Article2011

ARTIFICIAL METAPLASTICITY NEURAL NETWORK APPLIED TO CREDIT SCORING

Alexis Marcano-Cedeño · Amparo Marin de la Barcena · Juan Jiménez-Trillo · Juan Antonio Piñuela-Izquierdo · Diego Andina

52 citations0/0 files storedInternational Journal of Neural Systems

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Imbalanced Data Classification Techniques

Abstract

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

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References

2000Neural network credit scoring models 2006Credit scoring with a data mining approach based on support vector machines 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2007Using neural network ensembles for bankruptcy prediction and credit scoring 2005Building credit scoring models using genetic programming 2002Credit scoring using the hybrid neural discriminant technique 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring 2008Feature selection in bankruptcy prediction

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2020Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination 2011Predicting Business Failure Using an RSF‐based Case‐Based Reasoning Ensemble Forecasting Method 2012Forecasting business failure using two‐stage ensemble of multivariate discriminant analysis and logistic regression 2016Credit scoring model: A combination of genetic programming and deep learning 2017A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers

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