There are many applications that can help public agencies improve outcomes and streamline processes to make the best use of limited resources. New technologies, including AI and ML do come with challenges, and understanding these risks, including bias, is crucial for organizations to increase positive outcomes.
In the decade since US banking regulators published SR 11-7, model-risk management development has continued to evolve, challenged even more so by the COVID-19 pandemic. Six best practices may help banks to rebuild better after the crisis and avoid undesirable trade-offs between cost, timelines and quality.
Given the complexities of the global marketplace, it is critical that FIs improve the management of their model life cycle to improve efficiencies and controls. By taking a more integrated, strategic approach to the management of the model life cycle, banks can unlock massive model development and validation potential.
Traditional nowcasting has served its purpose well, but the COVID-19 crisis proved a challenge for typical nowcasting models. Today’s next-generation nowcasting approach reduces the number of variables for more accurate outcomes and making it easier to interpret estimates, understand structural breaks, and provide up-to-the-moment information.
Organizations must adopt concrete, dynamic frameworks to manage AI risks. This pinpointing, prioritization and management of AI risks now should be part of a holistic, long-term strategy to create value for the future.
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.
