In many banks, updating regulated models in production can take weeks — sometimes months — because the model must be re-coded as it moves from development to production.

This is an outdated approach. With TRAC’s open-source runtime service, models behave identically wherever they run — drastically simplifying the route-to-live and eliminating unnecessary rework.

In highly regulated environments like banking and insurance, the demand for explainability, auditability, and replicability doesn’t stop at model development. It extends all the way through deployment, execution, and reporting — which is where most platforms fall short. With trac, we’ve built model orchestration platform that is self-describing by design, so every calculation is explainable, auditable, and repeatable and there is no need for manual documentation.

Fintrac is delighted to announce a strategic partnership with Jaywing, a consulting firm with deep expertise and a long track record of success in data analytics, credit and fraud risk management, and regulatory advisory. Together, we have the opportunity to help reshape the analytical capabilities of the financial sector.

Research suggests that between 30–60% of the effort involved in managing processes such as IFRS9, RWA, and regulatory stress testing — is spent on documentation. Not building models but explaining how they were deployed, evidencing controls, attesting to system configurations, and completing internal audit reviews. Much of this burden can be eliminated by investing in production systems - like TRAC - that are self-describing and self-documenting.

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.

When we started fintrac, our goal was simple: to modernise structural analytics for financial services and beyond. Today, we’re taking a big step in that direction. We’re launching a free version:: TRAC Community.

With regulators appearing more sanguine about bank capitalisation levels, annual stress testing exercises are no longer the high-stakes events they once were. Like many risk management practices introduced after the global financial crisis, stress testing is now completing its transition into a business-as-usual (BAU) activity. So, what defines a modern stress testing infrastructure?

This article explores the three key capabilities that set truly effective platforms apart - modular deployments, runtime flexibility and the ability to self-document

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.