基于Ext-GBDT集成的类别不平衡信用评分模型

针对现实信用评分业务中样本类别不平衡和代价敏感问题,以及金融机构更期望以得分的方式直观地认识贷款申请人的信用风险的实际需求,提出一种基于Ext-GBDT集成的类别不平衡信用评分模型。使用欠采样的方法从"好"客户(大类)中随机采样多份与全部"坏"客户(小类)等量的样本,分别与全部小类构成训练子集;用不同的训练子集及特征采样和参数扰动的方法训练得到多个差异化的Ext-GBDT子模型;然后使用简单平均法整合子模型的预测概率;最后将信用概率转换为信用评分。在UCI德国信用数据集上,以AUC和代价敏感错误率作为评价指标,与决策树、逻辑回归、朴素贝叶斯、支持向量机、随机森林及其集成模型等当前最为常用的信用评分模型进行对比,验证了该模型的有效性。     

网络购物信用评价模型研究

信用评价在网络购物中扮演者越来越重要的角色,而现有的信用评价体系已经不能够很好地为消费者服务。针对现有信用评价体系当中存在的不足,提出了基于权重比的信用评价模型,最后,通过仿真实验,显示该模型能够更加准确地得到信用评价值,对于消费者的网络购物具有一定的指导意义。     

基于淘宝网的C2C电子商务信用评价模型改进研究

通过分析现有的淘宝信用评价体系,指出存在问题。将交易金额等客观因素引入到模型中,在算法中体现出交易金额对信用值的影响。同时为了反映出交易时间对信用值的变化,提供近期信用值和近期好评率作为参考。新的信用评价模型更加合理,对信用炒作有较好的抑制作用。     

基于淘宝网的C2C电子商务信用评价模型改进研究

通过分析现有的淘宝信用评价体系,指出存在问题。将交易金额等客观因素引入到模型中,在算法中体现出交易金额对信用值的影响。同时为了反映出交易时间对信用值的变化。提供近期信用值和近期好评率作为参考。新的信用评价模型更加合理,对信用炒作有较好的抑制作用．     

基于计算机大数据技术的贷款及贷记卡信用研究

随着国内经济的发展,贷款作为周转资金紧张、扩大产业规模的有效方式正呈现高速发展的状态,但由于用户群体的规模较大,贷款的种类较多,传统的信用评价体系很难满足当前经济发展的需要.本文利用能进行大规模并行处理以及多重分析的计算机大数据技术重建了信用评价模型,并且结合具体案例进行了分析验证.     

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。     

基于信用担保的资金约束供应链订货与定价决策

受自由现金流的限制, 中小企业需要外部融资来实现良性运营, 供应商信用担保贷款是一种有效手段. 考虑银行下侧风险控制的担保贷款模型, 根据供应链购销过程中的订货量和批发价参数决策, 评价供应链内部无风险资本转移过程. 通过建立供应商担保费率、风险担保比率设计和银行利率组合模型, 确定贷款担保系统的最优决策. 研究结果表明: 在具有贷款可获得性的资金约束供应链中, 存在最优订货量与批发价的组合, 且供应商销售收入存在最值; 在担保贷款过程中, 存在最优风险分担比例, 通过设计合理的风险控制模型, 可提高零售商资金水平, 达到供应链的协同.

     

贷款组合信用风险模拟的简化方法

金融机构对贷款组合风险管理的通常方法是在VaR框架下,用蒙特卡罗模拟模拟技术估计期末贷款组合价值分布来计算最大损失.但模拟技术会产生极大的计算工作量.提出了运用计算机模拟技术对贷款组合信用风险(Value at Risk,VaR)的蒙特卡罗模拟进行简化的方法,把一个贷款组合在每个信用评级级别划分为子贷款组合,用同类子贷款组合的非预期损失来获得不同类子贷款组合的最大损失.以期节省运行时间,提高计算效率.模拟结果表明利用该方法计算贷款组合信用风险VaR效率高,能够较准确地获得信用风险值.     

北票市农民贷款不再难

农民贷款难，是长期以来难以解决的问题，北票市的农民信用联合体，使农民贷款难问题得到了妥善解决，信用联合体是农民以户为基本单位自愿结成的相互担保式民间信用贷款组织，北票市目前共组建信用联合体1423个，参加农户8521     

预警应急，信息化打头阵

好“信用”也能向银行换贷款。所以,当“信用经济”呼啸而来的时候,我们还真得认真对待,别把信用不当回事。     

Credit scoring algorithm based on link analysis ranking with support vector machine

Credit scoring is very important in business, especially in banks. We want to describe a person who is a good credit or a bad one by evaluating his/her credit. We systematically proposed three link analysis algorithms based on the preprocess of support vector machine, to estimate an applicant’s credit so as to decide whether a bank should provide a loan to the applicant. The proposed algorithms have two major phases which are called input weighted adjustor and class by support vector machine-based models. In the first phase, we consider the link relation by link analysis and integrate the relation of applicants through their information into input vector of next phase. In the other phase, an algorithm is proposed based on general support vector machine model. A real world credit dataset is used to evaluate the performance of the proposed algorithms by 10-fold cross-validation method. It is shown that the genetic link analysis ranking methods have higher performance in terms of classification accuracy.     

Technology credit scoring model with fuzzy logistic regression

Technology credit scoring models have been used to screen loan applicant firms based on their technology. Typically a logistic regression model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit scoring model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit scoring with fuzzy input and output was presented in this study. The performance of the model is improved compared to that of typical logistic regression. This study is expected to contribute to practical utilization of the technology credit scoring with linguistic evaluation attributes.     

