Predicting Financial Distress in Microfinance Institutions: the Outperformance of Undersampling-Based Models

Fuzzy Tsukamoto is one method that is very flexible and tolerant of existing data. Fuzzy Tsukamoto has the advantage of being more intuitive, accepted by many, more suitable for the input received from humans rather than machines. Microfinance institutions are specialized financial institutions established to provide business development services and community empowerment, either through loans or financing in micro-scale businesses to members and the community, deposit management, and the provision of business development consulting services that are not solely for profit. The purpose of this study is to apply the fuzzy Tsukamoto method to determine the level of financial distress in microfinance institutions in the city of Medan based on the variables Liquidity Ratio, Age Firm, and Cumulative Profitability Ratio, Profitability Ratio, Financial Structure Ratio, Capital Turnover Ratio.

Microfinance is a valuable developing tool for alleviating poverty. Poverty remains to be one of the biggest policy concerns in Sri Lanka. Amongst various measures to eradicate it, Microfinance has provided a ray of hope. This paper presents a framework to predict the financial distress of such micro finance institutions operating in Sri Lanka using the Altman’s Z score model.
 Quantitative research approach was used based on secondary data from 2015 to 2019 on the sample population which comprised ten listed and unlisted microfinance institutions operating in Sri Lanka. This study applied the Altman Z score model on measuring financial health of selected microfinance institutions, where financial health is adopted as the dependent variable. Secondary data for the study were obtained through e-mail surveys at firm level, annual reports, and audited financial statements.
 The results of Z” Scores indicated that the mean financial health of the selected microfinance institutions has improved from 2015 to 2016, however there onwards it kept on deteriorating marginally. Thus, the microfinance institutions operating in Sri Lanka have a trend of downgrading their financial health moving closer to the Distress zone overtime.
 The distress prediction models used in this research, may not provide information as to what is wrong within the organization, but rather it will only give signals to identify the potential for financial distress which would encourage the firm to identify problems and take effective actions to minimize the incidence of failure.

Understanding the socioeconomic challenges faced by poor and vulnerable populations is crucial, as these groups are disproportionately affected by financial instability, inequality, and limited access to finance. Over-indebtedness remains a pressing concern, as excessive borrowing can lead to financial distress, reduced well-being, and long-term economic instability among low-income borrowers. This research examines the issue of borrowers’ over-indebtedness in Nepal’s microfinance institutions from the standpoint of client protection against the backdrop of global socioeconomic challenges such as rising inequality and increasing vulnerability of marginalized people. A questionnaire survey was administered among 180 MFI borrowers between August and September 2021, focusing on the sacrifices reported by borrowers as a result of their loans. Over-indebtedness is assessed using a subjective indicator based on reported loan-related sacrifices across 12 dimensions. These sacrifices are evaluated in terms of both acceptability and frequency. Using binary logistic regression, the study found that financial literacy, income level, multiple borrowing, and loan utilization are major predictors of over-indebtedness. The number of dependents in a household was found to be significant only in terms of acceptability, highlighting the role of family burden in financial distress. However, factors such as the type of business, the borrower’s age, and income volatility were not significant predictors of over-indebtedness. The findings suggest that financial literacy programs, improved income-generating opportunities, and responsible loan usage can mitigate over-indebtedness and promote sustainable financial inclusion. The results provide critical insights for microfinance institutions, policymakers, and regulators, emphasizing the need for strengthened client protection mechanisms. By addressing over-indebtedness, microfinance institutions can contribute to long-term financial stability and economic empowerment, ensuring that borrowers benefit from financial services without falling into debt traps. These findings contribute to the broader discourse on responsible microfinance practices, reinforcing the necessity for balanced policies that safeguard both financial inclusion and borrower well-being.

The objective of the present paper is to deal with the concept and working of MFIs with special focus on micro credit and the after effects on the poor occurring specially due to its mode of functioning in Indian economy. As there is no clear demarcation of the regulatory framework of the microfinance institutions in India, overlapping of regulation is prevalent. The government sectors as well as the stakeholders in the microfinance institutions have shown their eagerness to overcome this problem. This paper discusses the recent committee reports and the conflict arising out of the recommendations made in it. The critical financial condition of the MFIs and the suicides committed by the farmers due to financial distress shows the graft involved in the institutional setup and lack of proper vigilance on the government’s part. MFIs are observed as an important instrument for the welfare of the poor so it is the social responsibility of the government to revamp the structure of MFIs and make it more receptive for the benefits of the poor.

Despite the leading role that micro-entrepreneurship plays in economic development, and the high failure rate of microenterprise start-ups in their early years, very few studies have designed financial distress models to detect the financial problems of micro-entrepreneurs. Moreover, due to a lack of research, nothing is known about whether non-financial information and non-parametric statistical techniques improve the predictive capacity of these models. Therefore, this paper provides an innovative financial distress model specifically designed for microenterprise startups via support vector machines (SVMs) that employs financial, non-financial, and macroeconomic variables. Based on a sample of almost 5,500 micro-entrepreneurs from a Peruvian Microfinance Institution (MFI), our findings show that the introduction of non-financial information related to the zone in which the entrepreneurs live and situate their business, the duration of the MFI-entrepre-neur relationship, the number of loans granted by the MFI in the last year, the loan destination, and the opinion of experts on the probability that microenterprise start-ups may experience financial problems, significantly increases the accuracy performance of our financial distress model. Furthermore, the results reveal that the models that use SVMs outperform those which employ traditional logistic regression (LR) analysis.

This paper seeks to dispel the notion that Microfinance in itself is an effective poverty alleviation tool. It is a multi-faceted tool in promoting financial resilience, reaching the excluded, empowering women and developing the capacity of small groups of people to take control of their own lives. However, at the same time clients of Microfinance Institutions have fallen prey to high levels of over-indebtedness, financial distress and debt dependence. To tackle these challenges, development initiatives which can be clubbed with Microfinance have been explored, and suitable changes have been recommended, in this paper. The role of specialized algorithms which use a clients‘ digital footprints to determine his/her credit worthiness and the use of psychometric evaluations to assess default risk has also been looked at in great detail. The paper concludes with case studies of three new age microfinance institutions who are leveraging technology in novel ways to tackle challenges which, until now, seemed insurmountable.

Commitment leads directly to cooperative behavior, vital for long-term mutually beneficial relationships. When a relationship is weak and not considered in economic transactions, causing a microfinance institution (MIF) to disregard a small business entrepreneur’s actions, economic agents (i.e., the MIF and the entrepreneur) will lose economic rents. On the other hand, when a relationship is strong, and an entrepreneur’s actions are observed, the resulting economic rents will be such that each party will be better off building a relationship than not building it. COVID-19 in 2020 gave us an extraordinary opportunity to measure the impact of commitment in the interaction of MIF and survival entrepreneurs. Based on 876,920 observations from January 2020 to June 2021 of credits given to groups of survival entrepreneur women by a Mexican MIF, this study analyzes how relaxing credit policies during COVID-19 (financial distress time) has a positive effect on commitment from entrepreneurs to the MIF. This means that building a relationship between economic agents creates and distributes value among the economic agents.