Is it Safe for Financial Organizations to Rely on AI? Does it Matter?

The use of generative AI apps in banking, investment, and financial planning organizations has surged, reflecting the industry’s push toward automation, efficiency, and personalized services. In my opinion (and that of most experts in the field), the explosion of generative AI is one of the most disruptive and powerful opportunities to impact the finserv industry in decades. It’s right up there with the maturation of the Internet, and may eventually even surpass that.

Yet, I’m finding that many financial institutions are holding back on investing in this incredible technology. (The same sort of hesitance kept many banks and investment firms from embracing Agile development techniques in years past, while their competitors pivoted and gained market share as a result.) And, while I understand there’s reason for caution and strong governance, I think hesitation now can spell competitive disaster in a shockingly short time.

Here’s what I’ve learned from both research and personal experience as CEO of Cprime, a tech and transformation consultancy that’s worked with more than half of the Fortune 1000 over the past two decades. Look it over, finserv leaders, and tell me what you think.

Why are companies investing in generative AI?

Generative AI offers several benefits to financial institutions. Companies are leveraging these tools to process and extract valuable information from large volumes of financial documents,  generate realistic financial scenarios, assist with loan servicing issues, and create highly tailored financial advice. Furthermore, generative AI is being used to manage risk, improve credit scoring, and even detect and prevent fraud. 

You’ve heard this before. It’s not new information.

So, the question is: how are these opportunities panning out? Is it worth the investment? 

Pros and cons

The advantages of using generative AI in the financial sector mirror, in many ways, the benefits of embracing Agile principles—enhanced efficiency, improved decision-making, greater customer satisfaction—while adding the ability to provide personalized financial services, to automate time-intensive busy work, and leverage big data better than ever before. There’s no way to overstate the proven and potential value of these benefits. And we’re really just learning what generative AI can do in this regard. As capabilities mature and use cases evolve, we can imagine these pros only getting better, and new opportunities emerging. 

However, there are also downsides. These include the substantial investment required for implementation, the need for expertise in managing these tools, and potential issues around data privacy and security.

This last one is especially important, since the other two are hurdles a committed finserv organization can take on fairly easily.

Security and compliance risks

While generative AI holds much promise, it also raises legitimate concerns about data security, privacy, and governance. Financial organizations must ensure robust security measures are in place and that AI systems comply with all relevant regulations. 

While many governments and regulators have established basic rules around the fact that organizations need to maintain security and privacy, they haven’t done much to explain how to do so. Financial institutions are largely left on their own to figure that out as they go. And, with the AI landscape changing so incredibly fast, that’s a difficult task to say the least.

Promising you the moon could be slowing things down

At this point, action is paramount. But, unfortunately, independent software vendors are flocking to finserv and making a lot of claims they’re not really able to back up with solutions that are still very much in flux. We saw the same thing happen in the Agile realm years ago (and it still happens today). What it does is slow down progress rather than speeding it up. At a time when finserv organizations need to be forging ahead confidently, they’re getting bogged down in analysis paralysis, half-formed tools, and misaligned strategies.

But real help is available

That’s one of the main reasons so many large banks and investment firms have reached out to global consultancies to help guide their overall digital, Agile, and AI transformations. There’s simply too much at stake if they get it wrong, and yet, there’s just as much danger in failing to act.

So, what do you think? 

• Are you currently pursuing a generative AI strategy in your organization? 
• If so, how aggressively? How’s it working out so far? 
• If not, why not?