储蓄类国债与其他个人金融产品的比较分析——以许昌市为例

个人金融产品对商业银行和客户具有双赢作用,不仅能给客户带来超过储蓄存款的收益预期,而且也给商业银行带来了可观的手续费收入。目前,销售个人金融产品的手续费收入已成为商业银行中间业务收入的重要组成部分。各家商业银行都非常重视个人金融产品的研发与推广,专门成立了个人金融业务部,来具体负责个人金融产品相关业务     

互联网金融生态圈内融资信用风险仿真评测研究

本文从互联网金融生态圈内借款人的信用风险角度出发,通过融资主体信用风险评测指标体系,依据人人贷平台数据,建立自组织竞争型神经网络预测模型和BP神经网络模型都能适用于互联网金融生态圈内融资主体平台借款人信用风险评估,具有较强的风险预测评估能力。综合运用这两类模型,能对互联网金融生态圈内融资主体可能出现的违约情况进行预警,有效防范风险,增强互联网金融生态圈的信用等级。     

互联网金融浪潮下城商行经营发展面临的问题与挑战

近年来,伴随着科学技术的进步,以搜索引擎、大数据、社交网络和云计算等互联网技术手段为基础的互联网金融的迅速发展,因其较低的运营成本、便利的操作服务、完善的客户信用等级评估系统和惠及面较广的需求旺盛客户源,对传统商业银行业务产生了一定程度上的冲击。城市商业银行作为商业银行体系中的重要组成部门,由于资产规模和业务总量不大、IT系统建设滞后等,必然面临更多的问题与挑战,但同时如果能够积极转型,发挥自身优势,又未尝不是一个良好的机遇。     

互联网金融冲击下商业银行个人理财业务的机遇和挑战

当前,互联网金融正以方兴未艾之势深刻影响和改变传统金融体系,渗透和撼动着商业银行个人理财业务。一方面互联网金融给商业银行个人理财业务带来新的机遇,启动了平民理财市场,开创了碎片化理财;另一方面互联网金融给商业银行个人理财业务带来了挑战,抢占了商业银行个人理财业务的市场份额,弱化了银行的支付中介功能,改变了客户的思想观念。面对互联网金融的巨大冲击,商业银行个人理财业务必须具备"服务海量用户"、"关注个性化需求"、"重视创新"、"重视用户体验"等互联网思维模式,方能应对未来互联网金融带来的振动和冲击。     

互联网金融时代下我国商业银行的发展趋势

互联网投资理财产品、网络小额贷款和第三方支付的出现给我国商业银行带来巨大冲击,目前我国商业银行客户流失不断,盈利空间逐步缩小,以传统业务为主的竞争优势逐渐减弱。面对互联网金融的大潮,一些商业银行开始打破传统经营模式,依靠金融科技不断创新变革,呈现出新的发展趋势。     

商业银行表外业务风险不容忽视

一、信用风险金融市场中客观存在的信息不对称是信用风险产生的症结所在。信用交易在信息不对称的情况下进行,银行为客户提供着保证并规避风险,做为投资者暴露于不确定的信用风险中。由于表外业务涉及的客户和交易的金融属性较强,这个过程中金融生态环境的优劣对投资者的利益影响是至关重要的。我国现行的金融生态环境尚不能有效保护投资者的利益,商业银行尤其应关注来自客户个人或企业的诸多信用风险。     

商业银行个人客户对服务渠道的偏好

本文以山东省某商业银行个人客户交易渠道的抽样数据为样本,运用现代统计学中的对应分析方法,从客户的年龄、学历和家庭收入出发,对商业银行不同年龄、学历和家庭收入客户的渠道偏好进行了定量研究.通过研究找出了不同年龄、学历和家庭收入的客户与商业银行不同渠道之间所存在的对应关系,并根据这些对应关系,挖掘出不同年龄、学历和家庭收入客户对商业银行不同渠道的偏好.在此基础上.对商业银行营业网点如何运用这些渠道偏好关系对客户进行分流以及实施差别化营销和服务提出了具体建议,以期能为商业银行营业网点的客户分流和差别化营销提供参考.     

商业银行个人客户对服务渠道的偏好

本文以山东省某商业银行个人客户交易渠道的抽样数据为样本,运用现代统计学中的对应分析方法,从客户的年龄、学历和家庭收入出发,对商业银行不同年龄、学历和家庭收入客户的渠道偏好进行了定量研究.通过研究找出了不同年龄、学历和家庭收入的客户与商业银行不同渠道之间所存在的对应关系,并根据这些对应关系,挖掘出不同年龄、学历和家庭收入客户对商业银行不同渠道的偏好.在此基础上.对商业银行营业网点如何运用这些渠道偏好关系对客户进行分流以及实施差别化营销和服务提出了具体建议,以期能为商业银行营业网点的客户分流和差别化营销提供参考.     

