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.

Risk Management improvement and credit risk evaluation are turning core areas of concern within the financial and banking industries. Specifically credit scoring, as one of the key analytical techniques in credit risk evaluation is envisioned as an arena in which the application of Artificial Intelligence (IA) and Neural systems has high potential for development. This paper contributes by presenting a novel Neural based approach to enhance credit scoring modeling inspired by the biological metaplasticity property of neurons.