Wednesday, August 22, 2018

2017 & 2018 have seen more banks implementing and/or running pilot projects to deploy Machine Learning techniques in their credit risk management and modeling. While their tools and models have been around for many years, recent increases in computing power and data storage have catalyzed the expanded opportunities for application.

In Episode 3 of FRT, we explore the key findings from the IIF study, in which we interviewed 60 firms (58 banks and 2 mortgage insurers) on their adoption or exploration of machine learning. We discuss the specific techniques applied, key areas of application (such as credit decisioning and the monitoring of deteriorating credits), the benefits yields, and the major challenges and barriers encountered.

IIF Authors

Brad Carr

Senior Director, Digital Finance Regulation and Policy

Natalia Bailey

Associate Policy Advisor

Michael Kueker

Associate Policy Advisor
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