Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Peralungal, Alay; Natchimuthu, Natchimuthu Title A name given to the resource Predicting Financial Distress in India: A Deep Learning Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.587-603 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7717-4_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_42</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000439167?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000439167?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Peralungal A., School of Commerce, Finance and Accountancy, CHRIST (Deemed To Be University), Karnataka, Bangalore, 560029, India; Natchimuthu N., School of Commerce, Finance and Accountancy, CHRIST (Deemed To Be University), Karnataka, Bangalore, 560029, India Description An account of the resource The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Convolutional neural network; Deep learning; Financial distress prediction; Long short-term memory; Recurrent neural network Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981977716-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online