Customer-centric banking. Seamless onboarding. Hyper-personalized journeys. Ecosystem banking. These aren't new ideas—but generative AI is accelerating the transition from inspiration to reality.
What makes generative AI so transformative is that it combines AI's ability to analyze inputs with the ability to create outputs, explains Isabelle Guis, Chief Marketing Officer, Temenos. For example, AI enables banks to analyze target markets but with generative AI, a bank can take that analysis to the next level and create targeted marketing content.
Much in the same way that the birth of the Internet drastically changed the way banks operate, generative AI is quickly reinventing how banks will do business. A recent global study for Temenos of more than 400 banking leaders showed more than a third of banks (36%) have already deployed or are in the process of deploying generative AI. The primary use cases for these banks currently focus on improving efficiency, elevating customer experience, and driving business growth.
It's still early stages, but banks can't wait to ask themselves a critical question: 'How can we ensure that our generative AI investments will enable us to offer innovative, trusted, and future-proof banking solutions to attract customers and ensure loyalty and fuel revenue growth?'
"Generative AI can also significantly impact both external and internal productivity, especially in customer onboarding."
— Isabelle Guis,
Chief Marketing Officer, Temenos
"Generative AI will drive seamless, personalized and efficient customer banking experiences across all channels," says Guis. "AI is evolving is so fast that if you delay your investment and experimentation, it will be hard to catch up. You may end up losing your customers to more AI-savvy banks."
Banks have deployed generative AI in some areas but haven't yet industrialized generative AI in their organizations. Lack of standardization and clear, consistent processes increases challenges around oversight, governance, and privacy. In addition, uneven knowledge about generative AI in the C-suite and the board room slows down AI execution.
Temenos's study showed that although they are investing in generative AI, most banks (86%) have data protection concerns. In addition, over half of banks cite concerns with legal requirements (60%) and generative AI providing inaccurate results (59%).
There's still discussion on how to create a framework for use. Should generative AI be implemented as small, discrete projects, or will generative AI require rethinking the entire business? These are big questions that banks shouldn't wait to consider.
The Human/Generative AI Partnership
Since revenue growth is intrinsically linked to customer loyalty, customer experience has bubbled up to become one of the most compelling use cases for generative AI, whether that experience is digital or human-to-human. While generative AI won't replace human agents, says Guis, banks will use generative AI to handle simple customer interactions and make small decisions with increasingly human-like digital interaction. Generative AI also provides human agents with faster access to customer information, equipping them to make impactful decisions that are meaningful to customers. For example, generative AI can help agents analyze customer data so they can confidently offer personalized products and services.
"Agents will become more specialized with generative AI," explains Guis. "It will completely change the concept of customer experience."
"Generative AI can also significantly impact both external and internal productivity, especially in customer onboarding," adds Guis. "Generative AI automates onboarding with more advanced rules yet leaves complex onboarding to the agents."
The Road to Generative AI
To leverage generative AI, banks need to focus on both data and modernization. Since generative AI is data hungry, banks are looking to solutions such as data hubs that provide real-time visibility into customer data across systems.
The goal is to launch innovative banking products faster to take advantage of new revenue streams and market opportunities.
On the modernization front, banks must embrace open APIs and cloud integration to support rapid implementations within a large partner ecosystem. A modular core solution that can use customizable, out-of-the box banking solutions can seamlessly integrate and modernize technology stacks. The goal is to launch innovative banking products faster to take advantage of new revenue streams and market opportunities.
Modernization comes in several flavors: best-of-suite and best-of-breed. A best-of-suite strategy, in which a single solution provider manages almost the entire technology infrastructure, is popular with tier 3 and smaller banks. Having a best-of-suite solution enables these banks to work within their budget and resource restraints yet retain their front end and focus on new services or products.
Tier 1 and tier 2 banks often favor a best-of-breed approach. While these banks typically have the large IT teams and the associated budgets needed to manage their own technology infrastructure, they may need expertise or a point solution to enter a foreign market quickly or launch a payment module.
Beyond Banking Norms
Generative AI enables banks to reinvent banking norms. It's no longer business-as-usual and banks must explore what generative AI can do for customers as well as prioritize processes that make their business more efficient.
Guis notes that it will take a few years before financial institutions and governments understand how to implement and regulate generative AI and to fully leverage its value.
Generative AI is gaining momentum and is increasingly viewed as a competitive advantage, especially in customer experience, productivity, risk, and fraud "The longer you wait, the more difficult it will be to catch up," warns Guis.
