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How Generative AI and open banking are redefining personalization in financial services with Curinos’ Olly Downs

open banking holly downs

Generative AI and open banking are beginning to change how banks engage with customers. Today we will look at this process with Olly Downs. He is a Chief Technology and AI Officer at Curinos. With a career spanning three waves of AI, Downs brings a wealth of experience to the table. He published his first academic paper on what we now call generative AI, back in 1999. “I’ve almost been waiting for the current wave of AI to join us,” Downs reflects. He highlights the long-anticipated arrival of today’s AI capabilities.

AI-driven personalization will change digital banking. Banks are beginning to use it to recreate the personalized touch of traditional banking. Downs explains, “Traditional banking founded itself on personalized, high-engagement relationships. That followed families and businesses throughout their entire life cycle.” Personalizing the online experience is challenging due to the growth of digital channels. Curinos’ technology tackles this by analyzing customer journeys. It identifies the best times and ways to engage customers. This ensures that personalization continues in the digital space. The result is a more effective and tailored customer experience.

Generative AI is not just boosting personalization. It addresses the entire marketing cycle for banks. This shift is redefining how banks approach customer engagement. It’s enabling and testing tailored interactions with numerous ready-to-use marketing creatives. The impact is both profound and widespread. The blend of personalization with open banking is shaping the future of banking. 

1. Evolution of AI in Banking Personalization

Downs traces AI’s progress in banking, from Microsoft Research to today’s generative AI. He notes, “We’ve done so much better in understanding language. And the human internalization of concepts.” This progress has deepened our understanding of customer behavior across different communication channels. It provides a clearer picture of how customers interact, enabling banks to create more personalized experiences. Banks nowadays are focusing on data-driven customer lifecycle management.

2. Bridging the Gap Between Traditional and Digital Banking

Modern banks want to replicate the personalized touch of traditional banking online. This is a major challenge in the digital age. “The most satisfied retail banking customers engage with a branch. As well as digital services,” Downs says. This insight highlights the need for a consistent experience across all channels. AI helps unify customer journeys. It offers context for both digital and in-person interactions. Achieving this consistency is crucial for a seamless customer experience.

3. Generative AI: A Game-Changer for Financial Services Marketing

Generative AI addresses the marketing process for banks. Downs reveals, “We’ve been able to stitch in with the help of generative AI… how can we be experimenting live?” This technology allows for real-time learning and adaptation of marketing strategies. It accelerates the creative process and campaign execution.

4. Future of Open Banking and Personalization

Looking ahead, Downs contemplates the convergence of personalization and open banking. He muses, “There’s an opportunity for thinking about… pricing and packaging, both of deposit and lending products that can become very personal.” Yet, he also notes the potential challenges in data consolidation open banking might present, suggesting a need for consumer-driven solutions.

5. Micro-Personalization: The Next Frontier

The conversation touches on the concept of micro-personalization. It means “personalization for an audience of one.” The goal of personalized banking is to integrate both branch and digital services. Downs notes that open banking trends and data privacy issues make this complex. These challenges make personalization more difficult.

The Big Ideas

  1. AI-driven personalization is reviving traditional banking relationships. Downs highlights, “Traditional banking founded itself on personalized, high-engagement relationships.” He explains how AI is enabling banks to maintain this level of personalization. It is doing this across digital channels.

  2. Generative AI will change financial services marketing. Downs reveals, “It’s a massive unlock. It’s a hundred X unlock of the creative process in particular.” This technology allows for continuous experimentation and rapid adaptation of marketing strategies.

  3. The future of banking lies in the convergence of personalization and open banking. Downs predicts a future where banking products are highly personalized, stating, “There’s an opportunity for thinking about… pricing and packaging, both of deposit and lending products that can become very personal.” Yet, he also acknowledges the challenges that it might present in data consolidation.

  4. Customer engagement is key to long-term value. Downs explains, “The key use case has been about engagement and the path to primacy and maximizing quality of customers.”
  5. AI is enabling real-time learning and adaptation. Downs describes how Curinos technology can “generate new recommended creatives”. It does so in that “flow for the marketing team.” This allows for the immediate implementation of insights gained from customer interactions.

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The evolving role of Chief Data Officers and Generative AI in financial services with Jay Como and Glenn Kurban

T Rowe Price and Capco on the Tearsheet Podcast about data governance

In today’s episode, we explore what it takes to build world-class data governance in financial services. Our guests are Jay Como, the global head of data governance at T. Rowe Price, and Glenn Kurban, a partner at Capco.

We talk about how generative AI and new data strategies are transforming finance, sharing insights on the transformation of the Chief Data Officer role.

