Neural network credit scoring models

By David West. Computers & Operations Research, 2000

1,025 citations recorded by the source corpus.

Subfields: Accounting, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/7fc5bf2e6d63aeb6d10159d411752b5b67e573a5

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Citations in the corpus

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, methodology): “West (2000) noted that Dr Hofmann, who compiled his German credit data, reported that the ratio of misclassification costs, associated with Type II and Type I, is 5:1, which has been used by Abdou (2009b) as well.”
Using neural network ensembles for bankruptcy prediction and credit scoring (2008, background): “The benefits of using credit scoring include reducing the cost of credit analysis, enabling faster decision, insuring credit collections, and diminishing possible risk (West, 2000).”
Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (2003, neutral): “entropy [69, 212, 444, 684, 750, 784, 778, 780, 1069, 1162, 1281, 1357, 1377, 1070, 1707, 1847, 1862, 1927, 2005, 2040, 2067] entropy maximization [1707, 2005] environmental [194, 209, 494, 523, 756, 805, 929, 1279, 1287, 1304, 1384, 1629, 1764, 1765, 1873, 1912, 1913, 2052] epileptic [723, 724, 725”
Neural networks in business: techniques and applications for the operations researcher (2000, background): “These days, however, neural networks are the underlying technique driving the decision making [65,66].”
Credit scoring using the hybrid neural discriminant technique (2002, background): “Even though neural networks have shown to have better credit scoring capability than LDA and logistic regression (Desai, Conway, & Overstreet, 1997; Desai, Crook, & Overstreet, 1996; Jensen, 1992; Piramuthu, 1999; West, 2000).”
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “NN typically consists of three or more layers: an input layer containing the input nodes, at least one hidden layer containing the weighted nodes, and an output layer that makes decisions on the input instances (West, 2000).”
A deep learning approach for credit scoring using credit default swaps (2017, background): “The credit scoring accuracy of five neural network models for both the German and Australian credit data sets is investigated by West (2000).”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “Another interesting work is done by West [29] who studies the accuracy of credit scoring of five NN models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization and fuzzy adaptive resonance.”
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (2005, methodology): “In response, the statistical methods, non-parametric methods, and artificial intelligence approaches have been proposed to support the credit approval decision process (Desai, Crook, & Overstreet, 1996; Lee, Chiu, Lu, & Chen, 2002; Thomas, 2000; West, 2000).”
Artificial neural networks in business: Two decades of research (2016, neutral): “Among the noteworthy alternative approaches, adaptive resonance theory have been used in customer analysis [173, 343], generalized regression network estimated stock prices and focused on capital structure and earnings management [2, 3, 149], probabilistic network forecasted financial distress and m”
CARTMAP: a neural network method for automated feature selection in financial time series forecasting (2012, methodology): “Multiple linear regression is a basic form of a commonly used decision-making tool used in financial analysis [3, 5, 11, 46, 49, 55].”
Building credit scoring models using genetic programming (2005, background): “The advantages of using credit scoring models can be described as the benefit from reducing the cost of credit analysis, enabling faster credit decision, insuring credit collections, and diminishing possible risk (Lee, Chiu, Lu, & Chen, 2002; West, 2000).”
Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach (2019, background): “Scholars point out that the correct discrimination of the ‘positive samples’ can help FIs cluster credit risk customers and noncredit risk customers (Bekhet and Eletter, 2014, Kürüm et al., 2012, West, 2000, Yap et al., 2011).”
Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach (2015, methodology): “Credit scoring systems have the ability to process a large volume of credit applications in less time, with minimal labor and reduced cost [15,19,20] .”