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 begin, offering innovative leaders a rare opportunity to expand into credit markets and win market share.
