Throughout the years, many researches have been conducted on the potential applications of Artificial Intelligence (AI) in bankruptcy detection. This project will
provide an overview regarding the feasibility of the application of neural networks for direct classification of early bankruptcy detection based on financial ratio. A brief introduction to neural networks and the suitability algorithm of neural network for use in early bankruptcy detection determination will be investigated. In this project, several algorithm from Multilayer Perceptron network (MLP) will be developed and their performance are compared to yield the most suitable algorithm that will be used to model the classification system for determination early bankruptcy detection based on financial ratio. Among the types of algorithm that will be developed are Basic Gradient Descent (GD) algorithm, Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm, Scaled Conjugate Gradient (SCG) algorithm, Resilient Propagation (RP) algorithm and some more at Multilayer Perceptron network (MLP). This study proves that the Levenberg-Marquardt (LM) algorithm achieves the best performance as compared to the other algorithm. The Levenberg-Marquardt (LM) algorithm produces 84.85% accuracy. In this study, an intelligent system is developed for the classification of early bankruptcy detection using Levenberg-Marquardt (LM) algorithm. The proposed system provides several advantages in terms of its applicability, high accuracy, user-friendliness and as well as yields faster results compared to conventional system.
EARLY BANKRUPTCY DETECTION USING NEURAL NETWORK_Soh Rong Shu_E3_2007_NI