Driving new banking revenue streams through generative AI

Driving new banking revenue streams through generative AI

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By Andy Schmidt, Global Banking Lead at CGI

For many years, banks have been using advanced analytics, machine learning (ML), and artificial intelligence (AI) to drive operational and performance improvements across a wide range of business areas. Through AI, banks can gain greater insights and efficiencies, and can provide a more personalised customer experience by scanning and analysing massive quantities of data, automating more functions and delivering innovative products and services, along with a better customer experience.

While banks have been using various forms of AI, including predictive analytics and chatbots, generative AI (GenAI) is now the focus—the new kid on the block, so to speak—and banks are trying to figure out how best to use it. This has led to some debate around GenAI; is it a silver bullet for banks or just another tool in the toolbox?  

In helping banks responsibly leverage AI, we’ve come to view GenAI as a tool versus a cure-all. However, it’s a very powerful tool, and, because it’s so powerful, great care is required in leveraging it. Further, GenAI can do more than just improve existing business functions; it can, and should, drive new revenue streams for banks. CGI’s responsible use of AI framework helps ensure that the solutions are transparent, reliable, secure, safe and robust.

The move toward GenAI

Because of its powerful capabilities, most banks are investing in GenAI. They’re running GenAI proof-of-concept experiments or pilot projects to get a sense of how the technology works and how it can be used to generate trusted outcomes responsibly.

One bank, for example, is using GenAI to save $150 million (£120m) annually through improved fraud detection and prevention. Other banks are using GenAI to improve the customer experience by, for example, routing more contact center and customer inquiries to chatbots (in turn, freeing up human agents to handle more complex inquiries that require human sensitivity and empathy). Other uses include code modernisation (migrating legacy code to newer technologies), generating meta-data and technical documentation, providing expert support for agents, improving the migration of data to the cloud, and enabling customers to access bank products and services more easily.

Banks are applying GenAI to areas they know and understand for the purpose of improving what they’re already doing. From a risk management standpoint, when implementing a new technology, this is a wise move. However, these initial opportunities are only the beginning of what GenAI can do. GenAI can improve not only what banks are doing now, it can also enable them to transform areas essential to their profitable growth and survival, such as new revenue generation (see our related white paper on embedded finance).

As more non-banks move into the banking arena, competition continues to intensify around traditional banking services. To combat this, an increasing number of banks are moving beyond the traditional banking market by embedding their services into the workflows of other businesses (e.g., via purchasing and financing processes, aka embedded finance). As banks use GenAI to both compete with non-banks and expand their markets, they can, at the same time, drive new efficiencies, new products and services, and innovative customer experiences. The overall result is improved customer acquisition and retention and, of course, increased revenue.

Challenges in doing more with GenAI

Shifting the focus of GenAI from operational and performance improvements to new revenue streams comes with challenges. First, it requires imagination and investment. Banks must imagine the possibilities and invest in identifying, prototyping, and refining new capabilities and offerings.

As part of this work, risks must be assessed through automated simulations, human testing and maintaining humans in the loop with AI-driven decision-making to ensure AI parameters are, and remain, correct. Further, this work requires addressing “ghosts in the machine” (i.e., unintended consequences that result from going beyond a system’s intended use as GenAI is applied).

Pursuing this level of creativity and innovation in a free-form way is not common to regulatory-focused and risk-averse industries like banking, but it’s essential for maximising the potential of GenAI. Leveraging best practices and setting clear guardrails enables the ability to explore the new AI technologies in a secure and reliable way. For example, responsible use of AI frameworks can and should be built into the evaluation of these use cases. (To learn more, check out this blog from my colleague Dr. Diane Gutiw: Embracing responsible AI in the move from automation to creation and Guardrails for data protection in the age of GenAI.)

Another challenge is funding. It’s not easy to ask for money to fund a fishing expedition, especially when you’re not 100% sure of what you’re fishing for and where, as well as what the payoff will be. In building a GenAI business case, it’s important to set appropriate boundaries, so that you don’t end up exploring the entire ocean, as well as to define clear ROI objectives and metrics so that you can gauge and capitalise on your success.

Speed also is a major challenge. Banks are measured every 90 days through their quarterly reports, so there’s pressure for GenAI experiments to pay off quickly. In addition, banks face cost containment and risk mitigation challenges. Decreased costs for computing power combined with greater cloud availability make it easier for banks to harness large language models to identify new opportunities. However, this harnessing effort can lead to increased costs and risks.

Recommendations for driving new revenue streams

How can banks address these challenges and effectively use GenAI to generate new revenue? Here are a few recommendations based on our GenAI work with banks:

  1. Look beyond your operations in exploring new revenue opportunities.

Success with GenAI requires getting off the beaten path and exploring new territories. This is especially true for banking, given how slowly the market changes and how quickly new entrants emerge. Responsibly designed and implemented GenAI enables new market exploration through its powerful data and automation capabilities. By looking beyond your operations, discovering new horizons, and analysing, with GenAI, the lay of the land, you can find new ways to expand your markets and offerings and create new revenue streams.

  1. Establish thoughtful and meaningful boundaries in terms of market opportunities and time so that you can execute quickly and cost-effectively.

Once you identify market opportunities to pursue with GenAI, identify which elements of those opportunities you want to pursue and the amount of time you want to spend pursuing them. For example, do you want to pursue a specific element that is well-defined, or does your GenAI strategy call for a broader approach?  Targeted efforts typically have shorter and clearer feedback loops than all-encompassing strategies, increasing your chances for success in less time. As GenAI develops, these exercises can be accelerated by leveraging AI to simulate penetration rates and sales cycles to provide your teams with a better indication of how successful your efforts will be before the project even begins.

  1. Fail fast, roll forward.

Learn from each iteration of your GenAI execution, refine the prompts used to interact with the solutions and apply lessons learned to the next iteration. For example, make sure search parameters are better defined and more tightly integrated with each iteration, and therefore ultimately more achievable.

It’s all about the margins

While there are different margins that measure a bank’s success, the stock market cares most about profit margins. A bank can improve its profit margin by decreasing costs and/or increasing revenue. GenAI enables it to do both—and in a manner that considers and couples risk avoidance and business outcomes. While many banks are focused on driving cost-efficiencies through GenAI right now, using the technology to increase revenue is an untapped opportunity.

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