This study addresses the problem of corporate bankruptcy prediction. Previous research primarily employed statistical techniques such as Multiple Discriminant Analysis (MDA), logit analysis, and probit analysis to solve the bankruptcy prediction problem, but these approaches had methodological limitations requiring strict statistical assumptions to be satisfied. Accordingly, as artificial intelligence techniques have been more actively applied to various business problems in recent years, studies applying AI-related techniques such as inductive learning and artificial neural networks to the corporate bankruptcy prediction problem have been actively introduced, and their performance has been demonstrated to be superior to conventional statistical techniques. However, since these studies predominantly employed research methodologies that simply compared predictive performance with existing statistical techniques, the need for more novel methodologies emerged. From this perspective, this study goes beyond merely comparing with conventional statistical techniques and introduces the Inductive Learning Assisted Neural Network (ILANN) technique, which combines inductive learning methods with artificial neural network models. This approach adopts a method of combining the performance of inductive learning and neural network models by analyzing Type I Error and Type II Error. The analysis was based on financial ratio data from 166 domestic bankrupt and non-bankrupt firms from 1979 to 1992. A total of 57 financial ratios were used, consisting of 11 growth-related ratios, 12 profitability-related ratios, 18 stability-related ratios, 4 cash flow-related ratios, 6 activity-related ratios, and 6 corporate credit-related ratios. The experimental results demonstrated that the ILANN model proposed in this study exhibited the highest predictive accuracy compared to MDA, inductive learning methods, and neural network models alone.