Systematic Review of Financial Distress Identification using Artificial Intelligence Methods

Kuizinienė, Dovilė; Krilavičius, Tomas; Damaševičius, Robertas; Maskeliūnas, Rytis

doi:10.1080/08839514.2022.2138124

Cited by 27 publications

(25 citation statements)

References 208 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The scientific literature frequently associates the concept of financial distress with bankruptcy, insolvency, the likelihood of default, and patterns of failure. Financial distress is commonly defined as the state of a company facing challenges in meeting its financial obligations [3]. In parallel, it is essential to distinguish between insolvency, bankruptcy, and distressed assets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the pulse of economic stability, predicting financial distress has spurred extensive research, evolving from early multivariate statistical models like the Altman Z-score model [1] to the contemporary frontier of machine learning (ML) algorithms. Altman's model was based on logistic regression and was widely used and developed by other researchers [2], [3], [4]. The application of machine learning algorithms for forecasting financial distress has gained significant prominence in the realm of corporate finance [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

Financial distress prediction is a crucial aspect of financial planning, particularly in the face of emerging uncertainties. This study introduces a novel methodology for predicting financial distress by amalgamating network analysis and machine learning techniques. The approach involves establishing two networks among companies based on their similarity and correlation in key financial indicators. The first network reflects similarity across five features, while the second captures correlation in the most critical feature. Subsequently, seven network-centric features are extracted and integrated into the dataset as new variables. Additionally, community detection algorithms are applied to cluster companies, with the resulting labels added as categorical variables. This process yields a modified dataset comprising both initial and network-based variables. Five classification algorithms are employed to forecast financial distress across three scenarios. Initially, models are trained using only the initial features. In subsequent scenarios, network-centric features from both similarity and correlation networks are incorporated, enhancing the predictive accuracy of machine learning models. Notably, features from the similarity network play a pivotal role in this improvement. The proposed model not only showcases superior predictive capabilities but also offers a holistic understanding of the dynamic interactions among financial entities. The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It needs to continuously optimize its algorithm to solve practical problems [5]. Some scholars conducted a systematic review of 232 studies, analyzed the necessity and urgency of applying artifcial intelligence technology to identify the fnancial difculties of supply chains, and pointed out that in data preprocessing, attention should be paid to data balance and dimensionality reduction, and the evolution index should be optimized to improve the model performance [6]. Some scholars also apply the machine learning algorithm to the operational risk management faced by the enterprises in the operation course.…”

Section: Evaluation Of Supply Chain Digital Financial Risk Prevention...mentioning

confidence: 99%

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Lei

Qiaoming

Zhao

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Artificial intelligence (AI) proves decisive in today’s rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

show abstract

“…The other school of thought considers bankruptcy as a distinct and separate event caused by severe liquidity problems (Farooq et al, 2018). The proponents of this view consider FD as a protracted process having different stages and bankruptcy as the final destination (Farooq et al, 2018; Kuizinienė et al, 2022). However, as argued by Donoher (2004), bankruptcy can be filed at any stage of FD since it may carry other motives.…”

Section: Theoretical and Empirical Debatementioning

confidence: 99%

“…Accordingly, extensive research has been carried out to explore how corporate governance practises mitigate organisational issues, such as earnings management, fraud and financial distress (Daily & Dalton, 1994a;Fich & Slezak, 2008;.The review of extant literature reveals that studies tend to use financial distress, insolvency, default, failure, and bankruptcy as synonyms albeit these terms have slightly different meanings (Alaka et al, 2018;Habib et al, 2018). Recent studies consider financial distress as a vivacious process having different stages (Farooq et al, 2018;Kuizinienė et al, 2022). In whatever way it is defined, financial distress is a dynamic situation in a firm's life cycle that may include different conditions such as profit reduction, dividend curtailment, diminishing liquidity, and insolvency.…”

mentioning

confidence: 99%

Corporate governance and financial distress: A review of the theoretical and empirical literature

2022

View full text Add to dashboard Cite

This study comprehensively reviews the theoretical and empirical literature on the relationship between corporate governance and financial distress and provides future research directions. We follow several different approaches by identifying, reviewing, and classifying related articles during the period 1985 to 2021. We used a set of keywords that included a combination of different terms of corporate governance and financial distress. Our review indicates that the majority of the studies based their arguments considering a single theory. The generalizability of different results in less explored countries is a serious concern and the one‐size‐fits‐all approach does not apply to the corporate governance‐financial distress relationship. Several different theories and confounding factors need consideration when examining the relationship between corporate governance and financial distress. Our review highlights future avenues by suggesting that studies should consider different governance attributes and test those factors in different and diverse contexts. Our review of the literature provides implications for academics and practitioners.

show abstract

Systematic Review of Financial Distress Identification using Artificial Intelligence Methods

Cited by 27 publications

References 208 publications

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Research on Supply Chain Financial Risk Prevention Based on Machine Learning

Corporate governance and financial distress: A review of the theoretical and empirical literature

Contact Info

Product

Resources

About