Appropriate modelling of loan default correlation capturing the fat tail distributions and non-symmetrical behaviour linked to the sensitivity of the loss correlations is a prerequisite for effective credit risk management, as banks seek to optimally allocate capital. In this study, we provide an insight to the use of copula functions, particularly addressing the key question of why Gaussian copulas caused so much instability during 2007-08. We empirically demonstrate that using an Archimedean copula, particularly the Gumbel, it is more efficient in capturing the top right hand side tail-dependencies, thereby illustrating the impact of fat-tails on non-linear parameters. This finding has significant implications for banks and their capital management requirements, particularly banks employing the Advanced Internal Rate-Based method. This is even more relevant now, with Basel III providing more detailed information as to what constitutes Tier 1, Additional Tier 1 and 2 Capital.