A two‐stage Bayesian network model for corporate bankruptcy prediction

Cao, Yi; Liu, Xiaoquan; Zhai, Jia; Hua, Shan

doi:10.1002/ijfe.2162

Cited by 30 publications

(16 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2019) found that deep neural networks can predict more accurately than the logistic regression and the naïve Bayes approach. Similar findings emerged from the study by Cao et al. (2020), observing the superior predictive ability of deep neural networks over support vector machines and Bayesian networks.…”

Section: Resultssupporting

confidence: 81%

“…Thus, Alexandropoulos et al (2019) found that deep neural networks can predict more accurately than the logistic regression and the naı €ve Bayes approach. Similar findings emerged from the study by Cao et al (2020), observing the superior predictive ability of deep neural networks over support vector machines and Bayesian networks. Recent developments in deep learning are long-term shortterm neural networks, which simulate a short-term memory by remembering previous expectations.…”

Section: Bankruptcysupporting

confidence: 81%

See 1 more Smart Citation

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Kureljusic

Karger

2023

JAAR

View full text Add to dashboard Cite

PurposeAccounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.Design/methodology/approachThe authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.FindingsThe authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.Research limitations/implicationsOwing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.Practical implicationsGiven the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.Originality/valueTo the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

show abstract

Section: Resultssupporting

confidence: 81%

Section: Bankruptcysupporting

confidence: 81%

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Kureljusic

Karger

2023

JAAR

View full text Add to dashboard Cite

show abstract

“…It shows a high ability in bankruptcy prediction with machine learning models to differentiate between bankrupt and non-bankrupt classes. The Receiver Operating Characteristic (ROC) curve is used to calculate the AUC, which measures the efficiency of the applied model to balance between FP and TP rates [44].…”

Section: Performance Evaluation Of the Proposed Modelmentioning

confidence: 99%

Intelligent Model for Enhancing the Bankruptcy Prediction with Imbalanced Data Using Oversampling and CatBoost

Aly

Alfonse²,

Salem³

2022

IJICIS

View full text Add to dashboard Cite

Bankruptcy prediction is one of the most significant financial decision-making problems, which prevents financial institutions from sever risks. Most of bankruptcy datasets suffer from imbalanced distribution between output classes, which could lead to misclassification in the prediction results. This research paper presents an efficient bankruptcy prediction model that can handle imbalanced dataset problem by applying Synthetic Minority Oversampling Technique (SMOTE) as a pre-processing step. It applies ensemble-based machine learning classifier, namely, Categorical Boosting (CatBoost) to classify between active and inactive classes. Moreover, the proposed model reduces the dimensionality of the used dataset to increase predictive performance by using three different feature selection techniques. The proposed model is evaluated across the most popular imbalanced bankrupt dataset, which is the Polish dataset. The obtained results proved the efficiency of the applied model, especially in terms of the accuracy. The accuracies ofthe proposed model in predicting bankruptcy on the Polish five years datasets are 98%, 98%, 97%, 97% and 95%, respectively.

show abstract

“…By identifying early warning signs and potential indicators of financial distress, predictive models play a vital role in informed decision-making and risk management. (Cao et al, 2022) Corporate bankruptcy signifies a state of financial insolvency where a company is unable to meet its debt obligations. This event can have profound effects on employees, shareholders, suppliers, and the broader economy.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Corporate Bankruptcy Prediction Research: Trends and Insights

2024

Journal of Informatics Education and Research

View full text Add to dashboard Cite

This paper is the outcome of a bibliometric study aimed at providing a worldwide overview of the prediction of corporate bankruptcy. This research paper provides a thorough examination of bibliometric analysis in the field of corporate bankruptcy prediction, specifically emphasising Artificial Intelligence-based models. This study uses rigorous bibliometric methods to identify and analyse the journals with the highest productivity, the papers with the most citations, and the authors with the most citations in this particular field. In addition, the research environment is illuminated by descriptive analysis of authors, countries, journals, and keywords. The study also examines the expansion and patterns in publications, offering significant insights for identifying periods of heightened interest and possible domains for additional investigation. In addition, this study examines country-specific annotations and analyzes individual and country-specific co-authorship to reveal collaboration networks and research contributions in various locations. Co-citation analysis and co-occurrence analysis of journals provide a more profound understanding of how knowledge is spread and the formation of topic clusters within the field. The results of this study have important consequences for research institutions and politicians, providing valuable insights that can lead to future research and influence policy decisions in the field of corporate bankruptcy prediction. It will guide future research and advance the discourse on financial stability and risk management.

show abstract

A two‐stage Bayesian network model for corporate bankruptcy prediction

Cited by 30 publications

References 58 publications

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Intelligent Model for Enhancing the Bankruptcy Prediction with Imbalanced Data Using Oversampling and CatBoost

A Bibliometric Analysis of Corporate Bankruptcy Prediction Research: Trends and Insights

Contact Info

Product

Resources

About