The discussion also focuses on the challenges of large-scale data migrations. Jay Como reflects on the convergence of data and digital roles. He states, “What we’ve seen is there used to be kind of two shapes of CDOs. There was a chief data officer and there was a chief digital officer. And what I think in the last five years is what we’ve seen is those roles have really come together.”

Glenn Kurban adds depth to this perspective, emphasizing the shift towards more proactive data strategies. Glenn says, “You’re seeing much more being asked of CDOs in terms of, how are we moving now to an offensive posture around data? That is, how am I going to monetize this data? How can I use it to drive better decisions, reduce costs, and actually outpace our competitors?”

As our discussion unfolds, it becomes clear that the financial services industry is at a pivotal moment.  AI tools and cloud technologies are reshaping traditional approaches to data governance and migration. The insights shared by Como and Kurban offer a glimpse into the future of data management in finance. AI-driven solutions and strategic data governance converge to create new opportunities and challenges.

Evolving Role of Chief Data Officer

The conversation begins with a deep dive into how the role of Chief Data Officer has transformed over the years. Jay Como explains that the position has expanded beyond its initial focus on analytics and regulatory compliance. “There’s still those separate titles, but you can’t be an effective chief data officer if you’re not fantastic at digitization and vice versa,” Como highlights the merging of data and digital roles.

Glenn Kurban says that CDOs are now expected to be more proactive. “You’re seeing much more being asked of CDOs in terms of, you know, how are we moving now to an offensive posture around data,” he says. This shift includes strategies for data monetization and improved decision-making processes.

The Impact of Generative AI on Data Governance

Generative AI is transforming data governance practices in financial services. Jay Como states: “A year ago, June, I was in a data conference and the individual speaking said, who has gen AI in their data governance programs and literally no hands with and eventually inspired me to write a white paper on it because I thought it was a great opportunity. A year later, same conference. That’s all we talked about.”

The discussion reveals how AI tools are being leveraged for various aspects of data governance, including:

  • Identifying and redacting Personal Identifying Information (PII).
  • Generating systematic data quality rules.
  • Creating robust data lineage for compliance and copyright infringement prevention.

Data Migration Challenges and AI Solutions

The podcast delves into the challenges of large-scale data migrations. These may be particularly from legacy systems to the cloud. Glenn Kurban highlights the creative use of AI in this context. He says, “We’ve got programs with clients right now that are woefully behind because you can’t find, you know, the legacy engineers to actually tell you what the old code was doing. And so we’re having AI do it for us.”

This application of AI to reverse engineer and translate legacy code demonstrates the potential for AI tools to streamline complex migration processes. It helps to overcome historical challenges in data management.

Future of Data Management in Financial Services

Looking ahead, both experts share insights on emerging trends that will shape data management in the financial sector:

Jay Como expresses excitement about the improving capabilities of cloud providers. Glenn Kurban predicts the rise of AI-powered data catalogs and marketplaces. This makes it easier for users to find and access relevant data within organizations. Kurban also envisions a future where AI reduces the manual effort in data migrations. He states, “I think we’re moving into an era now where if we think about, you know, when we got a new phone or a new laptop 20 years ago, you’re You’re, you know, slogging data from one hard drive to the next and moving with car, you know, memory cards and so on and so forth. And now it’s very much a push-button operation.”

The Big Ideas:

  1. Jay Como focuses on the convergence of data and digital roles. He observes, “There used to be kind of two shapes of CDOs. There was a chief data officer and there was a chief digital officer. And what I think in the last five years is what we’ve seen is those roles have really come together.” This convergence reflects the increasing importance of data in driving digital transformation initiatives.
  1. Shift to offensive data strategies emphasizes the growing focus on data monetization and strategic use of data assets to drive business value. Glenn Kurban notes, “You’re seeing much more being asked of CDOs in the terms of, you know, how are we moving now to an offensive posture around data?”
  1. The rapid adoption of generative AI in data governance underscores the transformative potential of AI in data management. Jay Como highlights the speed of change: “A year ago, June, I was in a data conference and the individual speaking said, who has gen AI in their data governance programs and literally no hands with… A year later, same conference. That’s all we talked about.” 
  1. The use of AI to interpret and migrate legacy systems represents a significant advancement in data migration strategies. Glenn Kurban shares an innovative application of AI: “We’ve got programs with clients right now that are woefully behind because you can’t find, you know, the legacy engineers to actually tell you what the old code was doing. And so we’re having AI do it for us.”
  1. The evolution of cloud services for data management has improved the capabilities of cloud providers in supporting sophisticated data management needs. Jay Como expresses optimism about cloud technologies: “I’m really, really excited now. And I’ve been a skeptic for years and now I’m, I’m kind of drinking the Kool Aid and I think it’s going to taste really good in the future.”

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