The EBA recently released its much-anticipated follow-up report to its 2021 consultation on using machine learning (ML) for IRB models. The principle-based recommendations remain largely unchanged and are welcomed by the industry.
With the upcoming amendment of the Minimum Energy Efficiency Standards (England and Wales) to improve the energy performance of private rented sector homes, banks are increasingly concerned about the potential impacts on their mortgage portfolios.
In this third part of our series on AI and Machine Learning (ML) for Credit Rating Models, we look at how ML techniques can be used either exclusively or combined with traditional approaches to tackle some of the common risk-related challenges facing banks today.
Since the dawn of cryptoassets, multiple technological advancements have been made in the area including improved transaction costs and speed, increased privacy, robust security, and inflation hedging.
In the second part of the series dedicated to low default portfolios, the focus is on how to tackle two of the biggest challenges of the modelling process: target definition and probability of default calibration.
In the EBA’s latest monitoring report on the implementation of IFRS 9, the regulator outlined significant strides have been made by EU firms in upholding the spirit of the standard, however, more can be done.
The ECB’s economy-wide climate stress test was the first step in their climate roadmap. In 2022, the SSM climate stress test will follow.
Throughout our Fintegral Webinar on February 8, 2022 from 16:00-17:15 CET,...
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