Strategic AI in banking – From data to revenue

The essential building block

With all the fascination surrounding AI, it’s easy to lose sight of the foundation that underpins its success, and that’s data. While no one has fully solved the dilemma of too much data without enough actionable insight, the success of your AI initiatives depends on your data management prowess.

It starts with a comprehensive strategy that accounts for data sourcing, storage, quality and security. With these elements in place, you can then layer in analytics and self-service business intelligence that allows business line managers to ask questions (e.g., next-best-offer queries) and view helpful charts and narratives.

The next layer, machine learning, finds patterns across the data set to surface predictive and prescriptive insights that can be folded into automated workflows, allowing for real-time decisioning.

The final layer is agentic AI. Unlike traditional AI, which follows predefined rules and reacts to input, agentic AI is proactive and capable of independent problem-solving. Once placed atop your real-time, machine learning-powered core, it sets to work planning and executing multistep tasks on behalf of your users.

Early AI successes

AI is making a significant impact in any number of areas, with operational efficiency a leading objective. By automating routine tasks, AI reduces costly manual intervention, minimizes errors and lets you reallocate resources to growth-oriented endeavors. When this new-age automation is applied to customer service, it provides 24/7 support, handles routine inquiries and resolves issues quickly.

Considering the rate of spiraling fraud, AI holds great promise in identifying anomalies at the moment of occurrence. According to the research, three-quarters (76%) of financial institutions that use AI and machine learning to identify and prevent fraud are satisfied with the results.

If compliance is your primary concern, AI helps you adapt to the changing regulatory scene with agility. It can also strengthen the know your customer process by automating document verification and data extraction. Again, more than half (51%) of survey respondents reported improvement in compliance processes when AI tools were added to their arsenal.

If revenue generation tops your priority list, big opportunities lie in customer personalization and engagement. With AI’s ability to analyze vast amounts of data, you gain deeper insights into individual preferences and behaviors, enhancing your ability to craft customized offers and design targeted marketing campaigns. Additionally, revenue-generating strategies rely on the ability to forecast market trends, an AI strength due to its proficiency in predictive analysis.

An agentic AI experiment in action

Ultimately, the real power of AI lies in its ability not to replace human decision-making but to amplify it. In a recent FIS experiment with clients, an AI agent – call it a digital worker – was tested during the onboarding of commercial account customers. As the banker took prospects through the typical questions, the virtual agent instantly began prepopulating the necessary forms. More importantly, it was scouring data across the institution, preparing a lineup of likely next-product recommendations at lightning speed.

The experiment encompassed more than just saving the banker time, shortening the onboarding process and upselling products. It also served as a training exercise for employees, thus minimizing compliance risk and bringing new hires quickly up to speed.

In these early days, AI is primarily used internally to automate routine tasks, streamline operations and enhance employee productivity, such as in the commercial account experiment described. External customer-facing applications are emerging more cautiously due to higher risk and regulatory scrutiny. However, pilots are underway to explore hyperpersonalized onboarding and personalized financial management, a real service to your customers who face challenges with handling their finances.

Technical expertise meets innovative thinking

Like most banks, your technology wish list probably far exceeds your bandwidth and budget. Before AI, you may have been forced to postpone high-priority projects because of their longevity, complexity and cost. Enter AI, and you may be able to bring those investments to the forefront, completing essential projects in a shorter timeframe and with less capital outlay.

It’s at this point that many bankers turn a corner in their thinking. Instead of attempting to throw an AI silver bullet at every individual business problem, you begin to think in terms of a holistic change that begins with top-down executive sponsorship and governance of an AI roadmap. With this corporate support, you then begin to create a data-centric culture focused on AI literacy.

After all, AI is still in its infancy and your employees face a learning curve. By continually training and incentivizing your workforce, a mindset of making data-driven decisions becomes ingrained at every level. To this point, “The Harmony Gap” research, which focused on the financial technologies companies have invested in to alleviate tensions caused by geopolitical instability, regulatory challenges, cyberthreats and growing customer demands for seamless digital experiences, indicates that half the respondents feel their AI investment has been successful in fostering a data-driven culture (50%) and improved employee engagement (51%).

Overcoming the hurdles

As new technologies emerge, you’ll always face challenges with operational complexity, integration with legacy systems and regulatory mandates, and AI is no exception. And while cybersecurity risk is hardly new, you’ll need to be especially vigilant against attacks in which malicious actors manipulate input data to deceive the AI model.