Sahawneh, N, Alzoubi, H M, Radwan, N eyara, Fatima, Areej, Rehman, Abdur and Khan, Shan (2022) An Intelligent Approach for Predicting Bankruptcy Empowered with Machine Learning Technique. In: 2022 International Conference on Cyber Resilience (ICCR), 06-07 October 2022, Dubai, United Arab Emirates.
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Several firms have had financial challenges in recent years as a result of the sluggish economic growth over the last decade. Financial institutions, fund managers, debtors, governments, and economic stakeholders would greatly benefit from a machine learning framework for bankruptcy forecasting. A debtor can be identified as a person or a business through the bankruptcy process. In order to minimize financial losses, it is essential to determine the risk of insolvency at the early phase. With this in mind, several soft computing methods may be utilized to assess insolvency. This study proposes a method for classifying firms according to their level of risk based on a deep extreme learning machine (DELM) that is used for bankruptcy forecasting. The method of forecasting offers a foundation for bankruptcy detection decision assistance.
Affiliation: | Skyline University College |
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SUC Author(s): | Sahawneh, N and Alzoubi, H M ORCID: https://orcid.org/0000-0003-3178-4007 |
All Author(s): | Sahawneh, N, Alzoubi, H M, Radwan, N eyara, Fatima, Areej, Rehman, Abdur and Khan, Shan |
Item Type: | Conference or Workshop Item (Paper) |
Uncontrolled Keywords: | Economics, Extreme learning machines, Government, Machine learning, Stakeholders, Forecasting, Bankruptcy |
Subjects: | A Business and Management > AE Economics B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 26 Jan 2024 15:02 |
Last Modified: | 26 Jan 2024 15:02 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/767 |
Publisher URL: | https://doi.org/10.1109/ICCR56254.2022.9995890 |
Publisher OA policy: | |
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