Bankruptcy Prediction Using Machine Learning Techniques

Shetty, Shekar; Musa, Mohamed E. M.; Brédart, Xavier

doi:10.3390/jrfm15010035

Cited by 28 publications

(8 citation statements)

References 36 publications

(39 reference statements)

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“…Although bankruptcy is one possible way of resolving a bad situation, businesses are advised to strive to remain competitive and viable in the market. In order for a business to avoid bankruptcy, there are many models capable of predicting the risk of bankruptcy and assessing the health of the company from a financial and economic point of view (Bateni et al, 2020;Hafiz et al, 2018;Hu et al, 2020;Kitowski et al, 2022;Kovacova et al, 2020;Shetty et al, 2022;Voda et al, 2021). It is also necessary to consider the main reasons for bankruptcy, including insufficient sales revenues (Pasternak-Malicka et al, 2021), unqualified management and poor business-economic competencies, external bankruptcy causes (Mitter et al, 2021), the size of the firms and the years of experience of its managers also have an impact on financial failure (Bozkurt & Kaya, 2022).…”

Section: Discussionmentioning

confidence: 99%

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Bič

2022

IJEK

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Nowadays, entrepreneurs can very easily find themselves in a bad financial situation. That is why it is essential to look for ways to help them effectively. However, this cannot be done without knowing the real situation in each country, which is the basis for creating the appropriate conditions. The study's main objective was to identify and compare the development of insolvency indicators representing the bankruptcy of entrepreneurs in Slovakia and the Czech Republic from the first quarter of 2017 to the second quarter of 2021. Descriptive analysis and primary research methods were used to achieve this objective. The analytical processing dealt with the indicator of initiated bankruptcies. A fluctuating decline was observed in both countries. In 2020, a slight increase was recorded, and a further increase can be expected due to the coronavirus disease 2019 (COVID-19) pandemic. 2018 and 2017 were the least positive years for Slovakia and the Czech Republic, respectively. The results indicated the need for interventions in the business environment of both countries to help entrepreneurs in bad situations.

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Section: Discussionmentioning

confidence: 99%

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Bič

2022

IJEK

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“…SVM-RBF, a nonlinear kernel, reduced error rates but performed less than other ML models. Recently, Shetty et al (2022) applied three ML models, including SVM, a deep NN, and extreme gradient boosting (XGBoost), to a dataset of 3728 Belgian companies to predict bankruptcy. The results showed a global precision of 82%-83% between 2002 and 2012.…”

Section: Single ML Modelsmentioning

confidence: 99%

Evolutions in machine learning technology for financial distress prediction: A comprehensive review and comparative analysis

El Madou,

Marso,

El Kharrim

et al. 2023

Expert Systems

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In recent years, financial distress prediction (FDP), also known as corporate failure prediction or bankruptcy prediction, has gained significant importance due to its impact on organizations, especially during unexpected events like pandemics and wars. Machine learning (ML) models have emerged as innovative and essential tools in predicting financial distress, leveraging the ever‐increasing volume of databases and computing power. This study utilizes bibliographic techniques to contribute to the field's literature review to address the disorganized nature of the existing literature on FDP, reduce confusion, and provide clarity to domain researchers. These techniques enable identifying the progress of articles published over the years, influential authors, and highly cited articles. Additionally, the study examines crucial aspects of data preprocessing, such as missing data, imbalanced data, feature selection, and outliers, as they significantly impact the robustness and performance of ML models. Furthermore, it discusses essential models employed in FDP, focusing on recent advancements that represent promising trends. In conclusion, this study contributes to the field by uncovering novel trends and proposing possible directions for advancing FDP research. These findings will guide researchers, practitioners, and stakeholders in their quest for improved prediction and decision‐making in financial distress.

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“…According to Shetty et al [17], the 1990s ushered in a new phase in the evolution of business failure prediction models, introducing innovative methods, particularly artificial intelligence algorithms, including neural networks (Špiler et al [41]) and decision trees. These artificial intelligence-based techniques offer promising alternatives to traditional statistical models, addressing their principal shortcomings (Dong and Chen [42]).…”

Section: Financial Distress Prediction Modelsmentioning

confidence: 99%

“…Subsequent models have been introduced, such as the multiple discriminant analysis model developed by Altman [8] and Deakin [9], the logit model presented by Ohlson [10], and the probit model proposed by Hoetker [11]. More recently, artificial intelligence-based models have emerged, including neural networks (Altman et al [12] and Neves and Vieira [13]), decision trees (Gepp et al [14] and Chiang et al [15]), support vector machines (Alaka et al [16] and Shetty et al [17]), and genetic algorithm (Gordini [18]). The bankruptcy prediction and artificial intelligence literature reflects a shift towards more sophisticated data-driven approaches.…”

Section: Introductionmentioning

confidence: 99%

Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

Silva,

Brito,

Lourenço

et al. 2023

Sustainability

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Understanding business failure within the transport industry is crucial for formulating an effective competitive policy. Acknowledging the pivotal role of financial stability as a cornerstone of sustainability, this study undertakes a comparative investigation between statistical models forecasting business failure and artificial intelligence-based models within the context of the transport sector. The analysis spans the temporal period from 2014 to 2021 and encompasses a dataset of 4866 companies from four South European countries: Portugal, Spain, France, and Italy. The models created were linear support vector machines (L-SVMs), kernel support vector machines (K-SVMs), k-nearest neighbors (k-NNs), logistic regression (LR), decision trees (DTs), random forests (RFs), extremely random forests (ERFs), AdaBoost, and neural networks (NNs). The models were implemented in Python using the scikit-learn package. The results revealed that most models exhibited high precision and accuracy, ranging from 71% to 73%, with the ERF model outperforming others in both predictive capacity and accuracy. It was also observed that artificial intelligence-based models outperformed statistical models in predicting business failure, with particular emphasis on the AdaBoost and ERF models. Thus, we conclude that the results confirm the hypothesis that the artificial intelligence models were superior in all metrics compared to the results obtained by logistic regression.

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Bankruptcy Prediction Using Machine Learning Techniques

Cited by 28 publications

References 36 publications

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Development of Initiated Bankruptcies in Slovakia and the Czech Republic

Evolutions in machine learning technology for financial distress prediction: A comprehensive review and comparative analysis

Sustainability of Transport Sector Companies: Bankruptcy Prediction Based on Artificial Intelligence

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