Demystifying and Deploying AI to Level the Playing Field in Banking

Five strategies for small and mid-market banks to move beyond the hype, and stay ahead of the AI adoption curve

When it comes to the benefits of deploying Artificial Intelligence (AI) in banking, the hype is real, and the potential is limitless. While some uncertainties around its ultimate impact are not fully resolved and cautions on good governance should always be a top priority for any financial institution, the broader adoption of AI presents an exciting and undeniable opportunity for small and mid-market banks to stay ahead of the adoption curve and compete with big banks.

The banking industry as a whole is still navigating through the trial-and-error stage of Generative AI (Gen AI) application, which promises to revolutionize how banks operate, but it’s also true that many banks are already 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 to outpace their bigger and slower to adopt competitors by leveraging AI to level the playing field.

However, despite its vast 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 led discussions on how the latest AI revolution can impact smaller banks and what actionable steps they can take to gain a significant competitive edge in the marketplace.

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 24/7 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. These AI tools 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, small to mid-market 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 integrate AI broadly and securely, key risk factors must be taken seriously, including but by far not limited to, data privacy and security, operational risks, data quality, regulatory compliance, ethical use, and impact on the workforce. Small to mid-market bank leadership must navigate the complex and ever-evolving AI landscape to maintain a competitive edge while taking control of these risk factors.

With that in mind, here are five leadership strategies to advance banking by leveraging 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 percent of businesses already use some form of AI without the board being aware and that 80 percent 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

As mentioned above, 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 our bank, we learned early on that recalibration and model validation are 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, it presents an unprecedented opportunity for small banks to position themselves for a leap forward and break the cycle of under-representation across the financial services industry.