Mastercard is accelerating its generative AI strategy as the technology enters the financial services industry, providing a source of new business for the card brand that is not dependent on card fees.

The payments company added an AI-powered digital assistant to its customer service offering this week, following previous generation AI-related measures focused on security. Mastercard uses Databricks’ data intelligence platform to train the gen AI engine that supports the card brand’s onboarding wizard. This is designed to automate routine tasks and generate responses to customers with less human supervision.

A new gem for Mastercard’s digital assistant is augmented recovery generation technology – an advanced AI tool that uses information tailored to a specific task from a dedicated organization. RAG uses Mastercard’s existing onboarding documentation to locate the precise information needed to respond to user prompts.

This is designed to improve the performance of “older” generation AI that uses less granular data. “There are a number of steps to integrating a new product,” said Greg Ulrich, head of AI and data at Mastercard. “This technology can get answers to consumers’ questions faster than we could otherwise.”

Market issues

Generation AI accesses data and user prompts to produce original content and has led companies in financial services and other industries to explore what “human tasks” can be replicated. Since its introduction at the end of 2022, AI generation starts to show results. Financial services companies save between 5% and 8% in productivity thanks to use AI generationaccording to Capgemini.

MasterCard generation AI strategy includes a month of May partnership with Salesforce to support the resolution of payment disputes.

Mastercard also rolled out Generation AI in May to detect compromised cards more quickly. The real-time decisioning product helps banks produce risk scores and approve transactions. Gen AI analyzes additional data points to predict whether a transaction is likely to be authentic, strengthening Mastercard’s ability to analyze account, purchase, merchant and device information in real time. Called ID Pro, it has improved fraud detection rates by 20% on average since May, according to Mastercard.

The brand of the card will then determine how AI generation can be used beyond integration and security. “If a consumer has a question about a product, for example, that can be helpful,” Ulrich said. “How can we make our systems smarter and make better recommendations to our customers?”

The announcement of the AI ​​assistant came close to a non-AI version at Mastercard, designed to minimize manual steps in payments. Mastercard combines biometric authentication with tokenization, or replacing card account numbers with a temporary value that renders the card useless if stolen. By combining biometrics and tokens, the card brand hopes to eliminate the need for manual card entry for online purchases by 2030.

By using AI and other technologies, Mastercard hopes to compete with other payment companies that are also using AI generation to streamline work models.

Visa Generation AI The strategy includes a security element, using technology to alert banks to threats, particularly those that use bots or other forms of machine learning. PayPal under new CEO Alex Chriss has invested in AI generation to support less navigation and faster payment and shopping tools for merchants and consumers. Klarna has AI generation deployed to most of its workforce and has modified its recruiting strategy based on the impact of AI on manual labor.

“Generation AI can integrate data sources faster and easier,” Ulrich said. “And it can create new content to answer questions.”

Always a challenge

According to Celent, the AI ​​generation is evolving from large, general-purpose language models to more specialized models that are trained to understand context and perform a few tasks very well. This includes new AI designed to operate with little or no human supervision, or “Agentic AI“.

“Mastercard’s announcement is very consistent with some of our conclusions about where the puck is going in terms of investments in AI generation by payments companies,” said Zil Bareisis, principal analyst at Celent. Celent’s research, conducted with AWS, warns against inflated expectations of these cutting-edge versions of AI. Payments companies need to think about what they can do with GenAI over the next three years, while preparing for 2028, according to Celent.

“With AI, finding the right solution is more important than moving quickly,” said Christopher Miller, senior emerging payments analyst at Javelin Strategy & Research. Overlapping regulatory frameworks and the need for payments to be accurate and correct mean that the most obvious near-term use cases for AI generation lie on the fringes of payments, rather than playing a direct role in processing .

“This means that well-defined use cases, such as customer service and fraud reduction, go well beyond the direct checkout line,” Miller said. “Mastercard appears to have found a good fit with this product, remaining on the periphery of the payment flow.”

However, as AI technology continues to evolve rapidly and find new delivery models, the “co-pilot” or “human assessment” framework that is prevalent in late 2024 will not age well, said Miller. Mastercard is focused on its own integration as a company. initial use. One of the challenges of using AI generation or related technology for integration is selling it to customers for their own use, Miller said.

“A key question for other companies considering developing or using a tool like this is whether or not they have access to the data needed to train the models and whether they have the technical talent to put together multiple AI technologies in a stack that will deliver successful products. “.