基于熵的模糊综合评价在客户信用体系上的应用

考虑到客户信用体系已成为企业控制信用风险,提高自身竞争实力的管理问题。本文通过对中小型企业的调研分析,设计了一套适合中小型企业的信用评价体系,并将熵权理论应用于模糊综合评价的信用评价方法中,对各指标体系进行了评价和评价结果分析。该模型比传统的评价模型更具有客观性,实践结果符合实际,具有可行性,可以为企业客户信用评价提供参考。     

用于银行贷款决策的企业信用评价模型

信用风险是目前影响我国商业银行安全、高效运营的主要因素。本文针对商业银行贷款发放中的企业信用状况,构建指标体系,建立信用评价模型,运用决策分析的方法提出了确定信用临界值的标准,为提高商业银行贷款决策的科学性提供参考。     

有关非银行金融机构信用风险分析法的研究

自20世纪30年代以来,许多信用风险的分析方法被应用到银行机构的信用风险分析及信用评级中,而在非银行机构的信用风险分析领域,国内却鲜有学者进行探讨。基于此,我们对国内的非银行金融机构信用风险方法作了初步的探索,特别是给出了证券类金融机构的信用风险分析详细的指标体系及评价模型。该指标体系将对商业银行进行证券类客户信用评级起到一定的参考作用。     

基于价值链的生产企业质量信用绩效评价研究

建立企业质量信用绩效评价体系对于提高产品质量安全具有非常重要的作用和意义。本文在借鉴相关研究成果的基础上,建立了企业质量信用管理的价值链模型,并以此模型为依据建立了企业质量信用管理绩效评价指标体系,应用模糊综合评价模型进行企业质量信用绩效综合评价。实例分析表明,通过这种方法,能够发现企业质量信用的贡献因素和制约因素,这将为企业质量信用管理的进一步发展提供决策支持。     

电信客户信用评价实证分析

文章主要立足于客户长期价值提升的思想,基于定性和定量结合的层次分析法建立电信企业的客户信用评价指标体系,重点分析了个人用户的信用评价指标体系的建立过程,最后给出了数据实证分析。     

基于聚类分析与模糊可拓AHP的中小企业信用评价——一个供应链金融的视角

传统信贷政策下,银行对中小企业信用评价时是把单个企业作为评价主体,文章突破了这一限制,提出供应链金融模式下信用指标体系建立的准则,构建了信用评价指标体系,并建立了基于聚类分析和模糊可拓AHP的信用评价模型,该模型把区间模糊数的基本理论引入到AHP群组评价中,利用聚类分析法来确定专家的权重,并结合模糊综合评价法得出评价对象的信用评价值。最后将其用于A企业的信用评价,实例表明了该方法的实效性。     

解决中小企业融资难题的新思路——构建中小企业信用评价模型

近年来,我国中小企业得到了较快发展,但也遇到了各种各样的问题。其中融资难的问题最为突出,已引起全社会的关注。其实,中小企业融资难就难在"信用"上,加强中小企业信用制度特别是信用评价制度建设,构建专门针对中小企业的信用评价指标体系和模型是解决融资难的有效途径。本文从财务、非财务两方面构建中小企业信用评价指标体系,采用主成分分析法、模糊层次分析法构建中小企业信用评价模型,帮助银行客观公正地评价中小企业信用情况,以期解决中小企业融资难的问题。     

基于因子分析的我国中小企业贷款信用风险评价研究

本文针对中小企业发展中所面临的贷款难问题。在对客户资料进行初步筛选后，建立了中小企业贷款信用风险评价指标体系，运用多元统计方法中的因子分析法对样本企业进行信用评估，得出适合我国中小企业的贷款信用风险评价模型，从而为中小企业融资和商业银行评价授信企业提供更为简洁和有效的依据。     

探讨我国信用风险度量模型的应用

本文阐述了财务分析和信用风险度量模型在信用评级上的重要性。从信用评级发展角度出发,对传统信用风险评价方法和现代信用风险模型的发展进行了总结,揭示了传统信用风险评价方法存在的弊端,现代信用风险模型的优点是减弱主观判断的因素。最后通过各个模型的优缺点总结选择适合我国信用风险度量模型应从我国实际情况出发。     

基于产品质量记录的企业质量信用评价模型及应用研究

文章分析了国内外信用评价理论和评价模型,并结合质量信用的特征提出了基于产品质量记录的企业质量信用评价模型、指标体系和评价标准。局部试点证实,该研究成果能够为质量信用管理部门提供技术支撑,为企业质量信用分类和评价工作提供参考。     

Tenancy Databases,Professional Practices and Housing Access among Low‐Income Tenants in the Private Rental Sector in Australia

This essay addresses the impacts of electronic tenancy databases upon social relations in the field of private rental tenancy in Australia. Insights arise from research carried out, in 2002–3, in the eastern, mainland states of Australia that included interviews with tenants and property managers in the private rental sector. Property managers viewed tenancy databases as a tool for efficient, effective and professional risk management, and ‘professional’ practice was held to render misconduct or improper listings extremely unlikely. In this context, tenants were individualized and expected to actively work to construct, maintain and document their reputation as a ‘good tenant’. For tenants, tenancy databases could have a particular, definitive effect. ‘Not being listed’ (along with getting a full rental bond refund, references from previous landlords, and so on) is an indicator that helps build a satisfactory, personal rental history. On the other hand, being ‘listed’ is read as a prime indicator of risk and effectively overriding other aspects of a tenant’s application for tenancy. ‘Listed’ tenants find themselves being forced to shift further and further away from the formal rental market and ultimately into insecure and inappropriate housing arrangements at the periphery.     

