Robust quantitative models have been an essential component of risk management and analysis for decades, but have dramatically increased in sophistication and complexity over the last few years. Rapidly changing market dynamics and increased regulations demand robust financial models that address a broad range of evolving risks. Recent high-profile model failures have emphasized the importance of modeling practices that employ stringent implementation, validation and review techniques to manage the hidden risks risks within models themselves. As a result, model risk is increasingly on every risk managers radar. In this article, we explore the evolution of model risk, including regulatory drivers and industry challenges. We also take a closer look at model risk analysis, examining model assessment, validation and review processes. We examine model risk management methodologies, using a case study example involving a large insurer. In conclusion, we outline what we believe are the four best practices for model risk management in todays financial marketplace.