A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm

Credit rating is an assessment performed by lenders or financial institutions to determine a person’s creditworthiness based on the proposed terms of the loan. Frequently, these institutions use rating models to obtain estimates for the probabilities of default for their clients (companies, organizations, government, and individuals) and to assess the risk of credit portfolios. Numerous statistical and data mining methods are used to develop such models. In this paper, the potential of a multicriteria decision-aiding approach is studied. As a first step, the proposed methodology models the problem as a multicriteria evaluation process with multiple and in some cases, conflicting dimensions, which are integrated to derive sound recommendation for DMs. The second step of the methodology involves building a multicriteria outranking model based on ELECTRE III method. An evolutionary algorithm is used to exploit the outranking model. The methodology is applied to a small-scale financial institution operating in the agricultural sector. We compare loan applications based on their attributes and the credit profile of the customer or credit applicant. Our methodology offers the flexibility of combining heterogeneous information together with the preferences of decision makers (DMs), generating both relative and fixed rules for selecting the best loan applications among new and existing customers, which is an improvement over traditional methods The results reveal that outranking models are well suited to credit rating, providing good ranking results and suitable understanding on the relative importance of the evaluation criteria.     

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.     

基于图神经网络的金融征信研究

金融机构对申请借贷的用户进行信用评价是互联网金融领域的前沿方向之一。首先,基于互联网金融借贷网络历史数据,通过用户间借贷关系的网络化建模来反映融合用户节点与周边关系节点相互作用的借贷关联作用的复杂网络。其次,通过引入基于节点中心性结构特征指标的图神经网络模型,提出了具有邻接圈层信息与借贷信用信息耦合的个人征信评估模型。最后,模型在包含756100条交易记录的历史数据集上运行实现,并与BP神经网络算法和RF-Logistic模型进行了对比,结果显示所提模型具有更高的评估准确率。     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

Financing strategies for a capital‐constrained manufacturer in a dual‐channel supply chain

This study investigates a co‐opetition‐type dual‐channel supply chain that consists of a competitive supplier (CS) and a capital‐constrained manufacturer (CCM). The CCM procures key components from and simultaneously competes with the CS in the consumer market. To address the CCM's capital constraint, we consider three financing strategies, namely, trade credit, bank loan, and hybrid financing (i.e., combined use of bank loan and equity financing). Game models are established to characterize the interactions between the CS and CCM. The corresponding equilibria are derived under each strategy. Then, comparative analyses are conducted, and the CS's and CCM's preference structures regarding the three strategies are revealed. On this basis, the equilibrium strategy can be concluded as either trade credit or hybrid financing, but never bank loan. Specifically, when the equity financing ratio is small or large, trade credit is an equilibrium strategy. When the equity financing ratio is medium, the equilibrium strategy between trade credit and hybrid financing is determined by consumers’ product preference and loan interest rate.     

融入区块链技术的大数据征信平台的设计与应用研究

信用是一笔无形资产,良好的信用记录不仅可以带来更高的借款成功率和更低的借款利率,还可以让人们享受信用服务带来的便利。未来信用红利将会突显,但也伴随着个人隐私泄露、信用数据篡改、大数据征信商业化的合法边界不明确等问题。为营造一个良性的互联网信用生态环境,首先总结了现有征信平台中存在的问题,探讨并分析了采用新兴技术解决这些问题的可行性;然后融入区块链技术设计了一种辅助未来征信系统的多源数据共享框架;接着以区块链的多源数据共享为基础,应用人工智能、数据挖掘、智能合约等方法建立了多源异构数据融合的大数据征信平台;最后以互联网借贷为例,设计了一款基于大数据征信平台的去中心化借贷应用。     

A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment

Credit risk assessment has been a crucial issue as it forecasts whether an individual will default on loan or not. Classifying an applicant as good or bad debtor helps lender to make a wise decision. The modern data mining and machine learning techniques have been found to be very useful and accurate in credit risk predictive capability and correct decision making. Classification is one of the most widely used techniques in machine learning. To increase prediction accuracy of standalone classifiers while keeping overall cost to a minimum, feature selection techniques have been utilized, as feature selection removes redundant and irrelevant attributes from dataset. This paper initially introduces Bolasso (Bootstrap-Lasso) which selects consistent and relevant features from pool of features. The consistent feature selection is defined as robustness of selected features with respect to changes in dataset Bolasso generated shortlisted features are then applied to various classification algorithms like Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB) and K-Nearest Neighbors (K-NN) to test its predictive accuracy. It is observed that Bolasso enabled Random Forest algorithm (BS-RF) provides best results forcredit risk evaluation. The classifiers are built on training and test data partition (70:30) of three datasets (Lending Club’s peer to peer dataset, Kaggle’s Bank loan status dataset and German credit dataset obtained from UCI). The performance of Bolasso enabled various classification algorithms is then compared with that of other baseline feature selection methods like Chi Square, Gain Ratio, ReliefF and stand-alone classifiers (no feature selection method applied). The experimental results shows that Bolasso provides phenomenal stability of features when compared with stability of other algorithms. Jaccard Stability Measure (JSM) is used to assess stability of feature selection methods. Moreover BS-RF have good classification accuracy and is better than other methods in terms of AUC and Accuracy resulting in effectively improving the decision making process of lenders.