Artificial neural networks in business: Two decades of research

• We introduce a literature review considering articles on artificial neural networks in business published in last two decades.
• 412 suitable articles are identified and classified according to defined methodology.
• We focus on date, area of interest, type of neural network, benchmark method, journal and citations.
• The most applied are multilayer feedforward networks with backpropagation learning performed by gradient descent algorithm.
• Majority (25.73%) of the examined articles were published in Expert Systems with Applications.

In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as well-known method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.

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