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

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Financial Innovation 2022年第1期8卷 1274-1292页

作者： Hyunwoo Woo So Young Sohn Department of Industrial Engineering Yonsei University134 Shinchon-dongSeoulRepublic of Korea

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 *** study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’job *** projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator(MBTI)that fits each job *** 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 *** found that each MBTI type’s credit scoring model has different significant *** study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

关键词： Alternative credit scoring Behavioral finance credit scoring Locally weighted logistic regression MBTI P2P lending

A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS credit scoring MANAGEMENT

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Journal of Systems Science and Systems Engineering 2006年第4期15卷 419-435页

作者： Wenbing XIAO Qian ZHAO Qi FEI Institute of Systems Engineering Huazhong University of Science & Technology Wuhan 430074 China School of Economics Renmin University of China Beijing 100872 China

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.

关键词： Data mining credit scoring classification and regression tree support vector machines,multivariate adaptive regression splines credit-risk evaluation

Leveraging Geospatial Technology for Smallholder Farmer credit scoring

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Journal of Geographic Information System 2023年第5期15卷 524-539页

作者： Susan A. Okeyo Galcano C. Mulaku Collins M. Mwange Department of Geospatial and Space Technology University of Nairobi Nairobi Kenya

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 cred

关键词： credit scoring Machine Learning Geospatial Technology Migori

Feature selection based on SVM for credit scoring

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Feature selection based on SVM for credit scoring

A Hybrid Support Vector Machine Model for credit scoring

2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Ping Yao School of Economics & Management Heilongjiang Institute of Science and Technology Harbin 150027 China

As the credit industry has been growing rapidly,huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually,a large amount of redundant information and features are involved in the credit dataset,which leads to lower accuracy and higher complexity of the credit scoring model,so,effective feature selection methods are necessary for credit dataset with huge number of features. This paper aims at comparing seven well-known feature selection methods for credit scoring. Which are t-test,principle component analysis （PCA）,factor analysis （FA）,stepwise regression,Rough Set （RS）,Classification and regression tree （CART） and Multivariate adaptive regression splines （MARS）. Support vector machine （SVM） is used as the classification model. Two credit scoring databases are used in order to provide a reliable conclusion. Regarding the experimental results,the CART and MARS methods outperform the other methods by the overall accuracy and type I error and type II error.

关键词： credit scoring feature selection support vector machine t-test principle component analysis factor analysis stepwise regression rough set classification and regression tree multivariate adaptive regression splines

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A Hybrid Support Vector Machine Model for Credit Scoring

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

2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Ahmad Ghodselahi Department of Information Technology Management Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781612848334

credit risk is the most challenging risk to which financial institution are *** scoring is the main analytical technique for credit risk *** this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting *** hybrid model combines clustering and classification *** Support Vector Machine（SVM） classifiers are utilized as the members of ensemble *** even a small improvement in credit scoring accuracy causes significant loss reduction,then the application of ensemble in hybrid model leads to better performance of classification.A real dataset is used to test the model *** test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods.

关键词： component Ensemble SVM credit scoring Hybrid model

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Financial Innovation 2022年第1期8卷 954-974页

作者： Štefan Lyócsa Petra Vašaničová Branka Hadji Misheva Marko Dávid Vateha Faculty of Management and Business University of PrešovPrešovSlovak Republic Institute of Economic Research Slovak Academy of SciencesBratislavaSlovak Republic ZHAW School of Engineering WinterthurSwitzerland Depart-ment of Banking and International Finance Faculty of National EconomyUniversity of Economics in BratislavaBratislavaSlovak Republic Faculty of Economics and Administration Masaryk UniversityLipová507/41a60200 BrnoCzech Republic

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 ***,credit-risk management relies on credit scoring that predicts loans’probability of *** this paper,we use a profit scoring approach that is based on modeling the annualized adjusted internal rate of returns of *** 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 *** 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 *** found that our approach outperforms standard credit scoring models for Lending Club and Bondora *** 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 ***,our results are not driven by manual selection as profit scoring models suggest investing in more ***,even if we consider data sampling bias,we found that the set of superior models consists almost exclusively of profit scoring ***,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.

关键词： Profit scoring credit scoring Financial intermediation P2P Fintech

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

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China & World Economy 2006年第3期14卷 86-94页

作者： Xiaoqing Eleanor Xu Jiong Liu Associate Professor Stillman School of Business Seton Hall University South Orange N J USA Vice President Consumer Credit Risk Management Citibank N.A. Long Island City NY USA

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.

关键词： China consumer credit credit scoring risk management

Social credit:a comprehensive literature review

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Financial Innovation 2015年第1期1卷 70-87页

作者： Lean Yu Xinxie Li Ling Tang Zongyi Zhang Gang Kou School of Economics and Management Beijing University of Chemical TechnologyBeijing 100029China School of Business Administration Southwestern University of Finance and EconomicsChengdu 611130China

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 *** paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory *** 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 *** 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 *** 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.

关键词： Social credit Literature review credit scoring Regulatory mechanism credit risk

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基于半监督学习的客户信用评估集成模型研究