Accurate correlation testing is essential for financial institutions, especially to manage risk and comply with regulatory frameworks such as the Fundamental Review of the Trading Book (FRTB) Internal Models Approach (IMA). Traditional correlation testing methodologies, while foundational, need to be revised to capture the complexities and dynamics of financial markets. This chapter critiques the current approaches to correlation testing, highlighting their limitations and risks when used in automated risk systems. Through an empirical analysis using Pearson, Spearman, and Heston models, this study demonstrates the inconsistencies and pitfalls of traditional methods. The need for a broader, multifaceted testing approach is emphasised, advocating for developing a comprehensive evaluation framework. Such a framework could incorporate a Red-Amber-Green (RAG) status system, akin to Value-at-Risk (VaR), providing a clear and actionable assessment of correlation models to enhance risk management practices and ensure regulatory compliance. Empirical results reveal significant discrepancies between the models, underscoring the necessity for a robust, integrated approach in financial correlation testing. The findings suggest that a more sophisticated, multidimensional evaluation can lead to more accurate and reliable risk assessments, better aligning with the stringent requirements of FRTB IMA and ultimately contributing to the stability and resilience of financial institutions.