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Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China
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作者 Yan Zhang Yi Huang +1 位作者 Fan Zhang Zeng Tang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2024年第5期1774-1785,共12页
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.Howeve... 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. 展开更多
关键词 formal credit herders EXPENSE Qinghai-Xizang Plateau
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Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model
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作者 XU Yajuan WANG Guojing 《应用概率统计》 CSCD 北大核心 2024年第4期572-587,共16页
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... 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. 展开更多
关键词 PRICING catastrophe option credit risk REGIME-SWITCHING measure change
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Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation
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作者 Yu Dong Xiaoyu Huang +1 位作者 Hongan Gan Xuyang Liu 《中国科学技术大学学报》 CAS CSCD 北大核心 2024年第5期49-62,I0007,共15页
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 mod... 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. 展开更多
关键词 automobile enterprises green credit system dynamics reward and punishment mechanism
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Research on Early Warning of Banking Crises from the Perspective of Credit Structures
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作者 Zhou Yuqin Luo Zixuan Wu Shan 《Contemporary Social Sciences》 2024年第3期45-63,共19页
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 an... 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. 展开更多
关键词 banking crises credit expansion transnational empirical evidence structural perspective
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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection
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作者 Honghao Zhu MengChu Zhou +1 位作者 Yu Xie Aiiad Albeshri 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期377-390,共14页
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... 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. 展开更多
关键词 credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator
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Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System
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作者 Guifei JING Chen MIAO +4 位作者 Hengxue LUO Jiang XU Xiaoyuan PENG Yang CUN Xinghu LI 《Asian Agricultural Research》 2024年第6期25-32,54,共9页
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 th... 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. 展开更多
关键词 BEIDOU Navigation Satellite System (BDS) SPATIOTEMPORAL blockchain DIGITAL credit of AGRICULTURAL products DIGITAL TRADE
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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection 被引量:1
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作者 Zhanyang Xu Jianchun Cheng +2 位作者 Luofei Cheng Xiaolong Xu Muhammad Bilal 《Computers, Materials & Continua》 SCIE EI 2023年第6期5573-5595,共23页
Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise info... Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation. 展开更多
关键词 Federated learning feature selection credit risk assessment MSEs
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A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost 被引量:1
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作者 Wang Ning Siliang Chen +2 位作者 Fu Qiang Haitao Tang Shen Jie 《Computers, Materials & Continua》 SCIE EI 2023年第3期5951-5965,共15页
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec... With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance. 展开更多
关键词 credit card fraud noisy samples penalty factors AWTadaboost algorithm
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A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network 被引量:1
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作者 Yalong Xie Aiping Li +2 位作者 Biyin Hu Liqun Gao Hongkui Tu 《Computers, Materials & Continua》 SCIE EI 2023年第9期2707-2726,共20页
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr... Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses. 展开更多
关键词 credit card fraud detection imbalanced classification feature fusion generative adversarial networks anti-fraud systems
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Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework 被引量:1
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作者 Zhen‑Song Chen Jia Zhou +5 位作者 Chen‑Ye Zhu Zhu‑Jun Wang Sheng‑Hua Xiong Rosa M.Rodríguez Luis Martínez Mirosław J.Skibniewski 《Financial Innovation》 2023年第1期2939-2991,共53页
Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for ban... Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment. 展开更多
关键词 Real estate enterprise credit risk assessment PROMETHEE II Best–worst method Proportional hesitant fuzzy linguistic term sets
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An author credit allocation method with improved distinguishability and robustness
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作者 Yang Li Tao Jia 《Journal of Data and Information Science》 CSCD 2023年第3期15-46,共32页
Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.... Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.Design/methodology/approach:We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts.We also remove the target paper in calculating the contribution of co-citations.Following previous studies,we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society(APS)and the Microsoft Academic Graph(MAG)dataset to test the accuracy of our proposed method(NCCAS).In addition,we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS.Finding:Compared with the state-of-the-art methods,NCCAS gives the most accurate prediction of Nobel laureates.Furthermore,the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors.The results by NCCAS are also more robust to malicious manipulation.Finally,we perform ablation studies to show the contribution of different components in our methods.Research limitations:Due to limited ground truth on the true leading author of a work,the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.Practical implications:NCCAS is successfully applied to a large number of publications,demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.Originality/value:Compared with existing methods,NCCAS not only identifies the leading author of a paper more accurately,but also makes the deification more distinguishable and more robust,providing a new tool for related studies. 展开更多
