This paper aimed at identifying the leading credit risk indicators in the South African banking context as well as the development of an integrated leading credit risk indicator model. A content analysis was used as a data extraction methodology and structural equation modelling was used as a data analysis methodology. The results obtained indicated that utilising the structural equation modelling, gross savings, and prime overdraft rates, number of judgements, business insolvencies and unemployment rates were formulated as leading economic and market (external) indicators of credit risk in the South African banking context. Similarly, utilising the principal component analysis, bank asset quality, bank asset concentration as well as bank trading and hedging activities were formulated as leading bank specific (internal) indicators of credit risk in the South African banking context. The Integrated Leading Credit Risk Indicator Model (ICRIM) was formulated utilising the accepted leading credit risk indicators. The ICRIM parameters were benchmarked against the generally accepted fit indices such as the RMSEA, comparative fit (baseline comparison) as well as the Hoelter and its results output were found to be consistent with these generally accepted fit indices.
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