Understanding the generative AI industrial revolution

AI innovation is accelerating – with implications for financial services and payments

Generative AI is reshaping our world. In what felt like a blink of an eye, ChatGPT has become a regular part of everyday conversation. From high school teachers and software developers to corporate legal teams, everyone is grappling with the implications of chatbots that can instantly write term papers, create photorealistic images and generate software code.

What does it all mean? What are the risks? And how can we keep up with the pace of change?

At our Forum client conference earlier this year, Fiserv Senior Vice President of Corporate Communications Britt Zarling sat down with Senior Vice President of Digital Transformation Tom Eck to explore those questions, and the implications for financial services and payments experiences.

Understanding the current hype

“AI is one of those overnight success stories that took 70 years,” said Eck, whose own experience with AI spans three decades across biotech drug discovery to enterprise software.

From the 1950s when the term “artificial intelligence” was first coined to today’s explosion in generative AI, there have been numerous advances and lulls in AI research. One of the last big moments for AI occurred in 2011 when IBM’s Watson won the Jeopardy Challenge.

What’s behind today’s renaissance in AI? There are many converging trends at work, but you can thank video games for a major contribution. Hardware advances in graphical processing units, originally intended for high-end gaming, are adept at the parallel processing requirements needed for AI. Together with massive internet data sets and new software architectures for creating AI models, the boom is here.

"In the 30 years I’ve been at this, I have never seen such an explosive and exponential growth in the power of these techniques,” said Eck. “ChatGPT reached 100 million active users in just 2 months. That’s crazy. By comparison, TikTok took 9 months and credit cards took 15 years.”

Applications for fintech and payments

When it comes to the direct consumer experience, chatbots for customer service and self-service are obvious areas where generative AI can dramatically improve the intelligence, speed and consistency of helping consumers, said Eck. That’s true for most industries, if not all. Generative AI will broadly impact organizations and functions.

Financial services and payments could benefit even more acutely. Credit scoring, fraud detection and process automation have long been areas assisted by AI and machine learning, but new advances in generative AI and large language models (LLMs) can take them to the next level.

For instance, AI is enabling more effective service and fraud detection in call center operations through improved real-time sentiment analysis of the caller’s voice, tone and language. In compliance and regulatory functions, automating time-intensive reporting such as audit reports, reconciliation and initial KYC analysis to understand if a potential customer will be verifiable will shape operations in years to come.

AI tools won’t necessarily take over these roles, but they are likely to make them a lot more efficient. “I like the word that Microsoft uses: they think of these tools as co-pilots. They’re not sitting in the pilot seat. You’re still in the pilot seat, but they’re a co-pilot. And quite frankly, they’re really good,” said Eck.

What does that look like in real life? With code writing, you need to know your objectives and the right questions to ask. It also helps to have a baseline understanding of the technology. Having a perfect string of Python code is useless if you don’t know how to deploy it. Similarly, a compliance officer can generate reports faster with AI, but they still need to understand the regulations in the jurisdiction in question.

Other use cases make it easier to use existing tools, increasing productivity and speed to innovation.

Client360 is our portal to services for Fiserv solutions, which has been infused with AI. This includes a client-facing chatbot, issue resolution and internal tools for Fiserv associates to support clients and their customers.

Another example is developer productivity, according to Eck. “I have a product I created called Developer Studio that’s kind of like our catalog of APIs and documentation. I took all the data from Developer Studio and trained a model … a developer can just type in ‘what API should I call to issue a card?’ and it might come back and say, ‘There are three options'.” From there, the developer could ask for next steps through conversational chat, ask how to call that API in Java or request more information. Put simply, it makes the developer’s job faster and easier.

The data advantage

Companies that wait to move forward risk being left behind. “This is not a spectator sport,” Eck said. Establishing a centralized function for AI is a great way to start. An AI Center of Excellence can help you understand and manage your risks – whether they’re regulatory, data- and bias-related, or specific to your IP, the risks AI presents are as new and cross-functional as the opportunities. This kind of organizational structure can help you manage talent needs, plan for operational risk mitigation and chart a course for the future.

There’s a good chance you already have the most important asset needed to move forward: Data.

“Remember when the prices of storage basically went to zero, and the thinking was, don’t throw away any data because someday you might use it?” said Eck. “Today’s the day. The day has come.”