关键词 Citation network credit allocation Share of credit Leading author
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RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment
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作者 Qiuying Shen Wentao Zhang Mofei Song 《Computers, Materials & Continua》 SCIE EI 2023年第4期197-217,共21页
With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is o... With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly. 展开更多
关键词 Electricity consumption enterprise credit scoring edge computing deep learning
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Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain
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作者 Han Yanyan Wei Wanqi +1 位作者 Dou Kaili Li Peng 《Computers, Materials & Continua》 SCIE EI 2023年第6期5193-5211,共19页
As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data s... As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency. 展开更多
关键词 Dual blockchain credit data sharing truffle framework digital envelope ipfs
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Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring
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作者 Susan A. Okeyo Galcano C. Mulaku Collins M. Mwange 《Journal of Geographic Information System》 2023年第5期524-539,共16页
According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food con... According to the Food and Agriculture Organization of the United Nations (FAO), there are about 500 million smallholder farmers in the world, and in developing countries, such farmers produce about 80% of the food consumed there;their farming activities are therefore critical to the economies of their countries and to the global food security. However, these farmers face the challenges of limited access to credit, often due to the fact that many of them farm on unregistered land that cannot be offered as collateral to lending institutions;but even when they are on registered land, the fear of losing such land that they should default on loan payments often prevents them from applying for farm credit;and even if they apply, they still get disadvantaged by low credit scores (a measure of creditworthiness). The result is that they are often unable to use optimal farm inputs such as fertilizer and good seeds among others. This depresses their yields, and in turn, has negative implications for the food security in their communities, and in the world, hence making it difficult for the UN to achieve its sustainable goal no.2 (no hunger). This study aimed to demonstrate how geospatial technology can be used to leverage farm credit scoring for the benefit of smallholder farmers. A survey was conducted within the study area to identify the smallholder farms and farmers. A sample of surveyed farmers was then subjected to credit scoring by machine learning. In the first instance, the traditional financial data approach was used and the results showed that over 40% of the farmers could not qualify for credit. When non-financial geospatial data, i.e. Normalized Difference Vegetation Index (NDVI) was introduced into the scoring model, the number of farmers not qualifying for credit reduced significantly to 24%. It is concluded that the introduction of the NDVI variable into the traditional scoring model could improve significantly the smallholder farmers’ chances of accessing credit, thus enabling such a farmer to be better evaluated for credit on the basis of the health of their crop, rather than on a traditional form of collateral. 展开更多
关键词 credit Scoring Machine Learning Geospatial Technology Migori
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Trade credit financing for supply chain coordination under financial challenges:a multi‑leader–follower game approach
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作者 Faranak Emtehani Nasim Nahavandi Farimah Mokhatab Rafiei 《Financial Innovation》 2023年第1期131-169,共39页
This study is designed to solve supply chain inefficiencies caused by some members’financial problems,such as capital shortages and financing restrictions in a stochastic environment.To this end,we have established a... This study is designed to solve supply chain inefficiencies caused by some members’financial problems,such as capital shortages and financing restrictions in a stochastic environment.To this end,we have established a supply chain finance framework by designing two novel coordinating contracts based on trade credit financing for different problem settings.These contracts are modeled in the form of multi-leader Stackelberg games that address horizontal and vertical competition in a supply chain consisting of multiple suppliers and a financially constrained manufacturer.However,previous studies in the trade credit literature have addressed only simple vertical competition,that is,seller-buyer competition.To solve the proposed models,two algorithms were developed by combining population-based metaheuristics,the Nash-domination concept,and the Nikaido-Isoda function.The results demonstrate that the proposed supply chain finance framework can eliminate supply chain inefficiencies and make a large profit for suppliers,as well as the financially constrained manufacturer.Furthermore,the results of the contracts’analysis showed that if the manufacturer is required to settle its payments to suppliers before the end of the period,the trade credit contract cannot coordinate the supply chain because of a lack of incentive for suppliers.However,if the manufacturer is allowed to extend its payments to the end of the period,the proposed trade credit financing contract can coordinate the supply chain.Finally,the sensitivity analysis results indicate that the worse the financial status of the manufacturer,the more bargaining power suppliers have in determining the contract parameters for more profit. 展开更多
