This research is to study the impact of some financial risk indicators on fifteen selected commercial banks’ in Ghana. The indication from the augmented Dickey-Fuller unit root test results show that the data series after first difference at the first order achieved stationarity. The analysis of the data revealed the existence of significant long run relationship between bank financial performance and the variables of financial risk in the banking sector. The granger causality test results reveal that there is unidirectional causality flowing from the variables of financial risk This suggest that the indicators of financial risk strongly and actively stimulate and improve the financial performance of banks in Ghana. The study recommends that bank managers should improve on the management of all the indicators of financial risk variables in order to improve on the achievement of the objective of the firm.

Credit risk has great impact on the banks' profitability as large chunk of banks' revenue and interest income comes from loans. Factors that affects credit risk can be classified into microeconomic and macroeconomic factors. These factors have an impact on credit risk levels. Using secondary data from 2005 to 2018 for listed commercial banks on the two stock exchanges in China mainland, the study explored the effect of both internal and external factors that influences credit risk in the banking industry. A positive relationship was found between bank solvency and credit risk. Similarly, the study revealed a positive correlation between credit risk and interest rate. On the contrary, operation efficiency and gross domestic product growth rate revealed an inverse relationship with credit risk. The findings will add up to existing literature and guide policy-makers in implementing measures to control the influencing factors of credit risk in the banking industry.

In assessing microfinance institutions (MFIs) and civil servants' perspectives on borrowing in Zimbabwe, we examine the purpose and rationale of MFIs establishments. Thus, in an attempt to understand the reason behind high borrowing, we also considered loan terms, the nature of loans issued, and the uses of MFIs borrowed funds among households. Driven by the exploratory approach, qualitative research involving semi-structured interviews and observation methods were applied in this study. Using, the purpose of the loan, pricing of loans, repayment terms, and loan terms, interview questions were designed and conducted. Our results show that MFIs loans are: short term loans, income (salary) based; and, these loans are mainly for immediate household consumption needs not an investment. This study also indicates that loan application requirements are more favorable for employed households, especially public sector employees. Even though civil servants have a better advantage in accessing MFIs loans, in the long run, they are likely to remain in poverty; since their purpose of borrowing is geared towards family expenses. Also, MFIs prevailing interest rates (high), evidenced with shorter repayment periods, reflect their failure to pull borrowers out of poverty; however, creating an interdependence syndrome of continuous borrowing. Since we focused on lending practices of households, our results serve as a basis of a joint policy formulation in combating poverty. Thus, understanding poverty through the borrowing of employed citizens aids in grasping the interconnectedness of sectors; which, is an essential tool for sustainable development and strategic planning.

Financial innovation in recent years have prominently contributed to the growth of Peer-to-Peer lending marketplaces allowing individual and businesses to secure loans on a common internet-based network. Similar to the ‘bricks and mortar’ banking system, online lending is coupled with the problem of information asymmetry. Borrower risk assessment has henceforth become the major concerns of various platforms that aim to reducing information imbalance towards mitigating credit risk. In this article, authors compared two learning algorithms – Logistic regression and Artificial Neural Network to classify borrowers based on loan repayment schedule. We revealed that both approaches were robust in classifying late borrowers with logistic regression being 0.02% more robust than Neural Network. Regarding variable relative importance, gender is considered the least important variable whereas terms-of-repayment is the most important variable affecting borrowers’ intention to pay off loans. Even though our study contributes to existing literature, it is however not limited to determining factors that may affect lenders’ investment decision in social lending.