International Comparison and Lessons for Developing the Personal Credit Market

The rapid development of the global personal credit market has been driven by diversification and technological innovation,both of which serve as key catalysts for industry transformation.This paper conducts a comparative analysis of international personal credit markets to examine successful practices and challenges in credit products,credit assessment,and risk management across various countries.In particular,advancements in financial technology,including big data and artificial intelligence,have created new opportunities for enhancing the accessibility and personalization of credit services.Nevertheless,China’s personal credit market continues to face significant challenges,such as an underdeveloped credit infrastructure,outdated financial regulations,and limited product innovation.Drawing insights from international experiences,China can enhance its credit evaluation systems,strengthen financial regulations,and foster innovation in financial technology to facilitate healthy market development and advance inclusive finance.

Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China

Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Social credit:a comprehensive literature review

To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs.This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory mechanisms.The study considers the credit of various economic agents within the social credit system such as countries(or governments),corporations,and individuals and their credit variations in online markets(i.e.,network credit).A historical review of the theoretical(or model)development of economic agents is presented together with significant works and future research directions.Some interesting conclusions are summarized from the literature review.(1)Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms.(2)The most popular credit scoring methods include expert systems,econometric models,artificial intelligence(AI)techniques,and their hybrid forms.Evaluation indexes should vary across different target agents.(3)The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.

Credit margin of investment in the agricultural sector and credit fungibility: the case of smallholders of district Shikarpur, Sindh, Pakistan

Background:This study examines the access to credit,credit investment,and credit fungibility for small-holder farmers and medium-and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh,Pakistan.Methods:A standardized questionnaire was used to collect data from 87 farmers in the Shikarpur District.We investigated the availability of credit and the use of credit fungibility by farmers with small-,medium-,and large-scale holdings by applying a credit fungibility ratio and an ANOVA technique.The factors that influence the farmers’access to agricultural credit were analyzed using a probit regression model.Results:The results revealed that farmers in both study groups used some amount of their agricultural credit for non-agricultural activities.Further,the results of the probit regression analysis showed that formal education,farming experience,household size,and farm size had a positive and significant influence on the farmers’access to agricultural credit.Conclusion:Based on these findings,our study suggests that a strong monitoring of farmers is needed in the study area.

Detecting conflicts of interest in credit rating changes:a distribution dynamics approach

In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-à-vis conflict of interest and reputation.A novel distribution dynamics approach is employed to compute the probability distribution and,hence,the downgrade and upgrade probabilities of a credit rating assigned by these two CRAs of different compensation systems based on the dataset of 750 U.S.issuers between 2011 and 2018,that is,after the passage of the Dodd–Frank Act.It is found that investor-paid ratings are more likely to be downgraded than issuerpaid ratings only in the lower rating grades,which is consistent with the argument that investor-paid agencies have harsher attitudes toward potentially defaulting issuers to protect their reputation.We do not find evidence that issuer-paid CRAs provide overly favorable treatments to issuers with threshold ratings,implying that reputation concerns and the Dodd–Frank regulation mitigate the conflict of interests,while issuerpaid CRAs are more concerned about providing accurate ratings.

Agricultural Credit Sources and Determinants of Credit Acquisition by Farmers in Idemili Local Government Area of Anambra State

The study is focused on agricultural credit sources and determinants of credit acquisition by farmers in ldemili local government area of Anambra State, Nigeria with specific objectives to： describe socio-economic characteristics of rural farmers; identify sources of agricultural credit available to rural farmers; determine socio-economic factors that influence agricultural credit acquisition of farmers; ascertain reasons for any credit misappropriation and identify problems that constrain farmers from agricultural credit acquisition. Ninety farmers were randomly selected by multi stage random sampling technique. Semi-structured questionnaire was used to elicit data for the study. Descriptive statistics and multiple regression model were used in achieving the objectives. Results indicated that 74.44% of respondents were males with a mean age of 45 years. Majority （76.67%） were married with large house hold sizes. Majority （93.33%） received different level of education, with sources of credit from friends/relatives （30.00%）, cooperative societies （43.33%）, money lenders （14.44%）, and cumulatively from formal sources （（12.22%）. The result of the multiple regression analysis revealed age, household size, membership of cooperative societies, marital status, education level, farm size and amount of loan repaid at varied signs and levels as significant predictors of amount of agricultural credit acquired by farmers. The most common reason given among the respondents （55.89%） of those who misappropriated acquired agricultural credit, was meeting nonfood needs of the household. The farmers encountered problems of high interest rate （78.89%）, lack of collateral （75.56%）, long distance from source of credit （50.00%）, poor harvest （37.78%）, moratorium （33.33%） and delay in loan approval/disbursement （44.44%） as constraints to acquire credit. The study recommends that the state government should pass policies aimed at providing free educative seminars to all illiterate farmers to teach them possible ways and methods of acquiring credit. To ensure mass attendance to such seminars, little incentives should be given to farmer participants.

Credit Default Swaps （CDSs） and Systemic Risks

The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.

