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Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

Kavitha Muthukumaran*, K. Hariharanath

SSN School of Management, Kalavakkam, Chennai, 603110, India

* Corresponding Author: Kavitha Muthukumaran. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 521-536.


Recently, data science techniques utilize artificial intelligence (AI) techniques who start and run small and medium-sized enterprises (SMEs) to take an influence and grow their businesses. For SMEs, owing to the inexistence of consistent data and other features, evaluating credit risks is difficult and costly. On the other hand, it becomes necessary to design efficient models for predicting business failures or financial crises of SMEs. Various data classification approaches for financial crisis prediction (FCP) have been presented for predicting the financial status of the organization by the use of past data. A major process involved in the design of FCP is the choice of required features for enhanced classifier outcomes. With this motivation, this paper focuses on the design of an optimal deep learning-based financial crisis prediction (ODL-FCP) model for SMEs. The proposed ODL-FCP technique incorporates two phases: Archimedes optimization algorithm based feature selection (AOA-FS) algorithm and optimal deep convolution neural network with long short term memory (CNN-LSTM) based data classification. The ODL-FCP technique involves a sailfish optimization (SFO) algorithm for the hyperparameter optimization of the CNN-LSTM method. The performance validation of the ODL-FCP technique takes place using a benchmark financial dataset and the outcomes are inspected in terms of various metrics. The experimental results highlighted that the proposed ODL-FCP technique has outperformed the other techniques.


Cite This Article

K. Muthukumaran and K. Hariharanath, "Deep learning enabled financial crisis prediction model for small-medium sized industries," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 521–536, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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