Major Financial Services Firms Pursue All-of-the-Above Strategy for Artificial Intelligence

Nasdaq IR Insight, built on Amazon Web Services infrastructure, is an investor relations platform that uses AI to automatically analyze tens of thousands of documents disclosed by companies, saving IR professionals countless hours they would otherwise spend poring over them. At the same time, the company is equipping employees with AI-powered tools to improve engineering efficiency, sales enablement and customer support.

“We empower every single person at Nasdaq with AI tools,” says Angie Ruan, CTO for capital access platforms at Nasdaq. “Our top priority is engineering. It’s almost a no-brainer to use an AI coding assistant.”

How Nasdaq Empowers Employees With Generative AI

Nasdaq built its own generative AI internal platform that allows the company to retain control over its data, and employees also use commercially available AI solutions such as Microsoft Copilot.

Employees upload documents to the internal generative AI platform, then chat with the tool to quickly search their data and get instant answers to common questions. Sales teams can use AI tools to stay up to date on customer and product data, while support teams optimize their interactions with customers. And in data operations, teams are working to automate workflows in ways that improve quality and increase speed.

Nasdaq, Ruan says, encourages employees to adopt AI tools using top-down and bottom-up approaches, offering formal trainings but also giving workers the chance to learn from peer AI “champions.” This sort of push is needed, she says, to help the company keep up with the extraordinary pace of change in the AI landscape.

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“I’ve been in the industry a long time,” Ruan says. “I’ve never seen this type of speed in innovation.”

As AI tools mature, financial services companies are moving beyond the experimentation phase and integrating the technology into core business processes in ways that yield tangible business outcomes. While the potential benefits are significant, organizations in highly regulated fields such as financial services must balance the desire to move fast with the need to protect sensitive data and avoid disruptions to customers.

Organizations should not adopt AI tools simply to keep pace with competitors but instead carefully align their investments with business goals, says Jay Titus, vice president and general manager of workforce solutions at the University of Phoenix, who serves as a liaison between educators and employers on workplace tech.

“You don’t want to jump into something headfirst without understanding what problem you’re trying to solve,” Titus says. “If you do that, you may solve one problem and then create a new one.”

Generative AI Helps Ally Financial Focus on Customers

Until recently, customer service representatives at Ally Financial had to furiously type up notes during customer calls. The multitasking distracted them from their conversations and had the potential to be incomplete and inaccurate.

Today, those representatives can stay fully engaged with customers on the line while AI tools work in the background to automatically generate comprehensive call summaries. The improvement is part of a broader AI strategy that Sathish Muthukrishnan, Ally’s chief information, data and digital officer, says will fundamentally change what workers can accomplish in the coming years.

“In a decade, every human will have more capability than everyone who is living today, and that is because they will start using these tools very effectively,” Muthukrishnan says. “Someone who knows how to use AI is going to be far more capable with the amount of information and data harnessed, like how we perceive Ph.D.s and the geniuses we have today.”

Ally built a custom platform called Ally.ai, which connects the company’s applications with Azure OpenAI Service. The platform runs in Ally’s own virtual private cloud, and the company delivered a production-ready solution in just eight weeks.

“Creating call summaries and multitasking adds to the cognitive workload,” Muthukrishnan says. “We want our customer service representatives to be fully focused on the customers, not worried about capturing data.”

Credit: Photo courtesy of Ally Financial

How Ally Financial Guards Against AI Data Breaches

Creating customer service call summaries is one of several ways Ally is using AI to make life easier on its teams through process automation. Ally’s marketing team uses it to generate short summaries of financial education articles, and the internal auditing team uses AI to plan and scope audits. In fact, Muthukrishnan says, by using AI, the auditing team has shrunk some process timelines from days down to hours.

In all cases, though, Ally follows three key principles to make sure its AI efforts are safe and accurate. First, the company is focusing on internal-facing use cases until the technology evolves. Second, all AI workflows continue to keep a human in the loop, ensuring that AI tools aren’t making decisions without any oversight. And perhaps most critically, Ally never sends any personally identifiable information to external large language models.

The call summarization tool transcribes customer calls into text, organizes the content appropriately and sends it to AI for summarization before returning the results. Critically, the tool strips conversations of all PII before they are processed by AI and then “rehydrates” this information within the call summaries.

Muthukrishnan notes that Ally is the first U.S. bank to become a member of the Responsible AI Institute. “We’re interested in adopting and using it responsibly,” he says. “We take this extremely seriously. We think first about risk and security, and then we think about the magic of the technology.”

Mastercard Uses AI To Reduce Fraud

AI is nothing new for Mastercard, which has been using the technology for more than two decades for network security and intelligence. But recent advancements have sparked new AI-driven tools that identify fraud, improve decision-making and assist cardholders and merchants.

“We integrate off-the-shelf technologies such as scalable cloud infrastructure and data processing components, in addition to AI and machine learning capabilities, to ensure that our solutions are robust, scalable and capable of meeting the evolving needs of the market,” says Rahul Deshpande, executive vice president and global head of research and development.

The company’s Decision Intelligence solution leverages AI, along with billions of data points, to generate risk scores for transactions. Its Safety Net tool uses AI to monitor all transactions across the Mastercard network, identify widespread fraud attacks and mitigate them. And Shopping Muse combines personalization with generative AI to help shoppers more efficiently find products in retailers’ digital catalogs.

Deshpande says the AI tools improve outcomes and save time for both employees and shoppers. “Mastercard’s AI tools automate complex processes, provide real-time insights and allow users to focus on strategic tasks, enhancing overall efficiency,” he says.

Mastercard is also using AI tools to improve the productivity of its software developers using AI to suggest code snippets, automate repetitive tasks and provide real-time code reviews. And a generative AI-powered digital assistant simplifies customer onboarding by automating routine tasks and answering customer questions.

“These AI tools streamline operations, reduce time spent on manual tasks and improve overall efficiency, enabling employees to focus on strategic initiatives and enhancing productivity across the organization,” Deshpande says.

KEEP READING: More stories from our new publication BizTech: Financial Services.