AI continues to make waves with seemingly limitless possibilities and bold promises. However, AI also introduces new risks, and in this highly regulated industry, there is simply too much on the line to engage in trial-and-error experimentation. Generative AI has the potential to jeopardize security, customer privacy and operational resilience. Plus, if information is shared by AI that doesn’t align with policies and procedures (such as mistakenly approved loans), legal and regulatory issues could arise. When generative AI misguides people, national headlines are made. It is imperative for banks to avoid the mistakes that lead to that notoriety, not to mention the compliance violations that erode industry and consumer trust and are detrimental to the bottom line.
However, banks also can’t afford to be paralyzed by risk; AI offers huge opportunities, and those who do nothing will be left behind. The key is to approach AI and its rollout in a responsible way. There are several considerations and best practices banks can employ to apply a responsible AI framework that mitigates the common errors, issues and concerns of generative AI while driving innovation.
Before the AI journey even begins, a critical first step is to understand where to begin. While it may sound simple, banks across the country are so overwhelmed by potential use cases and information that they simply can’t determine the best starting point.
As banks continue to face talent shortages, rising call volumes and increasing online abandonment, frontline staff have emerged as a valuable starting point for implementing AI. It presents an opportunity to tackle these challenges, simultaneously increasing efficiencies and the employee and customer experience.
Once a starting point is established, it’s time to determine how to launch such technology in a safe, effective way. Responsible AI provides the proper guardrails, guidelines and controls to ensure secure and trustworthy interactions with customers, addressing common issues tied to traditional generative AI. This approach hinges on the technology to meet three critical requirements: It must be safe, proven and turnkey. Paying attention to these elements enables bankers to build a robust framework that supports innovation while minimizing safety, privacy, business and reputational risks.
First, responsible AI must prioritize safety. This means no information should ever leave the platform, with robust protections embedded within the infrastructure. Information should also be encrypted, facilitating private and secure data transfers with triple redundancy and high reliability. Plus, customer data should never be used to train foundational models. Instead, it should come from curated, approved datasets pertinent to financial institutions’ operations and regulatory requirements, with sensitive information automatically redacted before AI processing. Such efforts mitigate the risks associated with data breaches and unauthorized access.
Next, banks should look for purpose-built AI solutions that are easy to set up, train and use. AI brings little value if it is too complex to leverage or has a lag time before value can be reached. AI that is specifically designed for financial services — incorporating the complexities of banking and offering practical applications, workflows, content management, reports and insights tailored to the industry — is critical in evaluating AI’s usability and time to value. Plus, financial services-specific technology ensures that the AI is aligned with the unique needs and regulatory demands of the industry.
Finally, responsible AI must be proven. The noise and fanfare associated with the explosion of AI has made it difficult to separate what truly works from hype. With every solution now claiming to be “powered by AI,” banks are justifiably skeptical. Look for real solutions that deliver tangible results, such as productivity and efficiency gains, from the start. AI technologies that have a track record of success, with documented use cases and testimonials, are a safer and more beneficial investment.
With the customer experience as the new battleground for loyalty, banks simply can’t afford to miss out on the AI wave — or to approach it from anything other than a responsible framework. Implementing AI for customer support teams can boost efficiencies and drive faster, more personalized interactions while also providing valuable data for banks, which is essential for nurturing lasting customer relationships and differentiating from competitors.
By following the responsible AI framework, banks can optimize the potential of AI while maintaining the highest standards of safety, compliance and effectiveness. Prioritizing AI that is safe, proven and turnkey will allow banks to propel innovation and strengthen customer relationships without compromising security, privacy and regulatory compliance.
Jake Tyler is the AI Market Lead for Glia, a fintech focused on customer interaction technology.
