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This article maps the knowledge structure and evolution of bankruptcy/default prediction research in financial institutions using a bibliometric co-citation approach, which identifies works that are often cited together due to the proximity of their theories, methods, or themes. Bibliographic data were collected from the Web of Science (WoS) for the period 1970-2025, then extracted in a complete bibliographic format and prepared for network analysis. The co-citation procedure is carried out by extracting the reference list, normalizing and unifying citation variations, and preparing a co-citation matrix, which is then visualized and classified using VOSviewer with minimum threshold parameters and normalized association strength. The results of the document ranking based on the number of citations and Total Link Strength (TLS) show the development trajectory of the field from the ratio and Z-Score approach to the logit probabilistic model, the market foundation based on the structural theory of credit risk, the governance perspective supported by agency theory, the dynamic panel methodology/GMM for longitudinal data, to computational waves (neural networks and machine learning) to capture nonlinear patterns of credit scoring. The co-citation map uncovers four main clusters: econometrics & banking risk, structural theory, ratios, and computational, with Merton and Altman acting as the cross-cluster binding nodes. These findings support the design of a more comprehensive early warning system (EWS) by integrating ratio signals, market indicators, governance/agency factors, and ML models to improve accuracy and adaptation.    

This paper examines the impact of market share and capital adequacy on the bankruptcy rate of community banks in Tanzania, for the period spanned for 16 years from 2006 to 2021. For instance, recently in Tanzania, seven community banks were closed by the Regulator while others were being merged to increase their going concern and operational efficiency. This study has employed a quantitative approach based on secondary data collected from the Bank of Tanzania as a Regulator and some of them were gathered from community banks. The sample size is 11 community banks, with a total of 176 observations. The panel data analysis has been conducted using statistical software, namely, STATA version 17 with the random effect model being used to generate regression results ready for interpretation. The research findings depicted significant positive correlations between bankruptcy rates as computed using Altman’s Z-score model and the independent variables, namely, market share and capital adequacy ratio. In that regard, the study recommends adequate capital levels and market share to reduce the bankruptcy rates of community banks in Tanzania.

This study aims to enhance credit risk identification, improve loan borrower review efficiency, and increase default prediction accuracy rate using data mining and machine learning techniques. The study also employs deep learning to develop a consumer loan default prediction model that minimizes credit risks and ensures consistent development. The researchers collected data from a survey of 1000 participants, stratified into local and foreign banks, and selected the top 11 banks based on turnover and customer volume. To construct the machine learning model, Keras, a neural network library that runs on TensorFlow, was utilized. The model predicts loan applicant default likelihood. The study's practical implications demonstrate a noteworthy success rate of customer default prediction, which can significantly benefit banks. The model was evaluated on a test set of 250 records and achieved a test set accuracy of 95.2%, correctly predicting the default state of 238 out of 250 respondents.

The purpose of this study is to examine the differences in the value of the financial distress of manufacturing companies before and after the Covid-19 pandemic, as well as to examine the determinants that affect the financial distress of manufacturing companies before and after the Covid-19 pandemic. This study is a quantitative study with the population in this study namely manufacturing companies in Indonesia. The sample is 1005 Firm Years in manufacturing companies in Indonesia. These Manufacturing Companies were taken from 2016-2021, but we divided the sample into 2 categories, namely 2016-2019, namely in the pre-pandemic period as many as 653 firm years, and during the pandemic as many as 352 firm years. The dependent variable used in this study is financial distress, measured by the O-Score Model. Furthermore, the independent variables are profitability using ROA, liquidity using the current ratio, and leverage using the Debt to Assets Ratio. The test results show no difference between the financial distress of manufacturing companies in Indonesia before and during the pandemic. During the pandemic, financial ratios became more considered in predicting financial distress than before the pandemic. The role of profitability is a factor that impacts financial distress is more impactful in the pre-pandemic period. The role of liquidity being a factor that impacts financial distress is more impactful during the pandemic. Leverage can also be a good indicator in predicting financial distress both in the pre-pandemic and during the pandemic.

