Chuang and Lin presented a reassigning credit scoring model RCSM integrating ANN and case-based reasoning CBR -based classification technique for individual credit and implied that the proposed hybrid model was more accurate than other commonly used credit scoring methods and also contributed to a reduction in type I errors in the scoring system.
Chen et al. Existing studies note other interesting points concerning improvements in credit scoring methods. First, the data preparation process, such as data collection, variable selection, and data cleaning for credit scoring prediction can help reduce noise levels and further enhance the evaluation accuracy of credit risk.
However, most studies neglected to address this important process. Second, because hybrid systems have recently been introduced to social credit scoring and have demonstrated their superiority, even more powerful hybrid models can be formulated by combining various formidable AI tools.
Third, existing credit scoring studies mainly focused on country, corporate, and individual credit while neglecting network credit. Therefore, evaluating variations of country, corporate, and individual credit in the network context should be considered as important future research directions. An indicator system covering a set of evaluation indexes is another significant component of the credit scoring model that directly determines evaluation results.
Therefore, indicator systems should be designed to capture target agent features. For example, Balkan Balkan proposed a model with two political risk variables level of democracy and political instability and some economic variables and showed that the novel model including the political variables generated superior results than the model limited to economic variables.
Block and Vaaler argued that government stability is a significant index with a positive effect on government credit ratings. Yim and Mitchell classified the evaluation indexes for country risk into five categories: economics, balance of payments, external debt, government, and political risk.
Beirne and Fratzscher found that government credit was sensitive to social stability and changes in financial markets. Duffee and Zhou compared the effects of firm attributes and external factors on credit rating predictions. Min and Lee studied business bankruptcy models based on six variables: financial expenses to sales FE , the current liabilities ratio CL , total borrowings and bonds payable to total assets TB , the capital adequacy ratio CA , the current ratio CR , and the interest coverage ratio IC.
Avery et al. Bellotti and Crook evaluated individual credit risk for credit card applicants based on 11 features: home ownership, time with bank, required insurance, the number of settled non-mail order credit account information sharing CAIS accounts, total outstanding balance excluding mortgages on all active CAIS accounts, total number of credit searches in the last six months, worst account status, age, product type of credit card , time since the most delinquent account, and the UK Mosaic code.
With respect to future research, existing studies on indicator systems design mostly focused on countries or government , corporations, and individuals as economic agents while neglecting online market credit. Therefore, selecting appropriate indicators for network credit evaluation is imperative for future research.
Regulatory mechanisms supervise the credit risk of various agents to avoid further credit fraud. According to existing literature, establishing and sharing credit databases is one of the most pressing tasks currently under discussion Zhang and Smyth For example, Miller investigated public and private worldwide credit data and concluded that public credit registries were not a substitute for private sector registries but a complement.
Hunt reviewed the history of the individual credit reporting industry evolution and particularly emphasized the need for credit information sharing in the credit report industry by establishing credit standard systems and relevant rules. Smith et al. Moreover, Zhang and Smyth analyzed the emerging credit reporting system in China and argued that substantial progress should be made through public and private credit-reporting services cooperation.
With respect to future research, progress can be achieved in the regulatory mechanisms for the social credit field. First, important credit data and reports from different agents including governments, corporations, and individuals should be used in the construction of credit databases. Second, existing studies on regulatory mechanisms have mostly used qualitative analysis; therefore, some quantitative experimental models, such as multi-agent-based systems and system dynamics, should be employed to explore the optimal regulatory mechanisms for social credit rules or designs.
Third, the associated regulations concerning data protection and access to personal information deserve further investigations. Fourth, regulating network credit system has become an imperative task since the rapid development of online markets to guarantee a legitimate network environment and prevent network credit default.
For each aspect, the study presents a historical review of the theoretical or model development for all economic agents together with important works and future research directions. Social theory investigates economic explanation, creation mechanisms, and evolution mechanisms for social credit encompassing country, corporate, and individual credit for different target agents. Social theories on social credit can be classified into the traditional and emerging theories that use different research techniques.
Traditional theory incorporates various traditional economic theories into the social credit framework to explore the economic explanation or function , creation, and evolution of credit. Emerging credit theory performs analyses based on information economics and focuses on the information mechanisms in social credit systems. Credit scoring might be the most important component of the social credit research field, and the most related studies attempt to enhance the evaluation accuracy of the credit risk of different agents.
With respect to regression, various prediction techniques have been introduced into social credit risk evaluation and can be categorized as expert systems, econometric models, mathematical programming, AI tools, and their hybrid forms.
