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Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Electronics (Basel), 2024-04, Vol.13 (8), p.1596 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics13081596

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  • Title:
    Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
  • Author: Kuizinienė, Dovilė ; Krilavičius, Tomas
  • Subjects: Algorithms ; Annual reports ; Artificial intelligence ; Balancing ; Bankruptcy ; class-balancing techniques ; Classification ; Decision making ; Employment ; Feature selection ; Financial analysis ; financial distress ; Financial reporting ; Financial statements ; imbalance ; Machine learning ; Methods ; Ratios ; Researchers ; sampling ; Securities markets ; Small & medium sized enterprises-SME
  • Is Part Of: Electronics (Basel), 2024-04, Vol.13 (8), p.1596
  • Description: Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics13081596
  • Source: Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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