This study analyzes the bankruptcies of companies in the plastic and packaging industry listed on the Indonesian stock exchange. This analysis is essential to know the condition of the company's performance and in what position. The hope is to assist stakeholders in making the best decisions for sustainability. The positive contribution of the plastics and packaging industry in supporting economic growth in Indonesia, as well as the challenges of global issues regarding the negative impact of use, requires companies to avoid financial distress or bankruptcy. However, recent research argues that very few company managers try hard to avoid the risk of bankruptcy. This quantitative descriptive research takes a sample of 10 companies selected purposively with predetermined criteria. Following the arguments of Altman (1983), the research analysis uses the Altman Z-Score for a sample of firms in one homogeneous industry. The results showed that, in general, the plastic and packaging industry was potentially vulnerable and had financial distress or went bankrupt, and there was only one healthy company. The results of this study also have an essential contribution for stakeholders to detect early on the company's financial condition and help make sound managerial decisions.

Beginning in the mid-1970s, financial distress in local governments became big news when New York City nearly collapsed due to the energy crisis and high inflation. When local governments experience financial distress, the responsibility to provide services to the community both now and in the future cannot be fulfilled. Local governments can only maintain services to the community if they are able to avoid financial distress. If the financial distress condition is maintained, there will be a stagnation in the level of community welfare which will have an impact on regional economic growth because it is not supported by adequate public services. Some local governments like Pennsylvania, have implemented a measure to indicate financial distress as stipulated in the 1987 Financially Distressed City Act (UU 47). However, the local government in Indonesia does not yet have an absolute measure for this financial distress condition. For this reason, financial distress in this study will use a relative measure indicated by the probability value measured using a defined approach. The results of the study provide an overview of how the relevant values in local government financial statements are able to predict financial distress in local governments. The findings show that the variables intergovernmental revenue (IGR), financial independence (FIND), employee expenditure (EMPEX), capital expenditure (CAPEX), and operating position (OPPOS) have a significant effect and can be used as an alternative predictor of financial distress with more comprehensive suitability with the financial characteristics of local government in East Java Province.

This study aims to examine and analyze the effect of financial distress with the Altman and Springate Models on stock returns either directly or indirectly by involving earnings management as a mediation. This study uses secondary data from Textile and Garment Companies listed on the Indonesia Stock Exchange from 2015-2019, with a sample of 20 companies using sampling so that 100 observations are obtained. The data is obtained from the annual financial statements. The data analysis technique used SEM-PLS with the help of WarpPLS 6.0 software. The results of the study provide empirical evidence that financial distress has a positive effect on earnings management, while financial distress and earnings management has a negative effect on stock returns. Earnings management is able to mediate the effect of financial distress on stock returns.

Related party transactions are a key factor to the sustainability of Savings and Credit Cooperatives (SACCOs). In view of this fact, loans to directors and staff are viewed as a factor which can greatly influence savings and credit cooperatives into either falling into financial distress or helping them to remain a float and become financially stable entities. The stakeholder theory holds that entities should be managed a manner that will satisfy every stakeholder. The stewardship theory reiterates that managers have the organisation at heart and can it to profitability as if it were their own. However approval of loans and other transactions to managers and staff, may not positively impact the entity. This study was designed to establish the effect of related party transactions on the relationship between board characteristics and financial distress of deposit taking SACCOs in Nairobi County. We applied Descriptive research design on Deposit taking SACCOs in Nairobi County which was identified purposively while a census was conceded for all deposit taking SACCOs in the county. We obtained secondary data from SASRA using a data collection sheet after which we performed panel data analysis by use of STATA software. Findings were presented using tables. The study concluded that related party transactions influenced the relationship between board characteristics and financial distress of Deposit Taking SACCOs in Nairobi County. Related party transactions can be an avenue of causing financial distress and should be kept as low as possible. The regulator should come up with a tool based on Altman’s Z score models to predict financial distress in SACCOs in order to offer timely advice to alleviate more distress and consequent bankruptcy which may lead to closure of SACCOs. Another research may be carried out to establish other factors causing financial distress and how to turn around the SACCOs already in distress.

