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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 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.

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 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.

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.