As markets slowly resume normal activity, a new credit cycle will being, offering innovative leaders a rare opportunity to expand into credit markets and win market share
COVID-19 has amplified the scope and use of model risk management based on advanced analytics
In a post-COVID-19 world, proactive model risk management by all lines of defense is needed now – not only to meet new regulatory expectations, but also to strengthen institutional resiliency.
The financial markets are still experiencing the aftershocks of the pandemic, with volatility remaining above long-term average rates.
The COVID-19 pandemic has revealed deep flaws in some widely used advanced analytics techniques. One of the most widely used advanced analytics techniques, machine learning, relies on historical patterns of data to predict future behavior.
Businesses across every industry will need to adopt AI in order to remain competitive in the current market but implementation can be fraught with risk.
The COVID-19 pandemic has revealed unexpected flaws in the business models that banks rely upon. How can they best address this challenge?
The impact of the global pandemic on banking business operations has uncovered some unexpected flaws in the models that institutions rely on to operate their businesses.
Banks have traditionally considered financial crime and fraud as two distinct categories of risk. But as these crimes become increasingly sophisticated, the traditional boundaries between them are becoming blurred.
Welfare economics seeks to understand the value of both the positives and negatives of technology adoption.
The European Banking Authority’s (EBA) January 2020 report on the increasing use of Big Data and Advanced Analytics (BD&AA) in the banking sector provides guidance for banks on improving controls in their BD&AA implementation.
The added risk brought on by the complexity of machine-learning models can be mitigated by making well-targeted modifications to existing validation frameworks.
As models becomes more embedded across every aspect of banks’ business operations, the drive towards an increasingly efficient and value-driven approach to Model Risk Management (MRM) is growing.
