Lead competition in banking with advanced risk management of AI
Artificial intelligence (AI) is no longer a futuristic concept—it's a game-changer that is driving personalization, efficiency and competitive advantage everywhere.
However, as highlighted in the latest research study by the IBM Institute for Business Value (IBV), "Banking in the AI era - The risk management of AI and with AI" the true challenge lies in managing the risk associated with AI deployment. Also, another key factor is harnessing its power for enhancing the risk management function.
At Sibos 2025 this September, IBM will be sharing insights about how to manage large-scale AI adoption while governing risks in the global banking sector.
Drawing from a survey of 100 risk, compliance and validation (RCV) officers across major markets, the report underscores the need for a balanced approach to scaling AI enterprise-wide. This approach also focuses on maintaining institutional stability and regulatory compliance.
AI's transformative potential in risk and compliance is significant. Banking executives pinpoint fraud detection and cybersecurity as top areas where AI delivers high business value, with 61% and 52% of executives viewing them as key opportunities. Yet the report also reveals a cautious stance: only 16% of respondents prioritize AI for credit risk and pricing.
These use cases are where real market differentiation can be generated to compete through more digitalized and personalized client engagement.
The biggest hurdles? The application of AI to Know Your Customer (KYC) and anti-money laundering (AML) processes, which are deemed the most complex by 43% of respondents. These processes involve nonstandardized data, manual reviews and escalating regulatory demands. This approach makes them resource-intensive and prone to inconsistency.
Enter agentic AI—a breakthrough centered on autonomous agents that orchestrate tasks like data collection, verification and regulatory interpretation. The agentic AI approach can slash processing times from weeks to near-real-time, boosting efficiency, transparency, and trust. For example, specialized agents work in parallel to authenticate documents and assess risks, minimizing human error and disputes.
But innovation demands robust risk management of AI itself. The report emphasizes a clear gap: the need to strengthen validation capabilities to ensure that models are accurate, ethical and compliant. Over 60% of respondents also indicate the need to invest in stress-test simulations to validate new use cases in a timely yet thoughtful manner.
46% of RCV officers also indicate a relevant gap in their ability to control risk across AI use cases. However, only 25% state that their highest risk use cases are managed with real-time risk control capabilities.
More investments are necessary. And in parallel, financial institutions need to upskill the workforce to build a cohesive approach across the enterprise. Every banker should become an "AI risk manager" through training in AI use and continuous learning mechanisms.
Which bank will lead the pack in the competitive landscape of AI transformation? The study identifies a path to success: banks with advanced AI risk management, implemented through a governance-enabled IT platform, will excel at finding business value and mitigating technological risk as artificial intelligence transforms the industry.
The theme of the Sibos 2025 conference (Frankfurt, 29 September–2 October) is “the next frontiers of global finance.” This year’s program will “address the transformative forces reshaping the financial ecosystem.” Among these forces, none is more prominent than AI.
IBM will be at SIBOS 2025—and we’d love to connect. If you’re attending, this is your opportunity to meet one-on-one with IBM subject matter experts to discuss how AI-powered risk management can give your organization a competitive edge. Don’t miss out—connect with IBM through the SIBOS mobile app today or come to see us on the show floor.