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

Abstract

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.

Optimized Deep Learning Model to Predict Business Bankruptcy

“…For example: stock market information, particularities of the sector in which the activity is carried out, regulation, ease of access to credit, business bankruptcy, etc. In the same way, Muthukumaran and Hariharanath [7] state that deep learning is particularly effective in applications involving Small and Medium-sized Enterprises (SMEs), thanks to its versatility and capability to facilitate numerous training iterations. The main objective of the technique is to determine the financial status of SMEs, which contains the design of the selection of characteristics based on the optimization algorithm and neural network, which is used for data classification.…”

“…They also consider that the rating of financial performance originates the profitability of a company and its continuity in the long term. Muthukumaran and Hariharanath [7] observe that both established companies and new enterprises (some only months old) encounter a highly competitive, complex, ambiguous, volatile, and uncertain market. This challenging environment extends beyond local or national boundaries, directly connecting to the international market through products, services, and pricing.…”

Stock Price Volatility Prediction Based on GARCH and Related Models

“…In Muthukumaran's paper, the author analyzes the data of small and medium-sized enterprises to build a model, selects appropriate data classification methods to create appropriate data sets for data pre-processing, and then uses a variety of different deep learning technologies to analyze and predict the data, and carries out phased analysis and comparison of the proposed new technologies. And finally, develop a more suitable and excellent technology [27]. The importance of deep learning in risk management is mentioned in Guiling's paper [28].…”

Hybrid Hunter–Prey Optimization with Deep Learning-Based Fintech for Predicting Financial Crises in the Economy and Society

“…Then, LSTM with RNN is applied for the classification of collected financial information. Muthukumaran and Hariharanath [12] consider the design of the optimum DL-based FCP (ODL-FCP) method for SMEs. The presented method integrates two stages: optimal deep CNN with LSTM (DCNN-LSTM)-based data classification and Archimedes optimization algorithm-based FS (AOA-FS) procedure.…”