Application of Credit Scoring Models in the Analysis of Insolvency of a Brazilian Microcredit Institutio

Credit scoring models are quantitative models used commonly by financial institutions in the measurement and forecasting of credit risk, having a consolidated use in the process of credit concession of these institutions. The purpose of this paper was to evaluate the possibility of application of credit scoring models in a microcredit institution named Fundo Rotativo de A~~o da Cidadania--Cred Cidadania （Revolving Fund of Citizenship Action--Cred Cidadania）, located in Recife （Brazil）. In order to do this, data related to a sample of clients of the Cred Cidadania was collected, and this data was used to develop two types of credit scoring models： one is credit approval, another is called behavioral scoring. The statistical technique applied in the construction of the models was logistic regression. The results of the study demonstrated that the credit scoring models obtain satisfactory performance when used in the analysis of credit risk in the Cred Cidadania microcredit institution, reaching a correct client classification percentile of about 80%. The results also indicate that the use of credit scoring models supplies subsidies to the institution, aiding it in the prevention and reduction of insolvency and in the decrease of its operational costs, two problems that affect their financial sustainability.

Study on the Consumer Credit Development Path of China——From the Perspective of Supply-side Reform

With the development of China's market economy,China's consumer credit has also developed rapidly,but there are also problems such as unbalanced credit structure and uneven regional development.Based on the perspective of supply-side reforms,this article analyzes the opportunities and risks of China's consumer credit development,and puts forward practical and effective suggestions for problems,which is of great significance to the promotion of the healthy development of China's consumer credit.

模糊Credit Metrics模型及其在信用风险评估中的应用

在经典的Credit Metrics模型中,通常假设具有相同信用评级的债务人的贷款是同质的,即具有相同的信用等级转移概率,且在实际应用中这种概率难以精确估计。这些局限性大大限制了该方法在实际中的应用。为了更好地描述和处理关于信用等级转移的模糊信息,借鉴经典Credit Metrics模型的思想,提出了模糊Credit Metrics模型,通过将传统的确定的信用转移矩阵模糊化,从而利用二型模糊变量来对信贷资产的远期价值进行描述和刻画,在此基础上对信贷资产的期望值和在险价值等量化指标进行计算,为基于不确定信息或专家主观判断的信用风险评估提供了一种系统的有效方法。

基于Credit Risk+模型的互联网金融信用风险估计

文章基于Credit Risk+模型,使用互联网信贷平台四个行业的贷款数据,在不同置信水平下,对互联网金融的信用风险水平进行比较分析。结果表明,行业风险因子间协方差相等时,复合伽玛Credit Risk+模型和多元系统风险Credit Risk+模型计算结果几乎一致,与CSFB Credit Risk+模型和两阶段Credit Risk+模型相比能更好地反映贷款组合的非预期损失。行业风险因子间协方差不等时,多元系统风险Credit Risk+模型能克服其他Credit Risk+模型的缺陷,综合考量系统风险和行业风险的影响,能更好地估计贷款组合的信用风险水平。

基于Credit Portfolio View的信用风险度量模型研究

结合我国贷款企业的特点,Credit Portfolio View模型的转移矩阵中信用等级违约概率除了受宏观经济因素影响外,还受到行业因素、地区因素、规模因素以及企业所有制性质等因素影响,这些因素使得同一信用等级下的企业历史违约率统计出现差异。笔者对Credit Portfolio View模型违约因素做了宏观、行业、地区三个维度的扩展,并采用Logit模型与随机模拟相结合的方法,对模型参数进行了估计。

CreditMetrics模型中转移概率和风险价值的计算

CreditMetrics模型是量化信用风险的管理模型,信用矩阵转移概率的确定是该模型的核心问题之一.该文提出一种信用矩阵转移概率的估计方法,采用随机模拟的数据进行验证,并通过误差分析确定较为合适的样本容量.同时改进原有模型中对贷款现金流的计算方法,即一类客户在n年内信用等级的各种转移情况下的贷款现金流折算.最后采用核估计方法计算贷款风险值VaR,并与原有模型的计算结果进行比对.根据比对结果,可以证明此方法是行之有效的.

基于Credit Metrics的城投公司结构性风险评价研究

随着我国城市基础设施建设规模的快速扩大,城市基础设施建设投资公司项目类型多元化与融资方式多元化匹配造成的结构性风险已经成为影响其健康发展的主要问题.在分析了城投公司结构性风险的发生机理及传导机制的基础上,针对城投公司债务的信用转移问题,运用Credit Metrics模型对城投公司结构性风险进行评价研究.结果表明,城投公司存在结构性风险且隐患巨大.一旦资金链出现问题,城投公司的债务将出现严重的违约情况.

基于CreditMetrics模型评估银行信贷的信用风险

随着市场竞争日趋激烈,金融风险管理显得越来越重要。文章首先论述CreditMetrics模型的建模逻辑过程及其特点;基于风险价值(var)概念进行蒙特卡罗模拟,计算得出某商业银行信贷数据的核心参数:信用风险转移矩阵、门槛率、违约回复率以及最终的风险价值,进而利用这些参数测算出该商业银行贷款的风险等级及其分布。