个人抵押贷款违约的相关性因素——基于Copula模型的研究

运用国内商业银行积累的大量数据,统计得到银行个人客户住房抵押贷款多年度、不同信用等级、不同身份特征、分行业和分地区的违约情况,进行非线性的拟合分析,并采用Copula函数度量个人客户违约之间的相关性及厚尾特征。研究表明,房屋价格、客户性别以及受教育程度等与违约概率相关性比较低,在考察的样本区间内,这些因素不显著导致违约发生。另外,信用等级、收入结构和抵押担保剩余额度是影响个人违约决策的重要变量。所采用的模型在个人住房抵押贷款定价与风险管理中获得较好效果,银行可以根据违约状况的变动制定动态利率,随时准备弥补损失。     

我国商业银行信用风险管理策略研究

在我国金融分业经营,利率尚未市场化,存、贷款利差收入仍是商业银行的主要收入来源的情况下,信用风险是我国商业银行面临的主要风险。对信用风险的准确度量和有效管理,既是商业银行进行贷款甄别、定价,提高风险的识别、评估、预警能力,从根源上降低不良资产的关键,也是监管部门进行风险性监管的基础。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

The Importance of Adverse Selection in the Credit Card Market: Evidence from Randomized Trials of Credit Card Solicitations

Analyzing unique data from multiple large‐scale randomized marketing trials of preapproved credit card solicitations by a large financial institution, we find that consumers responding to the lender's inferior solicitation offers have poorer credit quality attributes. This finding supports the argument that riskier type borrowers are liquidity or credit constrained and, thus, have higher reservation loan interest rates. We also find a more severe deterioration ex post in the credit quality of the booked accounts of inferior offer types relative to superior offers. After controlling for a cardholder's observable risk attributes, demographic characteristics, and adverse economic shocks, we find that cardholders who responded to the inferior credit card offers are significantly more likely to default ex post. Our results provide evidence on the importance of adverse selection effects in the credit card market.     

Merchants and credit cards: Determinants,perceptions and current practices — A case of Malaysia

The credit card industry is made up of two business segments: the issuance of credit cards to consumers and the acquisition of merchants into the credit card payment scheme. Accepting credit card payments is an important business decision that involves cost, benefits and risk considerations. The paper discusses the merchants' personal and business characteristics and the perceptions of merchants towards the credit card payment system, both of which contribute significantly to merchants' acceptance of credit card payments. It is found that perceptions related to the social influence and performance expectancy constructs of the Unified Theory of Acceptance and Use of Technology play the most important role in a merchant's decision.     

An exploratory study of factors affecting credit card brand and category selection in India

This article aims to identify various credit card selection factors in the context of an Asian emerging economy – India. Considering the recent emergence of the three-tier banking system in India, the study proposes to investigate whether there exists an association between credit card selection factors and the type of credit card issuing bank (public, private national or private foreign). The article also seeks to find out whether the income level of the individual determines the type of credit card bank category selection, as prior literature suggests that demographic factors are closely related to credit card selection and usage patterns. The analysis was carried out using a mixed method research design involving thematic analysis of focus group data, factor analysis, PERMAP analysis and multiple correspondence analysis in different phases. Five primary credit card selection factors were identified. A strong correspondence between credit card selection factors, personal income levels and choice of credit card issuing bank was found. Accordingly, credit card selection factors and personal income level were together identified as factors affecting the type of bank selected. The phenomenon of bank category-based stereotyping of brands is emphasized based on the analysis. The implications of this phenomenon with respect to brand identity conception, target segmentation, brand positioning and marketing communication are discussed.     

Measuring Integrated Market and Credit Risk in Bank Portfolios: An Application to a Set of Hypothetical Banks Operating in South Africa

