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The room for automation in the financial services industry is huge and research by Citi finds that 54% of jobs in the banking industry could be impacted by Gen AI.

Within financial services, consultative services like wealth management and mortgage brokering may be the most vulnerable to disruption by Gen AI, says Matt Britton, CEO and founder of Suzy, a market research firm.

“When you talk about the financial services – particularly the services aspect – anything that’s consultative, that’s the first place AI is going to go. Mortgage brokers, wealth managers, accountants, those are areas AI is just built to be able to disrupt,” he said on a recent Tearsheet podcast.

One major reason for this is the expense that comes with hiring human expertise in these areas, according to Britton.

“[Employees are] so expensive, especially for SMBs, and 99% of the things that they do are highly templatized. Sure, there are going to be that 1% of cases where, if someone’s selling their company, they wouldn’t want an AI lawyer. But 99% of SMB-owners are going to seek AI-driven services because it’s just cheaper, faster, and more efficient.”

Gen AI’s entry into these services is already well underway:

Tax Management: Intuit’s Gen AI financial assistant integrates across its product line, including QuickBooks and TurboTax to help customers file their taxes easily and comprehensively.
Accountancy: Fintech Lili recently deployed a Gen AI tool called Accountant AI that will help its SMB customers with finding out answers to common accounting-related questions, as well as other tasks like budgeting.
Insurance: Lemonade has created bots that create custom policies and help with claims processing.
Investing: Public’s Gen AI powered assistant Alpha provides market trends, answers questions, and assists its users to do investment research. It’s set to become a major part of the firm’s strategy for the future, according to its CEO, Leif Abraham: “Currently Alpha, our AI assistant, is solely used to provide insights into the markets, public companies, and other assets. In the future, Alpha will expand to help people manage their portfolio. Moving Alpha from an assistant that gives context and information, to an assistant that can take action. This next phase is about integrating Alpha into that experience.”

Traditional FIs, on the other hand, have yet to take on a Gen AI strategy that centers around customer-facing products. And while most banks are steering clear of using AI assistants powered by Gen AI, they are more open to using it in the back office to help make their current employees and teams more productive.

Banks are using Gen AI to boost productivity

In July, JPMC introduced a new Gen AI powered tool to its Asset & Wealth Management team which the bank said could perform the tasks of a typical research analyst. The bank is gradually exposing more and more of its workforce to the tool, and an internal memo shows it’s encouraging its employees to use the tool for tasks like “writing, generating ideas, solving problems using Excel, [and] summarizing documents.”

Morgan Stanley has also launched its AI tool called Morgan Stanley Debrief, which helps financial advisors with creating notes on a meeting with a client.

Using Gen AI to increase productivity rather than build new products is a quintessential bank move. But apart from the obvious reasons like regulations and uncertainty, there may be another reason why banks are not moving faster with deploying Gen AI in client facing interactions.

Older folks aren’t keen on Gen AI

Suzy’s research shows that younger consumers are a lot more comfortable with using AI for financial planning and optimization than older consumers.

A horizontal bar chart titled "How comfortable would you be relying solely on AI for financial planning and optimization?" displays responses from three groups: the total sample (NET), ages 25-44, and ages 45+. Comfort is ranked on a scale from 1 (Not Comfortable At All) to 7 (Very Comfortable). The chart shows that individuals aged 45+ lean significantly towards lower comfort levels (1-3), while the 25-44 age group has a more balanced distribution, with higher levels of comfort (5-7) compared to older adults. — Source: Navigating the Financial Landscape: Predictions and Insights for Financial Institutions and Brands

The trend repeats when consumers are asked which financial tasks like tax management, mortgage brokering, and wealth management do AI perform better than humans. Close to 60% of older consumers report feeling that AI is not better than humans at any of these tasks, according to Suzy’s research.

A vertical bar chart titled "Which of the following tasks do you believe AI performs better than a human advisor?" compares responses from three groups: the total sample (NET), ages 25-44, and ages 45+. Categories include Financial Planning, None of the Above, Tax Return Optimization, Wealth Management, and Mortgage Brokering. The chart reveals that the 45+ group overwhelmingly selected "None of the Above," indicating skepticism about AI outperforming humans. Meanwhile, the 25-44 group shows higher belief in AI's capabilities, particularly in Financial Planning and Tax Return Optimization. — Source: Navigating the Financial Landscape: Predictions and Insights for Financial Institutions and Brands

The fact that a majority of older consumers don’t feel comfortable with AI nor trust the ability of Gen AI-powered tools to perform well in the areas mentioned is a problem for banks. In the US, 50% of the local banking revenue is generated by people who are fifty years or older, according to data.

The challenge for banks is clear: they must navigate a delicate balancing act between meeting the needs of their current, older customer base while preparing for a future shaped by younger, tech-savvy consumers who are far more open to AI-driven solutions. To stay competitive, traditional financial institutions will need to move Gen AI to the front of the office, and find a way to collaborate with fintechs and co-create what Gen AI powered products will look like.

If you want to read more about how AI is changing the role of banks, download this guide.

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:

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.

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?”

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.”

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.”

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.”

Listen to the full episode