Neural networks in business: a survey of applications (1992–1998)

Vellido, Alfredo

doi:10.1016/s0957-4174(99)00016-0

Cited by 376 publications

(170 citation statements)

References 105 publications

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“…An advantage of discriminant analysis is that ordinary least square estimation procedure can be implemented to estimate the coeffi cients of the linear discriminant function, but maximum likelihood methods are required for the estimation of logistic regression models. Logistic regression models have been widely adopted in many areas ranging from business to engineering (Laitinen, E. K. and Laitinen, T. 2000;Suh et al 1999;Vellido et al 1999). Logistic regression has also been explored by several in building credit scoring models for personal loan, business loan, and credit card applications (Lee et al 2006).…”

Section: Methodsmentioning

confidence: 99%

“…Neural networks and decision trees have been widely used to solve several problems related to engineering, science, business, forecasting fi elds (Vellido et al 1999;Lee et al 2002;Lee et al 2006). Neural networks and decision trees have been used to deal with credit scoring problems (Lee et al 2006;Lee et al 2002).…”

Section: Artifi Cial Intelligence Techniquesmentioning

confidence: 99%

“…According to Vellido et al (1999), more than 75% of business applications using neural networks adopted the BPN algorithm. Based on these facts, we will use the BPN algorithm for credit scoring model.…”

Section: Neural Networkmentioning

confidence: 99%

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A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

İnce

Aktan

2009

Journal of Business Economics and Management

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Abstract. Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan offi cers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artifi cial intelligence approaches: discriminant analysis, logistic regression, neural networks and classifi cation and regression trees. Experimental studies using real world data sets have demonstrated that the classifi cation and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

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Section: Methodsmentioning

confidence: 99%

Section: Artifi Cial Intelligence Techniquesmentioning

confidence: 99%

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A Comparison of Data Mining Techniques for Credit Scoring in Banking: A Managerial Perspective

İnce

Aktan

2009

Journal of Business Economics and Management

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“…Other problems are the enormous number of 'parameters' to be adjusted to fix the data and the resulting excessive training time for the model optimisation. Furthermore, because of the lack of a systematic approach for setting up the neural network structure, learning algorithm and parameters, the task of training such a network is still mostly based on trial and error (Vellido et al, 1999).…”

Section: The Neural Network Architecturesmentioning

confidence: 99%

“…Among the different neural network structures, one of the most widely used for practical business and financial applications is the MLP (Vellido et al, 1999). At the same time, it is usually described as the most classic network architecture while, however, having still proven to be a very good forecasting method compared with other more complex neural networks.…”

Section: Mlpmentioning

confidence: 99%

Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007–2009?

Dunis¹,

Laws²,

Schilling³

2012

J Deriv Hedge Funds

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The motivation for this article is to check whether neural network models have remained a superior method for forecasting the EUR/USD exchange rate during the financial crisis of [2007][2008][2009]. Alternative neural network architectures (Multi-Layer Perceptron (MLP), Recurrent Neural Network and Higher Order Neural Network (HONN)) are benchmarked against a random walk and a traditional ARMA model, and evaluated in terms of statistical accuracy and through a trading simulation on daily data over the period from January 2000 to February 2009, the period from August 2007 to February 2009, providing the out-of-sample testing period. Transaction costs and a confirmation filter devised to reduce false signals and thus also reduce losses and transaction costs were also taken into consideration. It is shown that the HONN structure gives the overall best results on a simple trading simulation; however, for an advanced trading simulation with a confirmation filter, the MLP outperforms all other models on most performance measures. On the whole, the results show that neural networks are still able to produce forecasts that yield a positive return and are superior to those of linear and more traditional models, with respect to both trading performance and statistical accuracy, even under very volatile market conditions.

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