ModelOps for Structural Analytics
Over the past few years, MLOps has become an essential discipline for organisations deploying AI/ML models at scale. Inspired by DevOps, it focuses on the infrastructure, operating model, and toolkits needed to manage models as enterprise assets. While this has helped industrialise AI/ML pipelines, most banks and financial institutions still rely heavily on deterministic, rules-based models in critical areas like credit scoring, regulatory capital, accounting valuations, and scenario analysis — a domain we refer to as ‘structural analytics’.
Built Differently, Used Differently
In structural analytics, models are not purely the output of a training process. They are designed upfront to comply with regulations, proven practices and business intuition and are updated in response to policy changes as well as business events. They are also deterministic: when run with the same inputs, they should always produce the same outputs.
Many of the core MLOps principles — such as versioning and deployment discipline — are still relevant. But ModelOps for structural analytics must address additional priorities:
Immutability and repeatability: Systemic versioning must apply across the entire job — not just model code, but data, configuration, and business logic — to ensure results are fully traceable and reproducible.
Structured experimentation: Users need the ability to run what-if, challenger, or scenario analyses without risk to the production environment. This isn’t the same free-form flexibility valued in data science — it’s controlled flexibility within governed boundaries.
Documentation: To meet existing governance frameworks — whether MRM, financial control, or audit — production systems should be capable of self-generating documentation that reflects how models are configured, run, and changed over time.
Conclusion: ModelOps for Structural Analytics
As regulatory expectations grow and business users demand faster, more trusted insights, the current patchwork of manual tools and brittle processes is no longer sustainable. Structural models may not be AI — but they are business-critical infrastructure. They deserve the same level of control, automation, and visibility. A tailored ModelOps approach for structural analytics brings together the rigour of MRM, the discipline of MLOps, and the realities of how these models are built and used. For banks, this means stronger controls, faster change, and better outcomes — without compromising trust.