The widespread failure of DT SACCOs in Kenya is likely to lead to a loss of confidence among the current and potential members of the DT SACCOs in Kenya and eventually threatens to kill the sector. The study examined the effect of related party transactions on financial distress in DT-SACCOs in Kenya; the study adopted the following theories as to the basis of analyzing the collected data; wrecker’s financial distress theories and Agency theory. The target population was 176 Deposit-Taking SACCOs. Secondary data was obtained from SACCOs records as published by SASRA. The study used a systematic sampling technique based on the sampling formula by Taro Yamane (1967) to obtain the appropriate sample size of 68 DT SACCOs. Data were analyzed using STATA computer software. Data collection covered seven (7) year period from 2008 -2014, this period of 7 years was selected for the study because SASRA has enacted in 2010 therefore the justification for the choice of the study period, that is 3 years before and 4 years after SASRA enactment. The study established that related party transactions had a significant effect on financial distress in DT SACCOs in Kenya. The implications of the findings are that if the DT-SACCOs do not manage related party transactions then they will continue experiencing financial distress. The government through SASRA therefore should enact strict regulations in directors borrowing from SACCOs. The study has expounded on the body of knowledge on measures of financial distress in finance by introducing new relationships established. In terms of the contribution of the current study to innovation, the study used panel data analysis while other studies used OLS.

The importance of Savings and Credit Cooperatives (SACCOs) cannot be underestimated. Despite their importance, they are faced with numerous challenges among them financial distress which threatens their very existence. The current research sought to establish the role of board characteristics in the financial distress suffered by Deposit Taking SACCOs in Nairobi County. The study is anchored inter alia on Agency Theory. Descriptive research design was adopted while Nairobi County was purposively chosen and a census was carried out on deposit taking SACCOs in the county. Secondary data was collected from SASRA using a data collection sheet and a panel data analysis performed using STATA software. The findings were presented using tables. The study concluded that there was a relationship between board characteristics and financial distress of Deposit Taking SACCOs where board composition, board education and board tenure have statistically significant and negative influence on financial distress. In conclusion SACCOs need to have lean boards, Board composition should also be improved by including more women on boards, there should be more inclusion of members with high and relevant education credentials, and SACCOs should have term limits for their members while an analysis too based on the Altman’s Z score models should be adopted for SACCOs. Another research may be carried out to establish other factors causing financial distress and how to turn around the SACCOs already in distress.

This study aimed to determine financial and non-financial factors influencing future cash flow and their impacts on company's financial distress. This study uses earnings, cash flow from operations, accrual components, working capital (financial), inflation (nonfinancial) as independent variables that are thought to have an effect on future cash flow. In addition, this research also suspects that there is an impact of this influence on company financial distress. The sample used in this study was 30 food and beverage manufacturing companies listed on the Indonesia Stock Exchange from 2013 to 2015. Hypothesis testing, in this research, used Structural Equation Model (SEM) method with AMOS 24 statistic. The result showed that earnings, accrual component, and working capital had a significant positive effect on future cash flow. Furthermore, cash flow from operation had significant negative effect on future cash flow, but inflation did not affect company's future cash flow. These results indicate that financial variables have a significant effect, both positive and negative, on the company's future cash flow. Meanwhile, non-financial variables have no significant effect. Other test resulted that future cash flow had a significant negative effect on financial distress. Based on the results of this study, it can be seen that stakeholders, especially investors, really pay attention to the company's financial performance compared to non-finance. This is due to the significant influence of financial variables on the company's future cash flow and it can have an impact on financial distress. Therefore, future research is expected to explore other financial factors that affect the company's future cash flow, for example, the company's financial risk.

Iran’s banking industry as a developing country is comparatively very new to risk management practices. An inevitable predictive implication of this rapid growth is the growing concerns with regard to credit risk management which is the motivation of conducting this research. The paper focuses on the credit scoring aspect of credit risk management using both logit and probit regression approaches. Real data on corporate customers are available for conducting this research which is also a contribution to this area for all other developing countries. Our questions focus on how future customers can be classified in terms of credibility, which models and methods are more effective in better capturing risks. Findings suggest that probit approaches are more effective in capturing the significance of variables and goodness-of-fitness tests. Seven variables of the Ohlson O-Score model are used: CL_CA, INTWO, OENEG, TA_TL, SIZE, WCAP_TA, and ROA; two were found to be statistically significant in logit (ROA, TL_TA) and three were statistically significant in probit (ROA, TL_TA, SIZE). Also, CL_CA, ROA, and WCAP_TA were the three variables with an unexpected correlation to the probability of default. The prediction power with the cut-off point is set equal to 26% and 56.91% for defaulted customers in both logit and probit models. However, logit achieved 54.85% correct estimation of defaulted assets, 0.37% more than what probit estimated.

<p>A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.</p>

A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.