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
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
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
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
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
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
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
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
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
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
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
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
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
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
Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of ...Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.展开更多
The utilization of a green financial system,particularly through the implementation of green credit,plays a pivotal role in fostering environmentally sustainable,low-carbon economic growth and facilitating the transit...The utilization of a green financial system,particularly through the implementation of green credit,plays a pivotal role in fostering environmentally sustainable,low-carbon economic growth and facilitating the transition toward a more ecologically responsible economy.This paper employs a two-way fixed-effects model,utilizing provincial panel data spanning from 2012 to 2020,to investigate the influence of green credit on regional carbon emissions within different regions of China.The results reveal a significant reduction in carbon emissions as a consequence of the green credit program’s implementation.The analysis of the pathway indicates that green credit is instrumental in mitigating carbon emissions by instigating shifts in the energy mix,with evidence suggesting a partial mediating effect.Furthermore,a heterogeneity analysis discovered that the suppressive impact of green credit on carbon emissions is more pronounced in the eastern and western regions of China,while it is less significant in the central and northeastern areas.The implications of this study provide robust evidence in support of the role of green credit in reducing carbon emissions and can serve as a valuable resource for policymakers aiming to promote the expansion of green credit programs and,in turn,contribute to substantial reductions in carbon emissions.展开更多
In recent years,internet finance has garnered increasing attention from the public.Online lending,emerging within the framework of Internet finance as a pivotal component,has witnessed substantial growth.While online ...In recent years,internet finance has garnered increasing attention from the public.Online lending,emerging within the framework of Internet finance as a pivotal component,has witnessed substantial growth.While online credit,within the realm of Internet finance,presents numerous advantages over traditional lending,it concurrently exposes a plethora of credit risk issues.This study aims to facilitate the effective utilization of online credit tools by the young generation within the context of Internet finance.Additionally,it seeks to ensure the overall stability of the Internet finance environment and mitigate risks for the youth.Given the significance of understanding credit risk management for college students in the age of internet finance,this paper adopts the logistic model to evaluate credit risk in internet consumer finance and provides pertinent recommendations from the perspective of the young generation.展开更多
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 mini...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.展开更多
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,Pa...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.展开更多
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-&...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.展开更多
基金supported by the Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘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.
基金This work was supported by University Innovation Research Group of Chongqing(No.CXQT21005).
文摘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.
文摘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.
基金This research was funded by Innovation and Entrepreneurship Training Program for College Students in Hunan Province in 2022(3915).
文摘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.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘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.
基金supported in part by the Advanced and High-level Discipline Construction Fund of Universities in Beijing(No.3201023)in part by the Beijing Electronic Science and Technology Institute of Basic Research Funds Outstanding Master Training Project(No.328202233)in part by the National First-class Undergraduate Discipline Construction of”Communication Engineering”and“Electronic Information Engineering,”and in part by the National Cryptography Science Foundation of China.
文摘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.
基金funded by the State Grid Jiangsu Electric Power Company(Grant No.JS2020112)the National Natural Science Foundation of China(Grant No.62272236).
文摘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.
基金This research was funded by National Natural Science Foundation of China (61906036)Science and Technology Project of State Grid Jiangsu Power Supply Company (No.J2021034).
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.72171182 and 72031009)the Spanish Ministry of Economy and Competitiveness through the Spanish National Research Project(Grant No.PGC2018-099402-B-I00)the Spanish postdoctoral fellowship program Ramon y Cajal(Grant No.RyC-2017-21978).
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘Legumes constitute a major component of sustainable cropping systems due to their biological nitrogen fixing potential. A field study was conducted in 2020 and 2021 at Ashanti-Mampong in the forest transition zone of Ghana to quantify nitrogen credits to carrot from early (70 - 75 days) and medium maturing (80 - 85 days) cowpea varieties (Asetenapa and Soronko) respectively, and Obatanpa maize variety as a reference crop. The experimental design was a split plot with five Nitrogen levels (0, 30, 45, 60 and 90 N kg/ha) applied to carrot as sub-plots following the legumes and the maize variety as main plots. NPK (15:15:15) was applied at the rate of 250 kg/ha to provide the nitrogen. The sub-plot treatments (0, 30, 45, 60 and 90 N kg/ha) were planted following the two cowpea varieties and the maize variety as a reference crop. Soronko had the highest number of nodules (176) while Asetenapa had the lowest nodules (55). Nitrogen credit to carrot from the early-maturing cowpea (Asetenapa) was 32 N kg/ha in the first year of incorporation and 18 N kg/ha in the second year after incorporation. N-credit from the medium-maturing cowpea (Soronko) was 18 N kg/ha and 29 N kg/ha in the first and second year after incorporation respectively. Obatanpa maize variety with 0 kg N/ha fertilizer level produced the lowest carrot yield, indicating that the soil amendment increased yields. The species and maturity of legumes are important determinants of their N credit contribution to crops in rotation.
文摘The utilization of a green financial system,particularly through the implementation of green credit,plays a pivotal role in fostering environmentally sustainable,low-carbon economic growth and facilitating the transition toward a more ecologically responsible economy.This paper employs a two-way fixed-effects model,utilizing provincial panel data spanning from 2012 to 2020,to investigate the influence of green credit on regional carbon emissions within different regions of China.The results reveal a significant reduction in carbon emissions as a consequence of the green credit program’s implementation.The analysis of the pathway indicates that green credit is instrumental in mitigating carbon emissions by instigating shifts in the energy mix,with evidence suggesting a partial mediating effect.Furthermore,a heterogeneity analysis discovered that the suppressive impact of green credit on carbon emissions is more pronounced in the eastern and western regions of China,while it is less significant in the central and northeastern areas.The implications of this study provide robust evidence in support of the role of green credit in reducing carbon emissions and can serve as a valuable resource for policymakers aiming to promote the expansion of green credit programs and,in turn,contribute to substantial reductions in carbon emissions.
文摘In recent years,internet finance has garnered increasing attention from the public.Online lending,emerging within the framework of Internet finance as a pivotal component,has witnessed substantial growth.While online credit,within the realm of Internet finance,presents numerous advantages over traditional lending,it concurrently exposes a plethora of credit risk issues.This study aims to facilitate the effective utilization of online credit tools by the young generation within the context of Internet finance.Additionally,it seeks to ensure the overall stability of the Internet finance environment and mitigate risks for the youth.Given the significance of understanding credit risk management for college students in the age of internet finance,this paper adopts the logistic model to evaluate credit risk in internet consumer finance and provides pertinent recommendations from the perspective of the young generation.
基金supported by grants from the National Science Fund for Distinguished Young Scholars(NSFC No.71025005)the National Natural Science Foundation of China(NSFC No.71433001 and NSFC No.71301006)the National Program for Support of Top-Notch Young Professionals and the Fundamental Research Funds for the Central Universities in BUCT.
文摘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.
文摘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.
基金funded by Research Grants Council,Hong Kong,Grant Number UGC/FDS14/B20/16the Hong Kong Polytechnic University,Grant Number P0030199.
文摘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.
