The idea behind investigating the leading external and internal indicators of credit risk is premised on the proposal that banks have an important role in the economy. The relationship between banks and the economy is driven by the role of banks which many researchers are in agreement that banks assume the financial intermediation role in the economy (Allen & Carletti, 2008; Boot & Marinc, 2008; Delibasic, 2008 and Wilson et al., 2010).
For Delibasic (2008), banks become financial intermediaries when they finance different business activities in the economy. To be able to finance different activities as suggested by Delibasic (2008), Allen and Carletti (2008) state that banks collect demandable deposits to raise funds in the short term and invest these funds in long term assets (i.e. loans to different sectors of the economy). Allen and Carletti (2008) refer to the transformation of deposits into loans as a “maturity transformation role” of banks in the economy.
Based on this relationship, it is clear that any instability in the banking system would result in instability within other sectors of the economy, thus impacting the growth of the economy. Banque de France (2008) support this statement and state that banks assume an important role in the financing of economies and in their view, should banks be unable to perform their assumed position (financing function) the economic growth of a country will be compromised.
Within this knowledge, I have undertook an research aimed at identifying the leading credit risk indicators in the Camerronian 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.
For Delibasic (2008), banks become financial intermediaries when they finance different business activities in the economy. To be able to finance different activities as suggested by Delibasic (2008), Allen and Carletti (2008) state that banks collect demandable deposits to raise funds in the short term and invest these funds in long term assets (i.e. loans to different sectors of the economy). Allen and Carletti (2008) refer to the transformation of deposits into loans as a “maturity transformation role” of banks in the economy.
Based on this relationship, it is clear that any instability in the banking system would result in instability within other sectors of the economy, thus impacting the growth of the economy. Banque de France (2008) support this statement and state that banks assume an important role in the financing of economies and in their view, should banks be unable to perform their assumed position (financing function) the economic growth of a country will be compromised.
Within this knowledge, I have undertook an research aimed at identifying the leading credit risk indicators in the Camerronian 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.
