(untitled) (International Journal of Banking and Finance, 2022)
This study contributes to the debate on the effect of Lending Concentration (LC) on credit risk. It is based on data from an online survey of 151 employees from 37 lending institutions, employees with diverse experience in the different sections of their institutions. Following a successful three-factor solution on LC based on an Exploratory Factor Analysis, Binary logistic regression models were implemented to determine the Perceived Lending Risk (PLR) based on three types of LC, namely Social Status Lending Concentration (SSLC), Private Sector Lending Concentration (PSLC) and Public Employee Lending Concentration (PELC). It was noted that over- concentration based on social status provided an explanation for the increase in Non-Performing Loans (NPL) risk among both large and small lenders. Since Lending Concentration reverses the effect of macroeconomic variables, such as credit risk management practices (CRMPs), Credit Processing Considerations (CPCs), as well as collateral types, assessing the degree at which the lender is concentrated across sectors is imperative, prior to any credit risk management initiative. Although LC directly affects lending risks perceptions alongside the traditional and corporate finance theories, the indirect LC effect via CRMPs, CPCs, bank size and originality, as well as the various collateral typologies seems to provide new insights into this area of research.