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Adopting a Use Case Mindset for Immediate ROI

This blog is excerpted from a white paper based on a panel discussion with Jim Marous of the Financial Brand, which also Josh Nash, SVP and Director of IT, Camden National Bank; Greg Spencer, Director of Financial Services at Passerelle; James McGeehan, Head of Banking and Capital Markets at Snowflake; Jason Bishop, Senior AI Solutions Consultant at Qlik; and Anil Sharma, Senior Partner Solutions Architect for Worldwide Banking at AWS. Read the first part of this series here.

To view the panel discussion, click here.

Adopting a Use Case Mindset

What is the secret to getting started with AI implementation? Start with a use case, and look at building a scalable infrastructure to build on.

James McGeehan, Head of Banking and Capital Markets at Snowflake, pointed to the myriad business challenges facing banks and credit unions today, from driving deposit growth to reducing credit risk to accelerating loan underwriting. Identifying one problem to focus on will help narrow the scope of the project, so banking leaders can focus on the right tool for the job, James said.

“We need to ask those questions first, because while this is exciting new technology, the expected productivity and efficiency and profitability will come, but we don’t want to force fit,” James said.

Working backward from the benefit helps to tie business value to technology.

“It’s the union of strategic business thinking and the technology infrastructure that provides the cost versus lift metric for efficiency and ROI that people really looking for out of technology,” James continued.

Greg Spencer, Director of Financial Services at Passerelle, added that keeping account holder experience at the forefront of decision-making places the emphasis of AI initiatives on using data as a strategic asset.

“Consumers and businesses aren’t outwardly saying, we want a bank or credit union with AI tools and competencies,” Greg said. “They want better products, services and experiences that improve how they manage their finances with you. Whether you want to put that label on it of offering personalization or hyper-personalization or not, it doesn’t really matter. At the end of the day, it’s all about the data.”

Incorporating AI into data strategy should be part of a holistic approach to data management, Greg continued.

“Rather than focusing on the experience or the particular solution that you’re looking to accomplish, look at the ability to be able to do that at scale, regardless of what the path is forward for you,” Greg said. “Look at how you’re actually going to drive the data layer for the institution going forward.

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Best Starting Use Cases for AI in Banking: Insights from Industry Experts

Focusing on business value is the best way to chart a path to AI adoption, they had a wide range of ideas for applicable first use cases, our experts agreed.

Unlock Insights from Unstructured Data

One of the first and most impactful use cases for AI in banking is mining unstructured data, such as PDFs or loan documents stored in cold storage. Greg Spencer highlighted that many banks store detailed mortgage applications that aren’t fully utilized because the data is not structured. By using secure generative AI applications to tap into these data sources, banks can identify potential assets or opportunities for deposit acquisition and retention.

"Consumers want better products, services and experiences that improve how they manage their finances with you. Whether you want to put that label on it of offering personalization or hyper-personalization or not, it doesn't really matter. At the end of the day, it’s all about the data."
Greg Spencer
Passerelle

In today’s competitive rate environment, banks can uncover hidden insights from previous mortgage applicants who might have indicated holding assets elsewhere. AI applications can sift through the details and flag applicants by institution, amount, and asset type, providing actionable insights for deposit strategies.

“Given the current rate environment, as well as deposit acquisition and retention challenges, this could prove to have immediate ROI and measurable value for the institution,” Greg said.

 

Automation and Augmentation for Operational Efficiency

For James McGeehan, AI can drive both automation and augmentation in banking, and combining these two aspects leads to greater success. James explained that AI can automate repetitive back-office tasks, allowing employees to focus on more value-added activities. A primary concern for banks is to satisfy two key stakeholders: customers and regulators. AI simplifies processes to meet regulatory requirements while maintaining excellent customer service.

A key example is using AI to augment call center representatives by analyzing unstructured voice data. AI can provide recommendations on best practices, helping call center reps cross-sell or upsell more effectively.

“If you automate a process by 50% that’s half the work in half the time, and that’s where we’re seeing a lot of the upside and profitability,” James said.

Augmenting Human Brilliance

While AI is reshaping banking operations, employee concerns about job security remain a significant challenge. Financial Brand Co-Publisher Jim Marous noted that many employees are anxious about how AI might impact their roles. However, AI should be seen as a tool to empower employees rather than replace them. By automating mundane tasks, employees can focus on strategic, creative, and customer-facing responsibilities that add value to the business. This shift can enhance job satisfaction.

“Use augmentation so people can use their minds,” Jim said. “It will actually enhance their deliverables, make them more proud of what they can do and help them focus on those things that actually bring value to the customer, to the shareholders, ato the organization and to themselves.” 

Fraud Prevention and Risk Assessment

Fraud prevention and risk assessment are natural starting points for AI in banking. Jason Bishop, Senior AI Solutions Consultant at Qlik, pointed out that the industry has a wealth of historical data on fraud and risk management, making these areas ideal for AI-driven predictive analytics. AI can analyze historical fraud patterns, allowing institutions to anticipate and mitigate potential risks before they occur.

AI-driven fraud detection systems are becoming more sophisticated, using machine learning to identify suspicious activities in real time. These systems can also optimize risk assessment processes, ensuring that financial KPIs are monitored closely. Predictive AI offers banks a forward-looking perspective on potential risks, allowing them to take proactive measures to protect both their customers and their assets.

“There’s so much good historical data in your databases that typically, you know, machine learning and applying use cases for what is likely to happen in the future,” Jason said. “I think those are low-hanging fruit, and where people can get a ton of value.”

"AI can actually enhance their deliverables, to make them more proud of what they can do and to make it so they can focus on those things that actually bring value to the customer, to the shareholders, to the organization and to themselves."
Jim Marous, The Financial Brand
Jim Marous
The Financial Brand

KYC and Personalization

Customer experience is a top priority for banks, and AI is playing a pivotal role in transforming how banks engage with their customers. Anil Sharma, Senior Partner Solutions Architect for Worldwide Banking at AWS, said generative AI can streamline processes such as Know Your Customer (KYC) checks and onboarding, reducing friction and enhancing customer satisfaction. AI-driven personalization can also improve customer engagement by tailoring services to individual needs and preferences.

In addition to improving customer interactions, AI can enhance internal knowledge management systems, helping employees access relevant information quickly and efficiently. This boosts both customer and employee satisfaction, further driving transformation within the organization.

“These are transformational use cases that create customer and employee delight,” Anil said.

Metrics Dashboards

As Camden National Bank embarks on its journey toward AI empowerment, it is targeting a simple use case with a big impact. Josh Nash, SVP and Director of IT at Camden National Bank, shared an example of how his team at Camden started their AI journey by enhancing metrics dashboards. Initially, the dashboard displayed a limited set of key metrics, but over time, AI allowed them to create dynamic dashboards that provide daily tracking across multiple systems. These dashboards now offer real-time insights into retail sales, helping branches and regions monitor progress and meet goals more effectively.

“We are going to go from crawling to produce a few metrics to being able to change the way we’re driving sales while producing other assets,” Josh said.

Would you like to add AI to your data strategy? Passerelle is currently offering complimentary, 90 minute AI Readiness Workshops. Learn more and request a workshop here.

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