Systematic Review of Financial Distress Identification using Artificial Intelligence Methods
The study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study disc...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2138124 |
| _version_ | 1797641070421475328 |
|---|---|
| author | Dovilė Kuizinienė Tomas Krilavičius Robertas Damaševičius Rytis Maskeliūnas |
| author_facet | Dovilė Kuizinienė Tomas Krilavičius Robertas Damaševičius Rytis Maskeliūnas |
| author_sort | Dovilė Kuizinienė |
| collection | DOAJ |
| description | The study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study discusses bankruptcy-related financial datasets, data imbalance, feature dimensionality reduction in financial datasets, financial distress prediction, data pre-processing issues, non-financial indicators, frequently used machine-learning methods, performance evolution metrics, and other related issues of machine-learning-based workflows. The study findings revealed the necessity of data balancing, dimensionality reduction techniques in data preprocessing, and allow researchers to identify new research directions that have not been analyzed yet. |
| first_indexed | 2024-03-11T13:40:14Z |
| format | Article |
| id | doaj.art-4dc77de22d184f0ab0072475db979fe9 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:14Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-4dc77de22d184f0ab0072475db979fe92023-11-02T13:36:39ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.21381242138124Systematic Review of Financial Distress Identification using Artificial Intelligence MethodsDovilė Kuizinienė0Tomas Krilavičius1Robertas Damaševičius2Rytis Maskeliūnas3Vytautas Magnus UniversityVytautas Magnus UniversityVytautas Magnus UniversityVytautas Magnus UniversityThe study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study discusses bankruptcy-related financial datasets, data imbalance, feature dimensionality reduction in financial datasets, financial distress prediction, data pre-processing issues, non-financial indicators, frequently used machine-learning methods, performance evolution metrics, and other related issues of machine-learning-based workflows. The study findings revealed the necessity of data balancing, dimensionality reduction techniques in data preprocessing, and allow researchers to identify new research directions that have not been analyzed yet.http://dx.doi.org/10.1080/08839514.2022.2138124 |
| spellingShingle | Dovilė Kuizinienė Tomas Krilavičius Robertas Damaševičius Rytis Maskeliūnas Systematic Review of Financial Distress Identification using Artificial Intelligence Methods Applied Artificial Intelligence |
| title | Systematic Review of Financial Distress Identification using Artificial Intelligence Methods |
| title_full | Systematic Review of Financial Distress Identification using Artificial Intelligence Methods |
| title_fullStr | Systematic Review of Financial Distress Identification using Artificial Intelligence Methods |
| title_full_unstemmed | Systematic Review of Financial Distress Identification using Artificial Intelligence Methods |
| title_short | Systematic Review of Financial Distress Identification using Artificial Intelligence Methods |
| title_sort | systematic review of financial distress identification using artificial intelligence methods |
| url | http://dx.doi.org/10.1080/08839514.2022.2138124 |
| work_keys_str_mv | AT dovilekuiziniene systematicreviewoffinancialdistressidentificationusingartificialintelligencemethods AT tomaskrilavicius systematicreviewoffinancialdistressidentificationusingartificialintelligencemethods AT robertasdamasevicius systematicreviewoffinancialdistressidentificationusingartificialintelligencemethods AT rytismaskeliunas systematicreviewoffinancialdistressidentificationusingartificialintelligencemethods |