Journal Article10.1016/J.EJOR.2012.04.009
An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data
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TL;DR: This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro FBuzzy, and demonstrates that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network approaches.
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About: This article is published in European Journal of Operational Research. The article was published on 01 Oct 2012. The article focuses on the topics: Credit card & Adaptive neuro fuzzy inference system.
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Citations
Benchmarking state-of-the-art classification algorithms for credit scoring
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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
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Genetic algorithm-based heuristic for feature selection in credit risk assessment
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TL;DR: Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the H GA-NNclassifier is a promising addition to existing data mining techniques.
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Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
TL;DR: In this article , a penalised logistic tree regression (PLTR) model is proposed to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model.
Classification methods applied to credit scoring: Systematic review and overall comparison
TL;DR: A systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis shows the use and importance of the main techniques forcredit rating, as well as some of the scientific paradigm changes throughout the years.
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References
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Erkki K. Laitinen,Teija Laitinen +1 more
TL;DR: In this paper, the authors used Taylor's series expansion to solve the problem associated with the functional form of bankruptcy prediction models and applied the logistic regression model to the data from the Compustat database.
A data driven ensemble classifier for credit scoring analysis
Nan-Chen Hsieh,Lun-Ping Hung +1 more
TL;DR: This study focuses on predicting whether a credit applicant can be categorized as good, bad or borderline from information initially supplied, and introduces the concept of class-wise classification as a preprocessing step in order to obtain an efficient ensemble classifier.
Building credit scoring models using genetic programming
TL;DR: In this paper, genetic programming (GP) is used to build credit scoring models and it is concluded that GP can provide better performance than other models.
A neural network for classifying the financial health of a firm
TL;DR: This research is the first to use Cascade-Correlation for corporate health estimation, and it solves the hidden architecture enigma encountered using other types of neural networks.
An integrated data mining and behavioral scoring model for analyzing bank customers
TL;DR: It is demonstrated that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development.