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Introduction

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Bankruptcy Prediction through Soft Computing based Deep Learning Technique

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Abstract

Bankruptcy prediction [1, 2, 3] can be defined as the process where bankruptcy is projected along with several financial distress measures in corporate institutions and public firms. It is an active research area in business and mathematical finance. The importance of bankruptcy is mainly attributed to the creditors and investors in assessing the likelihood that an organization can become bankrupt. Bankruptcy investigation is generally expressed as function of the data availability. For public organizations that were either bankrupt or not, there are a large number of accounting ratios [4] that indicate danger which are calculated and other relevant explanatory variables [3] which currently exist. As a result of this, the topic of bankruptcy is well versed toward testing the complex and data-intensive prediction techniques [5].

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Authors and Affiliations

  1. Samsung R & D Institute Delhi, Noida, Uttar Pradesh, India

    Arindam Chaudhuri

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Soumya K. Ghosh

Authors
  1. Arindam Chaudhuri
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  2. Soumya K. Ghosh
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Chaudhuri, A., Ghosh, S.K. (2017). Introduction. In: Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Springer, Singapore. https://doi.org/10.1007/978-981-10-6683-2_1

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  • DOI: https://doi.org/10.1007/978-981-10-6683-2_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6682-5

  • Online ISBN: 978-981-10-6683-2

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