A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

The Role of Artificial Intelligence, Financial and Non-Financial Data in Credit Risk Prediction: Literature Review

Small and medium-sized enterprises (SMEs) are of major importance in world economies and job creation. Financing is one of the key issues for SME development since SMEs are often considered riskier than large companies. It is argued in the literature that artificial intelligence (AI) and non-financial data could increase the financial inclusion of disadvantaged groups, such as SMEs. This article presents an overview of selected studies on credit risk prediction from the 1960s to 2022, covering topics of research work applying classical statistical methods, studies using AI methods on traditional financial data and studies applying AI methods on non-financial data. Literature overview results showed that the inclusion of non-financial data in credit risk prediction models could increase credit risk prediction performance, while AI methods can enable the inclusion of non-financial data. Since non-financial data potentially could be used as alternative data in credit prediction models, AI and non-financial data could help to increase access to finance for SME

SME default prediction: A systematic methodology-focused review

This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs’ default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets.publishedVersio

Predicting SME's default: Are their websites informative?

[EN] We propose the use of online indicators, scraped from the firms¿ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model.This work was partially supported by the Ca' Foscari University of Venice, Italy and by Agencia Estatal de Investigacion, Spain under grant PID2019107765RBI00. We also acknowledge helpful comments by an anonymous referee.Crosato, L.; Domenech, J.; Liberati, C. (2021). Predicting SME's default: Are their websites informative?. Economics Letters. 204:1-3. https://doi.org/10.1016/j.econlet.2021.109888S1320

Size effect, financial characteristics and insolvency profiles among the SMEs in Malaysia / Maran Marimuthu and Indraah Kolandaisamy

This study makes an attempt to analyze the effect of size on financial characteristics and insolvency of small medium enterprises. The conceptual framework is designed using the right measures, variables, concepts and models. A total sample of 229 businesses is considered consisting of small (57), medium (111) and large (61) SMEs. Non-parametric statistical techniques are used for empirical testing. The results indicate that size effect is significant only on profitability measures. There are no significant differences among the small, medium and large SMEs with regard to insolvency scores. In general, about 55 per cent of the large SMEs fall under the bankruptcy category, as compared to 39 per cent of the small SMEs and about 47 per cent of the medium SMEs. Large SMEs face greater financial risk and thus, face greater insolvency

Revisiting SME Default Predictors: The Omega Score

SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management- and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model’s predictive accuracy

Work Relationship Terminated Employees Legal Protection To Get Severance Payment From PT. Kertas Lecess Related To Law Of Bankruptcy And Law Of Labor

This research titled is work relationship terminated employees legal protection to get severance payment from PT. Kertas Lecess related to law of bankruptcy and law of labor. The position of the worker or labor can be seen in two aspects, namely in terms of juridical and socio-economic aspects. From a socio-economic point of view, workers need legal protection from the state for the possibility of arbitrary action by entrepreneur. The form of protection provided by the government is by making regulations that bind workers and employeee, in this case there is Law Number 37 of 2004 concerning Bankruptcy and Delaying Obligations of Debt Payment Jo. Law Number 13 of 2003 concerning Labor Jo. MK Decision No. 67 / PUU-XI / 2013PT. The regulation is used as a basis for employees of PT. Kertas Lecess to sue the BUMN to be declared bankrupt and responsible for paying severance for its employees. PT. Kertas Lecess is a state-owned enterprise (BUMN), which went bankrupt in September 2019. There are around 1800 workers who must receive termination of employment. The value of severance payment for workers affected by layoffs is around Rp. 300 billion. Employees affected by layoffs protest because they have not received severance payment and even 1,900 employees who have not received their salary for 4 years. PT. Kertas Lecess was decided  bankrupt by the Surabaya Commercial Court as a result of the cancellation of the peace proposal submitted by 15 of his employees on September 25, 2018. With the above considerations, the Panel of Judges of the Surabaya District Court, decided to grant the request for a cancellation of peace (Homologation) and stated that PT. Kertas Lecess is proven guilty of negligence for the non-payment of the salaries of PT. Kertas Lecess employees