Imagine the peace of mind that would accompany being able to hand an existing model over to the validators with complete confidence in how the outcomes analysis will turn out. Now imagine being able to do this using a fully automated process. The industry is closer to this than you might think. The evolution of ongoing model monitoring away from something that happens only periodically (or, worse, only at validation time) and toward a more continuous process has been underway for some time. Now, thanks to automation and advanced process design, this evolutionary process has reached an inflection point. We stand today at the threshold of a future where: Manual, painful processes to generate testing results for validation are a thing of the past; Models are continuously monitored for fit, and end users are empowered with the tools to fully grasp model strengths and weaknesses; Modeling and MRM experts leverage machine learning to dive more deeply into the model’s underlying data, and; Emerging trends and issues are identified early enough to be addressed before they have time to significantly hamper model performance. Sound too good to be true? Beginning with its own internally developed prepayment and credit models, RiskSpan data scientists are laying out a framework for automated, ongoing performance monitoring that has the potential to transform behavioral modeling (and model validation) across the industry. The framework involves model owners working collaboratively with model validators to create recurring processes for running previously agreed-upon tests continuously and receiving the results automatically. Testing outcomes continuously increases confidence in their reliability. Testing them automatically frees up high-cost modeling and validation resources to spend more time evaluating results and running additional, deeper analyses. The Process: Irrespective of the regulator, back-testing, benchmarking, and sensitivity analysis are the three pillars of model outcomes analysis. Automating the data and analytical processes that underlie these three elements is required to get to a fully comprehensive automated ongoing monitoring scheme. In order to be useful, the process must stage testing results in a central database that can: Automatically generate charts, tables, and statistical tests to populate validation reports; Support dashboard reporting that allows model owners, users and validators to explore test results, and; Feed advanced analytics and machine learning platforms capable of 1) helping with automated model calibration, and 2) identifying model weaknesses and blind spots (as we did with a GSE here). Perhaps not surprisingly, achieving the back-end economies of a fully automated continuous monitoring and reporting regime requires an upfront investment of resources. This investment takes the form of time from model developers and owners as well as (potentially) some capital investment in technology necessary to host and manage the storage of results and output reports. A good rule of thumb for estimating these upfront costs is between 2 and 3 times the cost of a single annual model test performed on an ad-hoc, manual basis. Consequently, the automation process can generally be expected to pay for itself (in time savings alone) over 2 to 3 cycles of performance testing. But the benefits of automated, continuous model monitoring go far beyond time savings. They invariably result in better models. Output Applications Continuous model monitoring produces benefits that extend well beyond satisfying model governance requirements. Indeed, automated monitoring has significantly informed the development process for RiskSpan’s own, internally developed credit and prepayment models – specifically in helping to identify sub-populations where model fit is a problem. Continuous monitoring also makes it possible to quickly assess the value of newly available data elements. For example, when the GSEs start releasing data on mortgages with property inspection waivers (PIWs) (as opposed to traditional appraisals) we can immediately combine that data element with the results of our automated back-testing to determine whether the PIW information can help predict model error from those results. PIW currently appears to have value in predicting our production model error, and so the PIW feature is now slated to be added to a future version of our model. Having an automated framework in place accelerates this process while also enabling us to proceed with confidence that we are only adding variables that improve model performance. The continuous monitoring results can also be used to develop helpful dashboard reports. These provide model owners and users with deeper insights into a model’s strengths and weaknesses and can be an important tool in model tuning. They can also be shared with model validators, thus facilitating that process as well. The dashboard below is designed to give our model developers and users a better sense of where model error is greatest. Sub-populations with the highest model error are deep red. This makes it easy for model developers to visualize that the model does not perform well when FICO and LTV data are missing, which happens often in the non-agency space. The model developers now know that they need to adjust their modeling approach when these key data elements are not available. The dashboard also makes it easy to spot performance disparities by shelf, for example, and can be used as the basis for applying prepayment multipliers to certain shelves in order to align results with actual experience. Continuous model monitoring is fast becoming a regulatory expectation and an increasingly vital component of model governance. But the benefits of continuous performance monitoring go far beyond satisfying auditors and regulators. Machine learning and other advanced analytics are also proving to be invaluable tools for better understanding model error within sub-spaces of the population. Watch this space for a forthcoming post and webinar explaining how RiskSpan leverages its automated model back-testing results and machine learning platform, Edge Studio, to streamline the calibration process for its internally developed residential mortgage prepayment model.