优于其它两种方法。     

An artificial immune classifier for credit scoring analysis

The primary concern of the rating policies for a banking industry is to develop a more objective, accurate and competitive scoring model to avoid losses from potential bad debt. This study proposes an artificial immune classifier based on the artificial immune network (named AINE-based classifier) to evaluate the applicants’ credit scores. Two experimental credit datasets are used to show the accuracy rate of the artificial immune classifier. The ten-fold cross-validation method is applied to evaluate the performance of the classifier. The classifier is compared with other data mining techniques. Experimental results show that for the AINE-based classifier in credit scoring is more competitive than the SVM and hybrid SVM-based classifiers, except the BPN classifier. We further compare our classifier with other three AIS-based classifiers in the benchmark datasets, and show that the AINE-based classifier can rival the AIRS-based classifiers and outperforms the SAIS classifier when the number of attributes and classes increase. Our classifier can provide the credit card issuer with accurate and valuable information of credit scoring analyses to avoid making incorrect decisions that result in the loss of applicants’ bad debt.     

Prediction model building with clustering-launched classification and support vector machines in credit scoring

Recently, credit scoring has become a very important task as credit cards are now widely used by customers. A method that can accurately predict credit scoring is greatly needed and good prediction techniques can help to predict credit more accurately. One powerful classifier, the support vector machine (SVM), was successfully applied to a wide range of domains. In recent years, researchers have applied the SVM-based in the prediction of credit scoring, and the results have been shown it to be effective. In this study, two real world credit datasets in the University of California Irvine Machine Learning Repository were selected. SVM and a new classifier, clustering-launched classification (CLC), were employed to predict the accuracy of credit scoring. The advantages of using CLC are that it can classify data efficiently and only need one parameter needs to be decided. In substance, the results show that CLC is better than SVM. Therefore, CLC is an effective tool to predict credit scoring.     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

Computational time reduction for credit scoring: An integrated approach based on support vector machine and stratified sampling method

With the rapid growth of credit industry, credit scoring model has a great significance to issue a credit card to the applicant with a minimum risk. So credit scoring is very important in financial firm like bans etc. With the previous data, a model is established. From that model is decision is taken whether he will be granted for issuing loans, credit cards or he will be rejected. There are several methodologies to construct credit scoring model i.e. neural network model, statistical classification techniques, genetic programming, support vector model etc. Computational time for running a model has a great importance in the 21st century. The algorithms or models with less computational time are more efficient and thus gives more profit to the banks or firms. In this study, we proposed a new strategy to reduce the computational time for credit scoring. In this approach we have used SVM incorporated with the concept of reduction of features using F score and taking a sample instead of taking the whole dataset to create the credit scoring model. We run our method two real dataset to see the performance of the new method. We have compared the result of the new method with the result obtained from other well known method. It is shown that new method for credit scoring model is very much competitive to other method in the view of its accuracy as well as new method has a less computational time than the other methods.     

A data driven ensemble classifier for credit scoring analysis

This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied. This is essentially a classification task for credit scoring. Given its importance, many researchers have recently worked on an ensemble of classifiers. However, to the best of our knowledge, unrepresentative samples drastically reduce the accuracy of the deployment classifier. Few have attempted to preprocess the input samples into more homogeneous cluster groups and then fit the ensemble classifier accordingly. For this reason, we introduce the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier. This strategy would work better than a direct ensemble of classifiers without the preprocessing step. The proposed ensemble classifier is constructed by incorporating several data mining techniques, mainly involving optimal associate binning to discretize continuous values; neural network, support vector machine, and Bayesian network are used to augment the ensemble classifier. In particular, the Markov blanket concept of Bayesian network allows for a natural form of feature selection, which provides a basis for mining association rules. The learned knowledge is represented in multiple forms, including causal diagram and constrained association rules. The data driven nature of the proposed system distinguishes it from existing hybrid/ensemble credit scoring systems.     

Managing loan customers using misclassification patterns of credit scoring model

A number of credit scoring models have been developed to evaluate credit risk of new loan applicants and existing loan customers, respectively. This study proposes a method to manage existing customers by using misclassification patterns of credit scoring model. We divide two groups of customers, the currently good and bad credit customers, into two subgroups, respectively, according to whether their credit status is misclassified or not by the neural network model. In addition, we infer the characteristics of each subgroup and propose management strategies corresponding to each subgroup.     

Ensemble classification based on supervised clustering for credit scoring

Credit scoring aims to assess the risk associated with lending to individual consumers. Recently, ensemble classification methodology has become popular in this field. However, most researches utilize random sampling to generate training subsets for constructing the base classifiers. Therefore, their diversity is not guaranteed, which may lead to a degradation of overall classification performance. In this paper, we propose an ensemble classification approach based on supervised clustering for credit scoring. In the proposed approach, supervised clustering is employed to partition the data samples of each class into a number of clusters. Clusters from different classes are then pairwise combined to form a number of training subsets. In each training subset, a specific base classifier is constructed. For a sample whose class label needs to be predicted, the outputs of these base classifiers are combined by weighted voting. The weight associated with a base classifier is determined by its classification performance in the neighborhood of the sample. In the experimental study, two benchmark credit data sets are adopted for performance evaluation, and an industrial case study is conducted. The results show that compared to other ensemble classification methods, the proposed approach is able to generate base classifiers with higher diversity and local accuracy, and improve the accuracy of credit scoring.