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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.

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.

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.

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.

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.

Financial institutions predict the probability of default when making lending decisions using conventional credit risk scoring models. However, in emerging economies, the underbanked, widows, small and medium enterprises owners and youth are not able to access traditional forms of collateral or identification required by financial institutions due to lack of data for them to get access to loans. Financial institutions cannot obtain much of the information required about an applicant and they use alternative data sources such as public data or social media to deal with the problems of information asymmetry, adverse selection and moral hazard. To eliminate such problems, credit risk assessment models must be built on the foundation of artificial intelligence and machine learning approaches in an effort to perform an accurate credit risk analysis, to properly assess the behaviour of the customers and subsequently perform a thorough verification of the potential of the applicant to repay the loan thereby allowing less privileged people to access credit. However, big data sourced from the internet and public data sources is characterized by huge volume, variety, veracity, the curse of dimensionality, class imbalance, concept drift and non-linearity among others. These challenges affect the generalization performance of credit risk assessment models used by most financial institutions. This paper proposes an efficient and effective credit risk assessment model capable of learning incrementally based on machine learning algorithm called Adaptive Heterogeneous Dynamic Ensemble Selection (AHDES) that uses big data from alternative sources for financial for the underbanked. The algorithm is agile and adaptive to unexpected world events, changes in customer behaviour and cognitively counter bias likely to be introduced by information asymmetry. Experimental results on four large credit datasets and five evaluation metrics show that our proposed algorithm performs better than other selected benchmark models allowing the underbanked and less privileged to access loans to stimulate economic growth.

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 selected variables on the value creation of tourism enterprises in Western Austria (Salzburg, Tyrol, and Vorarlberg) with the objective of developing an insolvency prediction model for Alpine tourism businesses. This study specifically highlights practical applications for business owners, policymakers, and financial institutions, providing strategies to mitigate bankruptcy risks in the tourism sector. The objective of this study is to address existing gaps in the literature regarding the early detection of insolvency in the tourism sector, particularly in German-speaking regions. To this end, a hybrid analytical method is employed, combining quantitative statistical approaches with sector-specific contextual analysis. A dataset comprising tourism firms was divided into two groups, namely solvent and insolvent, with a view to identifying significant explanatory variables influencing insolvency risks. The primary findings indicate that enterprise size, company age, and debt levels (as indicated by the equity ratio) are significant risk factors for insolvency. Furthermore, the results suggest that small and medium-sized enterprises (SMEs) are particularly vulnerable to financial crises. Finally, the findings demonstrate that macroeconomic structures and sectoral effects significantly impact insolvency probabilities. These findings emphasise the necessity for the development of early warning systems that are tailored to the specific requirements of SMEs in Alpine tourism.This research is particularly pertinent in light of the rising insolvency rates in the tourism industry and the lack of comprehensive official statistics, which impede effective crisis management. By addressing these challenges, the study contributes to the development of targeted risk management and intervention strategies to enhance the resilience of tourism enterprises in the region.

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.