MLOps has become an essential discipline for organisations deploying AI/ML models at scale but most banks and financial institutions still rely heavily on deterministic, rules-based models. A Model Ops framework for this form of structural analytics therefore needs to go beyond the standard tenants of MLOps.

Over the past few years, banks have made huge progress on model governance. But operational efficiency hasn’t kept pace. Many institutions still rely on manual processes and disconnected tools to run their most critical models.

In this short blog, we explore why ModelOps needs to sit alongside MRM — and how it can help reduce risk, improve transparency, and bring some much-needed efficiency to structural analytics.

The banking sector is undergoing a profound transformation as organisations try to modernise their data and analytics capabilities. Significant strides have been made in digitisation, cloud adoption, and the deployment of AI and machine learning. Yet modernising structural analytics — the analytical backbone of Risk and Finance functions — remains a formidable and often overlooked challenge.