As artificial intelligence accelerates across financial services, institutions are rethinking how they manage and validate increasingly complex model ecosystems.
Rising model inventories, growing interconnectedness, and mounting regulatory expectations are pushing risk and validation teams into a new era in which traditional approaches no longer scale.
And according to a leading model risk expert, the challenge now is building a validation function capable of safeguarding integrity while enabling innovation, even as the technological landscape shifts beneath it.
Speaking at CeFPro’s flagship Risk Evolve 2025 event, Rita Gnutti, Executive Director, Head of Internal Validation and Controls Head Office Department at Intesa Sanpaolo, described how her institution has undergone a wide-ranging transformation of its model risk and validation function.
She explained that the model landscape has grown dramatically since 2020, with the inventory rising from fewer than 300 models to nearly 700.
This surge spans both risk management and business functions, introducing an entirely new generation of AI-driven and machine-learning-based models.
According to her, this expansion places mounting pressure on second line of defence teams, which must provide independent oversight despite resource constraints and rising model interconnectedness.
As she noted, independent coverage has fallen below 70 percent, making transformation essential.
Gnutti stated that her team has responded by redesigning the model risk management framework, and she highlighted a structured assessment methodology that evaluates inherent and residual risk across model design, implementation, data quality, performance, and use.
She emphasized that AI-specific dimensions such as explainability, fairness, hyperparameter robustness, and transparency are now integrated directly into risk assessments.
Gnutti also explained that all AI systems, whether formally classified as models or not, are centrally inventoried to ensure oversight across both proprietary and third-party applications.
A second stage of transformation focused on digitalization. Gnutti described how her team built an automation tool to streamline ongoing validation, integrating methodological handbooks, testing procedures, and validation code into a unified digital workflow.
This tool, she said, significantly reduces manual workload and allows experts to concentrate on higher-value analysis. Early use demonstrated substantial time savings, strengthening the case for broader automation.
