The firm’s latest resource explains for IT and business leaders how Gen AI is reshaping the banking industry by optimizing operations, personalizing services, and bolstering risk management for enhanced efficiency and customer satisfaction in the digital age.
In response to the swift advancement of generative AI (Gen AI), banking institutions are compelled to reassess their policies and the use of these technologies. With their competitors launching AI-powered services and products, banks are eager to follow suit but often grapple with where to start. Recognizing this challenge, Info-Tech Research Group has launched a pivotal research resource, Generative AI Use Case Library for the Retail Banking Industry. The blueprint, which contains real-world use cases, is designed to aid IT and banking leaders in identifying valuable Gen AI use cases that can transform their organizations and ensure the implementation of responsible AI solutions that comply with regulators, deliver value to stakeholders, and provide an exceptional experience for customers’ evolving needs.
“The use of advanced modeling, including machine learning (ML) and artificial intelligence (AI), is not new in the retail banking industry,” says David Tomljenovic, principal research director at Info-Tech Research Group. “However, the rapid acceleration of generative AI is causing banks to reevaluate their policies and use of these technologies. The explosion of new tools and capabilities is reshaping the landscape almost on a daily basis.”
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Gen AI, a distinct subset of machine learning, is capable of producing new outputs based on given prompts and the data it has been trained on. Although this technology has existed for over a decade, it’s currently gaining significant attention from high-level management and board members due to its prevalent media coverage and the rapid expansion of the AI ecosystem. The unique modalities offered by a Gen AI platform, contingent on its foundational model, allow for diverse use case applications. In today’s landscape, especially within the banking sector, IT teams are under immense pressure to rapidly integrate this transformative technology.
“The banking industry has several regulatory, privacy, and fiduciary restrictions in place that make the use of Gen AI more complicated,” explains Tomljenovic. “The strict requirements of having clear and audit-compliant processes in place sets a high bar that limits broader-based adoption of Gen AI in banking.”
While the adoption of Gen AI in retail banking may pose certain challenges, it also enables an array of significant new opportunities. According to Info-Tech’s research, AI has the potential to optimize and enhance numerous facets of banking, including:
Financial Education: Customers making major decisions such as purchasing a home or planning for retirement spend considerable time researching and educating themselves. Conversational AI provides 24/7/365 human-like engagement that is well-suited to help customers make the right decisions. This is an ideal opportunity for the bank to collect critical information about customers to create custom offers.
Complex Product Selection: A conversational AI-based selection process can accelerate product selection, drive better outcomes, and help the bank better understand its products/customers. Banking is characterized by increasing levels of competition and choice for customers. Products are also becoming more specialized and complex, which can be a benefit as well as a detractor for customers.
Digital Attendants: Customer experience (CX) is a key area of competition in banking. Customers expect much more from their bank than just basic financial transactions and services. AI-powered digital attendants help banks deliver highly customized products/services to their customers in a way that makes customers feel like their bank has their best interests at heart.
Customer Segmentation: Gen AI is redefining the process of customer segmentation within banks. Gen AI has a unique ability to use massive amounts of transactional data to generate new connections between its customers. Segmentation can lead to greater personalization as well as new product and service development for banks. Better and more detailed segmentation can also drive better CX.
Customer Sentiment: The ability to determine customer sentiment over time, as well as in real time, is an emerging area of CX competition among sophisticated banks. AI is used to analyze engagement histories as well as speech and language to better understand what a customer’s sentiment is. Banks are matching service reps, products, services, and offers based on sentiment, driving better customer experience.
The firm’s latest use case research stresses the importance of adopting an AI and Gen AI strategy. Without this proactive approach, the firm cautions that banks may find themselves trailing behind their competitors in terms of client-facing products and services. Moreover, early adopters of Gen AI could potentially lower their non-interest expense ratio through realized efficiencies, providing them with additional resources to reinvest in future differentiating capabilities.
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