Two-stage genetic programming (2SGP) for the credit scoring model

Huang, Jih-Jeng; Tzeng, Gwo‐Hshiung; Ong, Chorng‐Shyong

doi:10.1016/j.amc.2005.05.027

Cited by 128 publications

(76 citation statements)

References 28 publications

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“…For example, Ong et al 2005 used genetic programming (GP) for individual credit and concluded that GP is superior to benchmarks such as neural networks, decision trees, rough sets, and logistic regression. Huang et al 2006 proposed two-stage genetic programming (2SGP) to manage individual credit scoring problems by incorporating the advantages of if-then rules and the discriminant function. The author found that 2SGP provides superior accuracy compared to other models.…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

“…Particularly, when investing in government debt or loans, country (or government) credit should be considered a primary risk factor, namely, country risk represents the debt paying capability of the government. Similarly, when trading in commodity markets and investing in financial markets, the ability of the target corporations or individuals to meet debt obligations should be carefully estimated by credit scoring or rating using information about history records, current economic state, and other attributes (Huang et al 2004;Huang et al 2006). For example, commercial banks make financial loan decisions and issue credit cards to customers dependent on corporate (or individual) credit ratings or credit scores.…”

Section: Introductionmentioning

confidence: 99%

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Social credit: a comprehensive literature review

Tang

et al. 2015

Financial Innovation

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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.

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Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social credit: a comprehensive literature review

Tang

et al. 2015

Financial Innovation

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“…For instance, GP is more flexible in symbolic setting than conventional regression method or data-mining approach (e.g., ANN). Notably, GP is also widely utilized in practical applications such as in forecasting [10][11][12]42] and classification [8,43].…”

Section: Genetic Programmingmentioning

confidence: 99%

“…When selecting input variables, GP automatically finds variables that contribute most to the model [11,42] and does not have any restriction for data size, as compared to an ANN or large data-set [8,43]. …”

Section: Genetic Programmingmentioning

confidence: 99%

Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

Lee

Tong

2012

Energies

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Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV) system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST), data envelopment analysis (DEA), and genetic programming (GP). Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.

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“…A neural network is a system made of highly interconnected and interacting processing units that are based on neurobiological models mimicking the way the nervous system works. It usually consists of a three layered system comprising input, hidden, and output layers [1,4,5,33] . A Cascade Correlation Neural Network (CCNN) is a special type of neural network used for classification purposes.…”

Section: Related Studiesmentioning

confidence: 99%

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

et al. 2016

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a b s t r a c tThe preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L'Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models' performances, we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower's account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

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Two-stage genetic programming (2SGP) for the credit scoring model

Cited by 128 publications

References 28 publications

Social credit: a comprehensive literature review

Social credit: a comprehensive literature review

Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

Predicting Creditworthiness in Retail Banking with Limited Scoring Data

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