Bankruptcy prediction for credit risk using an auto-associative neural network in Korean firms

Empirical bankruptcy prediction models have been proposed and widely used in the last decades or so. Historic solvent and default firm data are collected and labeled appropriately. Statistical and neural network models are then "trained" to fit these data. A major problem is the imbalance of data, i.e. much more solvent data than default data. We propose an auto-associative neural network (AANN) that learns the identity mapping of input. By training the network with only solvent data, we built a bankruptcy predictor with better accuracies than conventional 2-class neural network based predictor.