RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

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.

Leveraging Geospatial Technology for Smallholder Farmer Credit Scoring

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.

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Although psychometric features have been considered for alternative credit scoring,they have not yet been applied to peer-to-peer(P2P)lending because such information is not available on platforms.This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’job category.We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator(MBTI)that fits each job category.Applying the distance in this space to Lending Club data,we used locally weighted logistic regression to vary the coefficients of the variables,which affect loan repayments,with each MBTI type for predicting the default probability.We found that each MBTI type’s credit scoring model has different significant variables.This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

Small-and medium-sized enterprises(SMEs)have a crucial influence on the economic development of every nation,but access to formal finance remains a barrier.Similarly,financial institutions encounter challenges in the assessment of SMEs’creditworthiness for the provision of financing.Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements.SMEs are perceived as unorganized in terms of financial data management compared to large corporations,making the assessment of credit risk based on inadequate financial data a cause for financial institutions’concern.The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions.To address the issue of limited financial record keeping,this study developed and validated a system to predict SMEs’credit risk by introducing a multicriteria credit scoring model.The model was constructed using a hybrid best–worst method(BWM)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).Initially,the BWM determines the weight criteria,and TOPSIS is applied to score SMEs.A real-life case study was examined to demonstrate the effectiveness of the proposed model,and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations.The findings indicated that SMEs’credit history,cash liquidity,and repayment period are the most crucial factors in lending,followed by return on capital,financial flexibility,and integrity.The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults.This model could assist financial institutions,providing a simple means for identifying potential SMEs to grant credit,and advance further research using alternative approaches.

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research.Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics.In this paper,a novel feature-weighted support vector machine(SVM) credit scoring model is presented for credit risk assessment,in which an F-score is adopted for feature importance ranking.Considering the mutual interaction among modeling features,random forest is further introduced for relative feature importance measurement.These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

Credit Scoring Based on the Set-Valued Identification Method

Credit scoring is one of the key problems in financial risk managements.This paper studies the credit scoring problem based on the set-valued identification method,which is used to explain the relation between the individual attribute vectors and classification for the credit worthy and credit worthless lenders.In particular,system parameters are estimated by the set-valued identification algorithm based on a given recognition criteria.In order to illustrate the efficiency of the proposed method,practical experiments are conducted for credit card applicants of Australia and credit card holders from Taiwan,respectively.The empirical results show that the set-valued model has a higher prediction accuracy on both small and large numbers of data set compared with logistic regression model.Furthermore,parameters estimated by the set-valued identification method are more stable,which provide a meaningful and logical explanation for extracting factors that influence the borrowers’credit scorings.

A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS CREDIT SCORING MANAGEMENT

Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms （e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor）, we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines （SVMs）, classification and regression tree （CART）, and multivariate adaptive regression splines （MARS）. The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS＇s explanatory capability outperforms the other methods.

Default or profit scoring credit systems?Evidence from European and US peer-to-peer lending markets

For the emerging peer-to-peer(P2P)lending markets to survive,they need to employ credit-risk management practices such that an investor base is profitable in the long run.Traditionally,credit-risk management relies on credit scoring that predicts loans’probability of default.In this paper,we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of loans.To validate our profit scoring models with traditional credit scoring models,we use data from a European P2P lending market,Bondora,and also a random sample of loans from the Lending Club P2P lending market.We compare the out-of-sample accuracy and profitability of the credit and profit scoring models within several classes of statistical and machine learning models including the following:logistic and linear regression,lasso,ridge,elastic net,random forest,and neural networks.We found that our approach outperforms standard credit scoring models for Lending Club and Bondora loans.More specifically,as opposed to credit scoring models,returns across all loans are 24.0%(Bondora)and 15.5%(Lending Club)higher,whereas accuracy is 6.7%(Bondora)and 3.1%(Lending Club)higher for the proposed profit scoring models.Moreover,our results are not driven by manual selection as profit scoring models suggest investing in more loans.Finally,even if we consider data sampling bias,we found that the set of superior models consists almost exclusively of profit scoring models.Thus,our results contribute to the literature by suggesting a paradigm shift in modeling credit-risk in the P2P market to prefer profit as opposed to credit-risk scoring models.

Social credit:a comprehensive literature review

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

Improving Federated Learning through Abnormal Client Detection and Incentive

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

Consumer Credit Risk Management in an Emerging Market: The Case of China

With the liberalization of the financial service sector mandated by China＇s access to the WTO, China＇s credit card market has received a great deal of attention from global financial institutions. This paper examines the enormous growth opportunities and key barriers facing the development of the credit card industry in China, and discusses the importance and tools of consumer credit risk management. In the process of rapid expansion of China＇s consumer credit card industry, credit risk management should be treated as a top priority to avoid a pile up of bad debt in credit card receivables. This requires the development of an updated and comprehensive national consumer credit database and the use of credit risk modeling and scoring in predicting consumer behavior. As structured finance develops in China, the securitization of credit card receivables into asset-backed securities might also serve as an alternative to traditional credit risk management.