Five strategies for banks to move beyond the hype and stay ahead of the AI adoption curve
Artificial intelligence (AI) is all around us. The recent proliferation of the hype-building generative AI (Gen AI) gets us wondering: is AI democratizing the technology for all? While uncertainties abound and cautions on good AI governance should always be a top priority for any company in banking touting the broader engagement of AI. There has arrived an opportunity for small and mid-market banks to coalesce on how to embrace and harness the technology or even avert another potential catch-up game on AI adoption.
Although the banking industry is still navigating through the trial-and-error stage of Gen AI application, which promises to revolutionize how banks operate, many banks are gearing up their strategic planning about broader AI adoption in various fields including customer engagement, cost saving, and revenue generation. Small banks can embrace the current window of opportunity and leverage AI to level the playing field. As the World Economic Forum founder Klaus Schwab put it, artificial intelligence will, after all, “augment our intelligence.”
However, despite its potential, implementing Gen AI requires careful planning and execution. In recent conversations with industry leaders at forums including Bloomberg’s The Future Investor: Garnering the Power of AI and the Financial Times’ 2024 Outstanding Directors Exchange, Piermont Bank had the privilege to lead discussions on how the latest AI revolution can impact smaller banks and what actionable steps they can take.
Demystify the practical benefits of AI in banking today
As of late, Gen AI use-case scenarios in banking tend to trumpet operational efficiency, enhancing customer engagement, driving innovation, and improving data-driven decision-making. Gen AI is machine learning that creates new content based on patterns and relationships learned from existing data. Thus, other than 7/24 AI-powered automated banking, many banks use AI tools to analyze large quantities of data, conduct highly repetitive tasks, identify trends that the human eye could easily miss, enhance fraud detection, and beyond. They can also customize and simulate analytical financial reports for customers or generate creditworthiness based on applicants’ available data from a broader spectrum.
The application and quality of Gen AI are only as good as the parameters humans set for it as well as the quality of data fed to it. To drive deeper insight into market trends, customer behaviors, and economic indicators, banks should place high priority on data hygiene and thoughtful human-machine interactions.
Managing AI-related risks
As the banking industry is still exploring how to broadly and securely integrate AI, key risk factors must be taken seriously. Such risk areas include but by far not limited to data privacy and security, operational risks, data quality, regulatory compliance, ethical use, and impact on the workforce. Bank leadership must navigate the complex and ever-evolving AI landscape to maintain a competitive edge while taking control of these risk factors.
Five leadership strategies to advance banking with the AI revolution
1. Amp up AI readiness for your leadership team
AI integration is becoming a strategic necessity for corporations. A 2022 paper on AI by the Institute of Directors found that over 86% of businesses already use some form of AI without the board being aware and that 80% of boards did not have a process in place to audit their use of AI, and did not know what questions to ask. It’s time to elevate AI integration to an executive and/or board agenda and establish a clear, long-term vision for AI deployment. While allocating resources for on-going AI training across the board is essential, high level AI readiness including appropriate training for executive teams and board members can help ensure that banks are taking proactive steps into the future.
2. Integrate AI adoption into your enterprise risk assessment framework
Integrating AI into enterprise risk assessment frameworks is not just beneficial but increasingly essential as it can help enhance risk detection and prediction, inform regulatory compliance and improve decision-making. Make sure your practice truly reflects the procedures already in place During examination and audit, clearly communicate any AI embedment in the process and delivery of your banking products to the regulators.
3. Invest in data infrastructure
The value that AI can deliver depends on the quality of data provided to it. A robust data infrastructure is the backbone of effective AI strategies. It ensures data quality, scalability, integration, security, and compliance, all essential for developing reliable and powerful AI systems.
For smaller banks with funding limitations, it’s wise to plan holistically but break down the infrastructural plan into digestible phases. At Piermont Bank, we learned early on that recalibration and model validation are really important. Proactive monitoring, recalibration and model validation at appropriate intervals ensure agility and course correction toward long-term goals while keeping senior management and the board on the same page.
4. Tracking incremental progress and measuring return on investment (ROI)
Measuring the ROI of AI initiatives can be complex. When quantifying ROIs, start in areas where you can track incremental metrics such as measuring efficiency gains, revenue generation, and cost-saving by comparing before and after AI deployment. Ensure that everything you do has a measurable ROI to secure internal stakeholders’ ongoing support.
5. Collaborations
AI adoption is a long and complex journey. While internal and cross-departmental collaboration is vital, external collaboration is also essential for successful AI adoption. Engaging with external partners, such as a data partner, can provide access to resources, expertise, and perspectives that are often not available internally.
As AI is increasingly a game changer in banking, now it’s an opportunity for small banks to position themselves for a leap forward and break the cycle of under-representation.