The banking crises of the ‘90s emphasize the need to model the connections between financial environment volatility and the potential losses faced by financial institutions resulting from correlated market and credit risks. Due to the number of variables that must be modeled and the complexity of the relationships an analytical solution is not feasible. We present here a numerical solution based on a simulation model that explicitly links changes in the relevant variables that characterize the financial environment and the distribution of possible future bank capital ratios. This forward looking quantitative risk assessment methodology allows banks and regulators to identify potential risks before they materialize and make appropriate adjustments to bank portfolio credit qualities, sector and region concentrations, and capital ratios on a bank by bank basis. It also has the potential to be extended so as to assess the risks of correlated failures among a group of financial institutions (i.e., systemic risk analyses). This model was applied by the authors to the study of the risk profile of the largest South African Banks in the context of the Financial System Stability Assessment program undertaken by the IMF in 1999. In the current study, we apply the model to various hypothetical banks operating in the South African financial environment and assess the correlated market and credit risks associated with business lending, mortgage lending, asset and liability maturity matches, foreign lending and borrowing, and direct equity, real estate, and gold investments. It is shown to produce simulated financial environments (interest rates, exchange rates, equity indices, real estate price indices, commodity prices, and economic indicators) that match closely the assumed parameters, and generate reasonable credit transition probabilities and security prices. As expected, the credit quality and diversification characteristics of the loan portfolio, asset and liability maturity mismatches, and financial environment volatility, are shown to interact to determine bank risk levels. We find that the credit quality of a bank's loan portfolio is the most important risk factor. We also show the risk reduction benefits of diversifying the loan portfolio across various sectors and regions of the economy and the importance of accounting for volatility shocks that occur periodically in emerging economies. Banks with high credit risk and concentrated portfolios are shown to have a high risk of failure during periods of financial stress. Alternatively, banks with lower credit risk and broadly diversified loan portfolios across business and mortgage lending are unlikely to fail even during very volatile periods. Asset and liability maturity mismatches generally increase bank risk levels. However, because credit losses are positively correlated with interest rate increases, banks with high credit risk may reduce overall risk levels by holding liabilities with longer maturities than their assets. Risk assessment methodologies which measure market and credit risk separately do not capture these various interactions and thus misestimate overall risk levels.     

浅析农户个人信贷业务拓展中的障碍和对策建议

中国经济长期以来呈现出“重城轻乡”的二元制结构特点,在这种经济结构背景下的城乡个人信贷政策却没有实行差别化,这是造成我国农村个人信贷业务难以推进的主要障碍。目前银行在针对个人办理贷款时都有一些硬性条件,包括面临风险时贷款利率会适当上浮,个人贷款期限以年为单位,要求借款人一定时间内支出与收入比不得超过50％,这些政策和限制性条件对农户贷款并不适用,在为农户办理贷款时应适用新的信贷政策,使其更贴近农户的实际信贷需求。     

商业银行信用卡业务个人信用评估模型研究

个人信用评估是商业银行信用卡业务一直存在的问题,本文通过信用卡客户信息数据进行实证研究,构建基于C4.5算法的个人信用评估模型,并对信用模型进行了预测和评估,模型有较好的预测效果,可以为信用卡业务的个人信用风险管理提供借鉴。     

信用卡、风险应对与城镇家庭股市参与

信用卡市场的发展有利于家庭平滑消费,但是否会影响家庭的投资决策尚缺少事实检验。基于中国家庭金融调查(CHFS)2011和2013年的调查数据,本文首次检验了信用卡对城镇家庭股市投资的影响。研究发现:(1)控制家庭财富、融资渠道、社会资本等特征,持有信用卡在边际上促进了家庭的股市参与率,股市投资额取决于信用额度。(2)中国信用卡市场的发展,并未像流动性约束理论预测的可以平滑消费,反而刺激了家庭风险性资产的配置,最终提高了储蓄率。原因在于,中国家庭更多地将信用卡作为应对未来短期突发性风险的金融工具,从而在当期将更多资产配置在风险资产上,以实现资本积累。使用信用卡数目、信用额度等多种衡量方式,并采用工具变量法(IV)和面板数据固定效应模型等解决内生性问题后,该结论稳健。该研究有助于理解信用卡在中国家庭投资决策的作用,并为流动性约束或有限股市参与假说提供中国经验。     

银保协作、风险自留与银行信用风险补偿——基于银保信贷系统的分析视角

在分析风险自留的内生逻辑基础上,进一步分析风险自留实现银行信用风险补偿的内在机理。研究表明：银保信贷系统通过对贷款企业个体风险与事先设定的平均代偿风险的匹配性甄别,实现对银行信用风险的分级补偿功能。针对银行超预期信用风险,先行实施银行风险拨备机制对银行平均代偿风险进行补偿,然后实施超额风险自留机制对超过平均代偿风险的银行超额风险部分再次进行补偿,超额风险自留补偿基金将由银行与担保机构依据各自的风险均衡配置阈值占比共同筹集,以此来实现银行信用风险分级补偿目标。并以此为依据,设计了银保信贷系统风险自留机制。     

Credit rationing in small firm-bank relationships

I study credit rationing in small firm-bank relationships by using a unique data set of matched loan applications and contracts. I establish the degree of credit rationing by relating a firm's requested loan amount to the bank's granted amount. In line with theoretical predictions, credit rationing is higher for opaque than transparent firms at the beginning of their bank relationships and decreases over time for both. After testing for several alternative explanations, the results suggest that information and incentive problems explain the observed credit rationing and its dynamics.