High-Performance Bankruptcy Prediction Model using Graphics Processing Units



In recent years the the potential and programmability of Graphics Processing Units (GPU) has raised a noteworthy interest in the research community for applications that demand high-computational power. In particular, in financial applications containing thousands of high-dimensional samples, machine learning techniques such as neural networks are often used. One of their main limitations is that the learning phase can be extremely consuming due to the long training times required which constitute a hard bottleneck for their use in practice. Thus their implementation in graphics hardware is highly desirable as a way to speed up the training process. In this paper we present a bankruptcy prediction model based on the parallel implementation of the Multiple BackPropagation (MBP) algorithm which is tested on a real data set of French companies (healthy and bankrupt). Results by running the MBP algorithm in a sequential processing CPU version and in a parallel GPU implementation show reduced computational costs with respect to the latter while yielding very competitive performance.


GPU Computing, Neural networks


IEEE World Congress on Computational Intelligence (WCCI 2010), July 2010


Cited by

Year 2014 : 2 citations

 Gaspar-Cunha, A., Recio, G., Costa, L., & Estébanez, C. (2014). Self-adaptive MOEA feature selection for classification of bankruptcy prediction data. The Scientific World Journal, 2014.

 Kumar, R., & Cheema, A. K. (2014). GPU Implementation of a Deep Learning Network for Financial Prediction. The International Journal of Science and Technoledge, 2(5), 374.

Year 2013 : 2 citations

 Antonio Gaspar-Cunha, Gustavo Recio, L. Costa, and Cesar Estebanez, Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data, The Scientific World Journal, 2013

 Langdon, W. B. (2013). Large-Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units. In Massively Parallel Evolutionary Computation on GPGPUs (pp. 311-347). Springer Berlin Heidelberg.

Year 2012 : 2 citations

 Sabine McConnell, Robert Sturgeon, Gregory Henry, Andrew Mayne and Richard Hurley, Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA, Journal of Physics: Conference Series, vol. 341, 2012.

 Li Weiming . ( 2012 ) . In the hierarchical growth patterns from network mapping and trajectory analysis Construction of Enterprise Financial Crisis Prediction Model .

Year 2011 : 1 citations

 W. B. Langdon, "Graphics Processing Units and Genetic Programming: An overview", Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 15, no. 8, pp. 1657-1669, 2011

Year 2010 : 1 citations

 William B. Langdon, Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units, Parallel and Distributed Computational Intelligence Studies in Computational Intelligence, vol. 269, pp 113-141, 2010