A neural network model for credit risk evaluation
Intelligent Systems Research Group, Near East University, Lefkosa, Mersin 10, Turkey. International Journal of Neural Systems
(Impact Factor: 6.51).
09/2009; 19(4):285-94. DOI: 10.1142/S0129065709002014
Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.
Available from: Gang Kou
- "The main objective of credit risk analysis is to classify samples into good and bad groups  . Many classification algorithms have been applied to credit risk analysis, such as decision tree, K-nearest neighbor, support vector machine (SVM), and neural network       . How to select the best classification algorithm for a given dataset is an important task in credit risk prediction   . "
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ABSTRACT: This paper proposes an analytic hierarchy model (AHM) to evaluate classification algorithms for credit risk analysis. The proposed AHM consists of three stages: data mining stage, multicriteria decision making stage, and secondary mining stage. For verification, 2 public-domain credit datasets, 10 classification algorithms, and 10 performance criteria are used to test the proposed AHM in the experimental study. The results demonstrate that the proposed AHM is an efficient tool to select classification algorithms in credit risk analysis, especially when different evaluation algorithms generate conflicting results.
Available from: Juan Manuel Corchado Rodríguez
- "In the present financial context, it is relevant to provide innovative tools and decision support systems that can help the small-medium enterprises (SMEs) to improve their functioning (Khashman, 2009; Sun & Li, 2009a, 2009b). These tools and methods can contribute to improve the existing business control mechanisms , reducing the risk by predicting undesirable situations and providing recommendations based on previous experiences (Chi-Jie, Tian-Shyug, & Chih-Chou, 2009; Li & Sun, 2008; Sun & Li, 2008a, 2008b). "
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ABSTRACT: Business Intelligence has gained relevance during the last years to improve business decision making. However, there is still a growing need of developing innovative tools that can help small to medium sized enterprises to predict risky situations and manage inefficient activities. This article present a multi-agent system especially created to detect risky situations and provide recommendations to the internal auditors of SMEs. The core of the multi-agent system is a type of agent with advanced capacities for reasoning to make predictions based on previous experiences. This agent type is used to implement a evaluator agent specialized in detect risky situations and an advisor agent aimed at providing decision support facilities. Both agents incorporate innovative techniques in the stages of the CBR system. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.
Available from: Lean Yu
- "These techniques can be roughly categorized into the following four groups (Yu, Wang, Lai, & Zhou, 2008). (1) Statistics: discriminant analysis (Altman, 1968; Fisher, 1936), logistic regression (Steenackers & Goovaerts, 1989; Wiginton, 1980), probit regression (Grablowsky & Talley, 1981), k-nearest neighbour (Henley & Hand, 1996, 1997), and decision tree (Carter & Catlett, 1987; Coffman, 1986; Makowski, 1985); (2) Operations Research (OR): linear programming (Hand, 1981) and integer programming (Kolesar & Showers, 1985; Showers & Chankrin, 1981); (3) Artificial Intelligence (AI): neural networks (Baesens, Setiono, Christophe, & Vanthienen, 2003; Desai, Crook, & Overstreet, 1996; Khashman, 2009; Malhotra & Malhotra, 2003; West, 2000; Yobas, Crook, & Ross, 2000), support vector machines (Baesens, van Gestel, et al., 2003; Schebesch & Stecking, 2005; van Gestel, Baesens, Garcia, & van Dijcke, 2003; Yu, Wang, & Cao, 2009; Zhou, Lai, & Yu, 2009), genetic algorithm (Chen & Huang, 2003; Ong, Huang, & Tzeng, 2005; Varetto, 1998), rough set (Beynon & Peel, 2001) and case-based reasoning (Li & Sun, 2008, 2009a, 2009b, 2010; Li, Sun, & Sun, 2009); (4) Hybrid, combined and ensemble (HCE) approaches: fuzzy system and artificial neural network (Malhotra & Malhotra, 2002; Piramuthu, 1999), rough set and artificial neural network (Ahn, Cho, & Kim, 2000), rough set and support vector machine (Yu, Wang, Wen, Lai, & He, 2008), fuzzy system and support vector machines (Wang, Wang, & Lai, 2005), casebased reasoning and support vector machines (Li & Sun, 2009), neural network ensemble (Yu, Wang, & Lai, 2008), support vector machine ensemble (Yu, Yue, Wang, & Lai, 2010; Zhou, Lai, & Yu, 2010) and group decision making approach (Sun & Li, 2009; Yu, Wang, & Lai 2010). "
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ABSTRACT: Support vector machines (SVM) is proved to be one of the most effective tool in credit risk evaluation. However, the performance of SVM is sensitive not only to the algorithm for solving the quadratic programming but also to the parameters setting in its learning machines as well as to the importance of different classes. In order to solve these issues, this paper proposes a weighted least squares support vector machine (LSSVM) classifier with design of experiment (DOE) for parameter selection for credit risk evaluation. In this approach, least squares algorithm is used to solve the quadratic programming, the DOE is used for parameter selection in SVM modelling and weights in LSSVM are used to emphasize the importance of difference classes. For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier. The results show that the proposed weighted LSSVM classifier with DOE can produce the promising classification results in credit risk evaluation, relative to other classifiers listed in this study.
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