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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.

<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>

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.

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 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.

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 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.

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.

<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>