Mastercard Reorg Shows Urgent Industry Focus on AI

The jury is still out as to how artificial intelligence (AI) will affect jobs in the financial services industry. But if the past six weeks have shown anything, it is this: AI is definitely coming for the org chart.

The technology’s impact has been felt at major and minor levels for almost every company in the payments industry. 

At Visa, it sparked what was arguably one of the company’s most high-profile new product launches with Visa Protect, which uses AI to reduce fraud across account-to-account and card-not-present payments, as well as transactions both on and off Visa’s network. 

At Amex, CEO Stephen Squeri used his March 15 shareholder letter to introduce a new Generative AI Council that includes technology, data science, risk management, legal and strategy teams to assess and approve the deployment of generative AI (GenAI) use cases across the company. 

And at JPMorgan Chase, most news outlets missed CEO Jamie Dimon’s announcement of a new chief data and analytics officer, still to be filled. 

“Elevating this new role to the Operating Committee level — reporting directly to Daniel Pinto and me — reflects how critical this function will be going forward and how seriously we expect AI to influence our business,” Dimon said in a letter to shareholders. 

“This will embed data and analytics into our decision making at every level of the company. The primary focus is not just on the technical aspects of AI but also on how all management can — and should — use it,” he added. “Each of our lines of business has corresponding data and analytics roles so we can share best practices, develop reusable solutions that solve multiple business problems, and continuously learn and improve as the future of AI unfolds.”

At Mastercard the importance of data — which these days is synonymous with AI — played a big role in an executive reshuffling announced April 9. 

In addition to the appointment of Jorn Lambert to chief product officer and the elevation of Raj Seshadri to chief commercial payments officer, CEO Michael Miebach tagged company veteran Craig Vosburg to the new post of chief services officer. 

Vosburg will have a wide data remit. His team will integrate offerings from the company’s current Cyber & Intelligence, Data & Services and Open Banking departments. It will include managing fraud, risk and cybersecurity, analytics and loyalty programs. Vosburg will also oversee a newly formed Data and AI organization that will include commercialization for both internal and external applications, and the governance of these functions across the entire enterprise. 

All these moves reflect the dramatic impact AI has had on the payments industry. Couple AI’s emergence with recent interchange reductions, and you have a pressing need to move data monetization to near the top of the agenda. And given the state of executive decisioning around AI as measured late last year, its happening right on time. 

A December survey from EY showed nearly all (99%) of the financial services leaders surveyed reported that their organizations were deploying AI in some manner, and all respondents said they are either already using, or planning to use, GenAI specifically within their organization. 

But 20% of the respondents said they were skeptical about the potential impact of GenAI on their organization. That same percentage also lacked confidence that their organizations were well-positioned to take advantage of the potential benefits AI might bring. 

Many management experts had been calling for a restructured place for AI and data to sit within an enterprise. For example, an MIT Management School article published last week cautioned executives that AI use isn’t about staying ahead of the curve or being part of a trend. It encouraged organizations to clearly define the breadth of available AI technologies and techniques available to them so they can match a business solution to a business problem.

“Culture is a big part of the equation,” according to the MIT article. “Organizations will need to create silo-busting cross-functional teams, make failure permissible to encourage creativity, and encourage innovative ways to combine human and machine capabilities in complementary systems.”