Tom helps financial service firms manage the risks of their rapidly growing inventories of models and analytics.


Tom helps clients set up and enhance the model governance that underpins good risk management. He implements enhancements to accommodate new model types, and address regulatory feedback, and he helps streamline the model risk function to improve efficiency and effectiveness.

Tom’s most recent initiatives include developing risk management for artificial intelligence and machine learning models. He has developed approaches to performance monitoring and cost attribution to support analytics management and investment decisions.

Tom also leads large-scale model validation programs, bringing together global teams of quantitative experts to help banks and insurers assess the quality of their models, identify methodological and implementation issues, and improve performance. His work in these areas covers a range of model types including stress testing, market risk, financial crime, and the internal ratings-based approach to capital requirements for credit risk.

Tom holds a master’s degree in mathematics from the University of Cambridge, and is a Fellow of the Institute of Actuaries.