On August 28, the IIF and ISDA submitted a joint response to the Basel Committee on Banking Supervision s (BCBS) consultation on Guidelines for counterparty credit risk (CCR) management.
The August 2024 edition of the Global Regulatory Update provides targeted updates on current workstreams and events.
The June 2024 edition of the Global Regulatory Update provides targeted updates on current workstreams and events.
December's edition of the Digital Finance Download. A Monthly Update of significant developments in digital finance around the globe.
The IIF and EY 2022 Survey Report on Machine Learning Uses in Credit Risk and AML Applications details the results of our comprehensive survey on the machine learning development and implementation process within the global financial services industry.
The IIF’s Machine Learning Governance Detailed Survey Report details the results of our comprehensive survey on the end-to-end governance of the machine learning development and implementation process within the global financial services industry.
The IIF Regulatory Affairs Department is pleased to share a comprehensive summary of the key regulatory and policy discussions that took place during the IIF’s 2020 Annual Membership Meeting.
This paper articulates how policymakers and supervisors can assist in ensuring safe machine learning innovation, harnessing the benefits of these new technologies while minimizing and mitigating risks.
With the IIF Machine Learning in Credit Risk 2nd Edition Report tracking the industry’s progress with these technologies over the past year, leading contributor Paul Edwards (Scotiabank) joins IIF report authors Brad Carr and Natalia Bailey to discuss the survey’s key findings.
Following on from the IIF’s 2018 Machine Learning in Credit Risk survey, our 2nd Edition (2019) survey tracks industry progress in the adoption and implementation of these technologies.
The IIF is pleased to share our paper on "Bias and Ethical Implications in Machine Learning," the second paper on our three-part Thematic Series on issues related to Machine Learning (ML).