The results suggest that various hybrid approaches have been developed and have become an increasingly potential tool in evaluating credit scores. Indicator systems covering a set of evaluation indexes are another important component of the credit scoring model that directly determines evaluation results. Therefore, indicator systems should be carefully designed to capture the features of target agents.
Establishing and sharing a general credit database might be one of the most pressing tasks for which consistent data standards are currently being discussed. The existing studies on social credit can be improved.
For social theory, the studies based on experimental simulation technologies are insufficient compared to the studies based on traditional credit theory. Additionally, a comprehensive exploration of the various economic agents and their interactions is another interesting research direction. With respect to credit scoring, additional powerful hybrid models should be formulated by combining various AI tools.
For regulatory mechanisms, quantitative experimental models should be applied to explore the optimal regulatory mechanism rules or designs. The regulations concerning data protection and access to personal information deserve deep investigations. Regulating network credit systems has become an imperative task necessary to guarantee a legitimate network environment without credit fraud. Allen F, Gale D Bubbles and crises. Econ J — Article Google Scholar. J Comput Appl Math 16 — Altman EI Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.
J Financ 23 4 — Financ Markets Inst Instrum 6 2 :1— Fed Res Bull Google Scholar. Exp Syst Appl 11 4 — Manag Sci 49 3 — J Oper Res Soc 54 6 — Balkan EM Political instability, country risk and probability of default. Appl Econ 24 9 — Ecol Econ 66 4 — Pattern Recogn Lett 19 11 — Beaver, WH Financial ratios as predictors of failure. J Account Res, 4, 71— Beirne J, Fratzscher M The pricing of sovereign risk and contagion during the European sovereign debt crisis.
J Int Money Financ — Bellotti T, Crook J Support vector machines for credit scoring and discovery of significant features. Exp Syst Appl 36 2 — Exp Syst Appl 40 1 — Block SA, Vaaler PM The price of democracy: sovereign risk ratings, bond spreads and political business cycles in developing countries.
J Int Money Financ 23 6 — Brown M, Zehnder C The emergence of information sharing in credit markets. J Financ Intermed 19 2 — Chant, J. Financial stability as a policy goal.
Chatterjee S, Barcun S A nonparametric approach to credit screening. J Am Stat Assoc 65 — Exp Syst Appl 24 4 — Exp Syst Appl 36 4 — Cooper JC Artificial neural networks versus multivariate statistics: an application from economics.
J Appl Stat 26 8 — Eur J Oper Res 95 1 — Manag Financ 27 8 — J Monet Econ 48 1 — J Dev Econ 4 1 — J Financ 34 2 — J Int Econ 1 3 — Goonatilake, S, and Treleaven, PC Intelligent systems for finance and business. Grinols, E International debt rescheduling and discrimination using financial variables. Earl Grinols. Financial aspects of economic development. Am Econ Rev, 45 4 — J Financ 11 2 — Appl Econ Lett 4 11 — Eng Appl Artif Intel 26 2 — Hsieh NC Hybrid mining approach in the design of credit scoring models.
Exp Syst Appl 28 4 — Decis Support Syst 37 4 — Appl Math Comput 2 — Exp Syst Appl 33 4 — Hunt RM A century of consumer credit reporting in America. J Bank Financ 31 1 — Jarrow R, Xu L Credit rating accuracy and incentives. J Credit Risk 6 3 :1— Jo H, Han I Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction.
Ann Oper Res 1 — Lee TS, Chen IF A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Decis Support Syst 18 1 — Exp Syst Appl 23 3 — Comput Stat Data Anal 50 4 — Exp Syst Appl 30 4 — Comput Ind Eng 63 3 — Appl Soft Comput 12 8 — New evidence from the credit default swap market.
J Financ 60 5 — Magruder, C. The Position of Shareholders in Business Trusts. Columbia Law Review, 23 5 — Mangasarian OL Linear and nonlinear separation of pat-terns by linear programming. Oper Res — Decis Sci 26 2 — Marx, K, Engels, F In: World Bank. Exp Syst Appl 35 4 — In: Neural Networks, So, Mankovich and his colleagues studied those observable waves and used them to backtrack inward to the planet itself. That's how the researchers came up with the measurement of 10 hours, 33 minutes and 38 seconds.
It's still not set in stone — the error bars on that calculation stretch between a minute and 52 seconds longer and a minute and 19 seconds shorter. But the new calculation's range beats a minute window.
The research is described in a paper published yesterday Jan. Email Meghan Bartels at mbartels space. Follow us Spacedotcom and Facebook. Original article on Space. Join our Space Forums to keep talking space on the latest missions, night sky and more!
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