关键词 Supply chain coordination Financial constraint Multi-leader–follower Stackelberg game Trade credit financing Population-based metaheuristics
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Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique
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作者 Yih Bing Chu Zhi Min Lim +3 位作者 Bryan Keane Ping Hao Kong Ahmed Rafat Elkilany Osama Hisham Abusetta 《Journal of Cyber Security》 2023年第1期33-46,共14页
The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machin... The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems. 展开更多
关键词 Machine learning credit card fraud ensemble learning non-sampled dataset hybrid AI models European credit card holder
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Present Situation, Problems and Strategies of Agricultural Credit Policy in Dingxi City
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作者 Xiaoli ZHU Wenjie FAN Zongli ZHANG 《Asian Agricultural Research》 2023年第9期11-12,15,共3页
This paper analyzes the current situation of agricultural credit policy in Dingxi City,and further studies the problems in the implementation of credit policy in Dingxi City,and puts forward strategies and suggestions... This paper analyzes the current situation of agricultural credit policy in Dingxi City,and further studies the problems in the implementation of credit policy in Dingxi City,and puts forward strategies and suggestions according to these problems. 展开更多
关键词 Agricultural credit policy Agricultural development Dingxi City
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The Impact of Credit Ratings on Financial Performance (ROA) and Value Creation (Tobin’s Q)
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作者 Nazário Augusto de Oliveira Leonardo Fernando Cruz Basso 《Chinese Business Review》 2023年第2期69-85,共17页
This study employs a bibliometric and systematic approach to examine the impact of credit ratings as a measure of financial performance for companies listed in the S&P 500 index.The study identified a knowledge ga... This study employs a bibliometric and systematic approach to examine the impact of credit ratings as a measure of financial performance for companies listed in the S&P 500 index.The study identified a knowledge gap as only two researches were found,one suggesting and another using credit ratings to measure financial performance.Most researches use leverage,profitability,liquidity,and Share Return measures to explain financial performance.The empirical analysis uses the data of 2,398 observations of 240 companies rated by S&P Global Ratings for the period 2009-2013,applying a Generalized Method of Moments(GMM)methodology to estimate the models due to its ability to address potential endogeneity issues.The study considers Return on Assets(ROA)and Tobin’s Q as dependent variables.It incorporates credit ratings(CRWLTA)along with variables such as Total Debt to Total Assets(TDTA),Total Shareholder Return(TSR),EBITDA Interest coverage(EBITDAICOV),Quick Ratio(QR),Altman’s Z-Score(AZS),as well as macroeconomic factors like Gross Domestic Product(GDP)growth,inflation(Consumer Price Index-CPI),and the Federal Reserve Interest Rate(FDRI)as independent variables.The study argues that credit ratings,which incorporate historical data and confidential information about companies’strategies,provide reliable forward-looking creditworthiness assessments to the market.It is supported by specialized rating agencies that employ their methodologies.However,the findings suggested that CRWLTA,had a negative relationship with Q Tobin,although it was not statistically significant,and a negative relationship with ROA that was on the verge of significance. 展开更多
关键词 credit ratings financial performance risk management
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我国社会信用体系建设面临的主要挑战及对策建议 被引量:5
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作者 韩家平 《征信》 北大核心 2024年第2期1-5,共5页
党的十八大以来,国家高度重视社会信用体系建设,在一些基础领域和关键环节取得了重要进展,总体社会信用状况得到明显改善。然而,社会信用体系建设是一项长期且极为复杂的系统工程,目前仍面临着理论研究和法治建设滞后,社会信用体系建设... 党的十八大以来,国家高度重视社会信用体系建设,在一些基础领域和关键环节取得了重要进展,总体社会信用状况得到明显改善。然而,社会信用体系建设是一项长期且极为复杂的系统工程,目前仍面临着理论研究和法治建设滞后,社会信用体系建设的目标定位和功能边界不够清晰,商务征信体系不健全、企业账款拖欠严重,以及其他发展不平衡不充分等问题。建议在系统总结前期经验教训的基础上,加强理论研究,厘清认识和实践误区,进一步明确社会信用体系建设的目标定位和功能边界,加快推进信用立法,健全商务信用体系,破解企业账款拖欠难题,大力发展信用经济,并做好面向公众的宣传引导工作。 展开更多
关键词 社会信用体系 信用立法 政务诚信 商务信用体系 信用经济 账款拖欠
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Why The Constitution Should Protect Personal Credit Information?——An Approach of Right Argumentation
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作者 李艺 SU Yilon 《The Journal of Human Rights》 2023年第2期328-346,共19页
Protecting personal credit information through constitutional rights is not only essemtial for individuals to defend against infringements on their personal credit information rights and interests by public power in t... Protecting personal credit information through constitutional rights is not only essemtial for individuals to defend against infringements on their personal credit information rights and interests by public power in the social credit system,but also a requirement for unified legislation on social credit to explore the basis for constitutional norms.In the era of the credit economy,personal credit information has become a vital resource for realizing personal autonomy.Along with the increase in the state’s supervision and control of personal credit,the realization of the autonomous value in the interests related to personal credit information has also set more obligations for the state.Therefore,interests related to personal credit information should be regarded as a constitutional right.Because of its significant economic interest and value,the right to personal credit information should be classified as a constitutional property right.As a constitutional property right,the right to personal credit information can not only help protect people’s economic interests,but also achieve the goal of safeguarding their personality interests. 展开更多
关键词 right to personal credit information constitutional rights social and economic rights property rights
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