How generative AI can help banks manage risk and compliance

Weekly Business Insights from Top Ten Business Magazines

Extractive summaries and key takeaways from the articles curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Since September 2017 | Week 339 | March 8-14, 2024

Strategy & Business Model Section | 2

How generative AI can help banks manage risk and compliance

By Rahul Agarwal et al., | McKinsey & Company | March 1, 2024

Extractive Summary of the Article | Listen

Generative AI (gen AI) is poised to become a catalyst for the next wave of productivity gains across industries, with financial services very much among them. From modeling analytics to automating manual tasks to synthesizing unstructured content, the technology is already changing how banking functions operate, including how financial institutions manage risks and stay compliant with regulations.  It’s imperative for risk and compliance functions to put guardrails around gen AI’s use in an organization. However, the tech can help the functions themselves improve efficiency and effectiveness. 

Gen AI has the potential to revolutionize the way that banks manage risks over the next three to five years. It could allow functions to move away from task-oriented activities toward partnering with business lines on strategic risk prevention and having controls at the outset in new customer journeys, often referred to as a “shift left” approach. That, in turn, would free up risk professionals to advise businesses on new product development and strategic business decisions, explore emerging risk trends and scenarios, strengthen resilience, and improve risk and control processes proactively.

These advances could lead to the creation of AI- and gen-AI-powered risk intelligence centers that serve all lines of defense (LODs): business and operations, the compliance and risk functions, and audits. Such a center would provide automated reporting, improved risk transparency, higher efficiency in risk-related decision making, and partial automation in drafting and updating policies and procedures to reflect changing regulatory requirements. It would act as a reliable and efficient source of information, enabling risk managers to make informed decisions swiftly and accurately.

Finally, gen AI could facilitate better coordination between the first and second LODs in the organization while maintaining the governance structure across all three. The improved coordination would enable enhanced monitoring and control mechanisms, thereby strengthening the organization’s risk management framework.

Of the many promising applications of gen AI for financial institutions, there’s a set of candidates that banks are exploring for a first wave of adoption: regulatory compliance, financial crime, credit risk, modeling and data analytics, cyber risk, and climate risk.

While several compelling use cases exist in which gen AI can propel productivity, prioritizing them is critical to realizing value while adopting the tech responsibly and sustainably. Three critical dimensions i.e., risk, feasibility and impact that risk leaders can assess to determine prioritization of use cases and maximize impact.

3 key takeaways from the article

1. Generative AI (gen AI) is poised to become a catalyst for the next wave of productivity gains across industries, with financial services very much among them. From modeling analytics to automating manual tasks to synthesizing unstructured content, the technology is already changing how banking functions operate, including how financial institutions manage risks and stay compliant with regulations.  It’s imperative for risk and compliance functions to put guardrails around gen AI’s use in an organization. However, the tech can help the functions themselves improve efficiency and effectiveness.
2. Of the many promising applications of gen AI for financial institutions, there’s a set of candidates that banks are exploring for a first wave of adoption: regulatory compliance, financial crime, credit risk, modeling and data analytics, cyber risk, and climate risk.
3. While several compelling use cases exist in which gen AI can propel productivity, prioritizing them is critical to realizing value while adopting the tech responsibly and sustainably.

Full Article

(Copyright lies with the publisher)

Topics:  Technology, Artificial Intelligence, Banking, Strategy, Business Model