Statistical model infrastructures at financial institutions are often developed using a piecemeal approach to model building, in which different components of complex interrelated statistical models are developed and validated separately. We propose an empirical framework, spurred by recent developments in the implementation of generalized structural equation modeling (GSEM), which brings to bear a modular and all-inclusive approach to statistical model building. We illustrate the “game changing” potential of this framework with an application to the stress testing of credit risk for a representative portfolio of mortgages; we also extend it to the analysis of the allowance for credit loss under the novel Current Expected Credit Losses (CECL) accounting regulation. We illustrate how GSEM techniques can significantly enhance every step of the modeling management life cycle, from development and documentation to validation, production and redevelopment. We also illustrate how GSEM can be used to combine various risk management projects and tasks into a single framework; we specifically illustrate how to seamlessly integrate stress testing and CECL (or International Financial Reporting Standard 9) frameworks and champion, and challenger, modeling frameworks. Finally, we identify other areas of model risk management that can benefit from the GSEM framework and highlight other potentially fruitful applications of the methodology.