Model Risk Management in the Age of AI and Analytics

Presentation

Model Risk Management Evolves Amid AI and Analytical Tool Explosion

Tingting She, Head of Model Risk Management at Bank of China, explores the shifting landscape of model risk in the AI era. She outlines the challenges in defining, validating, and governing modern AI models versus traditional ones, and proposes both short- and long-term frameworks for adapting risk governance accordingly.

May 22, 2025

Tingting She, Head of Model Risk Management, Bank Of China New York Branch

Tags: Model risk

The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization

vTingting She provides a compelling overview of how traditional model risk management frameworks are being tested by the rise of AI and advanced analytics.

While the foundational definition of a "model" remains unchanged, the tools and technologies now in use—ranging from simple spreadsheets to generative AI—blur the lines between what qualifies as a model versus a non-model, creating challenges in governance and compliance. The rapid expansion of analytical tools and AI applications requires firms to rethink their inventories and risk frameworks.

She outlines a dual-path forward: a short-term bottom-up approach that assesses each AI application individually to determine the appropriate governance framework, and a long-term top-down strategy that calls for a flexible umbrella framework. This would encompass all analytical tools—models, EUCs, and AI-driven systems alike—without being constrained by outdated regulatory boundaries.

The session emphasizes the importance of balancing risk control with practicality, especially when traditional documentation and validation processes may not suit the scale and complexity of modern AI systems.

Tingting She Bio

As a SVP and Head of Model Risk Management in the Bank of China New York Branch, Tingting has 15 years’ extensive experience in risk management and financial analytics, from Big-4 Consulting Firms and International Banks. She has a proven track record in establishing and leading analytical risk management functions e.g. Model Risk Management, AI Risk Management, Stress Testing etc.; Tingting is deeply passionate about leveraging analytical insights to drive organizational growth and enhance resilience in the face of challenges.