Bagging方法在基于日常交易数据解决中小企业的信用评价问题中的应用

关于信用风险评价问题至今已经做了很多研究,各种信用评价模型与方法也已被开发。但是这些模型与方法几乎都是基于财务数据、股票价格或风险调研机构发表的各种调查结果。因为几乎所有的中小企业的财务数据都是非公开的,至今开发的信用评价模型与方法都不免成为无米之炊。为此,本文提出了一个新的途径,只需要根据销售额、顾客付款额、拖欠款额等日常业务处理数据来评价顾客企业的信用度。本文提出一个应用Sagging方法评价顾客信用的系统,其目的在于解决由于异常顾客数比正常顾客要少很多而带来的问题,提高分辨异常顾客的能力。本文所提出的信用评价系统将应用到一个实际企业的信用评价问题中,借此来验证系统的性能和效果。     

关于构建我国保险信用评级制度的思考

保险信用评级制度是为市场参与者提供服务的一种机制,虽然保险信用评价制度已有近百年的历史,但是在中国等发展中国家,信用评级机构还比较少,信用评级制度急需完善。文中从各个方面讨论和思考了我国保险信用评级制度的构建问题,对我国保险信用评级制度起了一定的推动作用。     

A network based fintech inclusion platform

The paper evaluates, from a sustainable finance viewpoint, a machine learning model implemented in a fintech platform, whose aim is to assign credit ratings. The aim of the model is to learn from both micro economic data and macro economic trends the credit rating of companies that ask for credit. We show that the proposed model is able to reward the companies that have better financial performances with better ratings and, therefore, a higher probability/lower cost of obtaining credit. At the same time, the model correctly takes into account the overall evolution of the economy, favoring financial inclusion for the more penalized economic sectors, particularly during crisis times. The model, its application to credit rating, and its evaluation, are illustrated with reference to more than 100,000 European companies before and during the COVID-19 pandemic crisis. The results shows that, while the impact of the financial variables does not change over time, and particularly during the pandemic, the impact of sectors changes considerably, favoring financial inclusion and resilience.     

Firm entry, credit availability and monetary policy

This paper presents a dynamic general equilibrium model that incorporates firm entry under credit rationing. Goods-producing firms in this model are bank dependent in the sense that they have no choice but to borrow funds from banks to cover labor wages that must be paid in advance of production. The results show that a cut in the policy rate enhances firm entry by mitigating the severity of credit rationing. This policy transmission is different from the conventional balance sheet channel in that a change in the policy rate directly affects borrowers' credit availability. I also show that a sudden stop in the credit supply to new firms is most likely to occur shortly after a credit boom. This is because endogenous downward wage rigidity prohibits the credit risk of prospective firms from decreasing enough to re-equilibrate the loan market.     

Transition matrix models of consumer credit ratings

Although the corporate credit risk literature includes many studies modelling the change in the credit risk of corporate bonds over time, there has been far less analysis of the credit risk for portfolios of consumer loans. However, behavioural scores, which are calculated on a monthly basis by most consumer lenders, are the analogues of ratings in corporate credit risk. Motivated by studies of corporate credit risk, we develop a Markov chain model based on behavioural scores for establishing the credit risk of portfolios of consumer loans. Although such models have been used by lenders to develop models for the Basel Accord, nothing has been published in the literature on them. The model which we suggest differs in many respects from the corporate credit ones based on Markov chains — such as the need for a second order Markov chain, the inclusion of economic variables and the age of the loan. The model is applied using data on a credit card portfolio from a major UK bank.     

信用突变下商业银行信用风险预警模型及应用

在信用突变环境下,传统的基于模糊评价技术的信用风险预警模型存在较大的功能局限性。对此,文章运用偏好信息熵与物元可拓理论相融合的偏好熵权物元可拓方法,构建了基于偏好熵权物元可拓的商业银行信用风险预警模型,并进行了预警模型的实证分析。文章认为,基于偏好熵权物元可拓的信用风险预警模型的优势在于,通过偏好信息熵与物元可拓理论相融合的偏好熵权物元可拓方法,使得信用突变下信用风险的预警结果具有较好的平滑性与客观性,此外,该模型的综合关联度预警功能,提高了信用风险预警结果的精确度,很好地解决了信用突变下商业银行信用风险的预警问题。     

我国信用信息共享现状及征信模式研究

本文介绍了我国信用信息共享发展现状,分析了三种典型的信用信息共享模式。根据我国实际情况,构建一种“现阶段以公共征信模式为主导,不断完善我国信用信息共享外部环境建设,积极发展私营征信模式,建立一种‘公共与私营相结合,全国统一’的信用信息共享模式”,为促进我国社会信用体系健康发展起到积极的推动作用。     

信息不完全的信用风险定价模型及其在我国的应用

基于信息不完全的信用风险定价模型与传统的结构化模型和约化模型的最大区别在于它将信息不完全这一前提引入了以信息完全为前提的结构化模型,同时它又考虑了约化模型中强度的优点,引入短期信用风险的度量,成为当前最切合现实的信用风险定价模型。本文认为,应用基于信息不完全的信用风险定价模型来测度信用风险,将具有十分重要的现实意义。     

Spatial dependence in microfinance credit default

Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.