Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine le...

Full description

Saved in:
Bibliographic Details
Published inComputers, materials & continua Vol. 72; no. 2; pp. 2429 - 2444
Main Authors Vaiyapuri, Thavavel, Priyadarshini, K., Hemlathadhevi, A., Dhamodaran, M., Kumar Dutta, Ashit, V. Pustokhina, Irina, A. Pustokhin, Denis
Format Journal Article
LanguageEnglish
Published Henderson Tech Science Press 2022
Subjects
Algorithms
Artificial intelligence
Decision making
Deep learning
Design optimization
Economic crisis
Feature selection
Financial management
International finance
Machine learning
Optimization algorithms
Risk assessment
Risk management
Online AccessGet full text

Cover

More Information
Summary:Due to global financial crisis, risk management has received significant attention to avoid loss and maximize profit in any business. Since the financial crisis prediction (FCP) process is mainly based on data driven decision making and intelligent models, artificial intelligence (AI) and machine learning (ML) models are widely utilized. This article introduces an intelligent feature selection with deep learning based financial risk assessment model (IFSDL-FRA). The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise. In addition, the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection (WSOA-FS) manner to an optimum selection of feature subsets. Moreover, Deep Random Vector Functional Link network (DRVFLN) classification technique was applied to properly allot the class labels to the financial data. Furthermore, improved fruit fly optimization algorithm (IFFOA) based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model. For enhancing the better performance of the IFSDL-FRA technique, an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2022.026204