Natalia Bailey is an Associate Policy Advisor in the Digital Finance Regulation and Policy team at the IIF. Natalia has worked on the IIF Report on Machine Learning on Credit Risk, analyzing 60 banks’ usage of machine learning techniques for credit risk, evaluating banks’ experiences, applications, motivations and challenges encountered. More recently, Natalia’s work has focused on investigating the ‘explainability’ or so-called black box challenge, as well as collaborating on analyzing the adoption of these techniques in other areas such as AML, and fraud detection.
Natalia previous work has been on the IIF RWA Task Force (IRTF), analyzing banks’ credit risk modelling practices, and focusing on the importance of risk sensitivity of the regulatory capital framework, RWA and credit risk issues. In her capacity, she reviewed the modelling practices in banks’ internal RWA models, and helped develop a multi-pronged approach to enhance internal model based capital approaches. This work has culminated in the publication of a number of reports, the most important of which is named ‘The IIF RWA Task Force Final Report’ and was published in November 2014.
Natalia has also led the IIF’s submissions on Downturn LGD modeling, Point-in-time versus Through-the-cycle modelling approaches, and Sovereign Risk.
Natalia holds a Master of Public Policy from George Mason University, and a Bachelor’s degree in Economics from Hollins University, where she attended as a recipient of a IIE-Fulbright Scholarship.