Artificial intelligence is by no means a new concept.according to mango pay Kirk Donohoe, Chief Product Officer The emergence of AI and predictive AI has been part of the technology landscape for quite some time.
However, advances in AI, especially generative AI, have brought about a transformative change in how we approach data. This evolution is characterized by a focus on unstructured data, marking a departure from previous structured data-centric approaches.
“Ninety percent of everything we deal with on a daily basis is unstructured data, and in the last 24 months or so generation AI has really come into the spotlight,” Donohoe said. “What’s next? In an interview with PYMNTS as part of “Will we do it?” Payments: Payments and Gen AI” series.
More specifically, instead of providing a dataset and making predictions, as has traditionally been done, generative AI processes large unstructured datasets through linguistic queries and derives insights from them. He explained that it now produces results not previously seen in the traditional AI space.
He cites advances in fraud prevention, particularly the ability to monitor discussions about a particular brand across various forums and platforms, as an example of the diverse applications that can be facilitated by leveraging the power of unstructured data and generative AI. I emphasized.
Donohoe said generative AI can identify potential attempts to exploit these brands through subtle analysis of communication patterns. This is a new approach that goes beyond traditional rules-based workflows and traditional machine learning to enhance fraud prevention strategies.
The impact extends beyond customer support and success efforts, where generative AI streamlines documentation processes and enhances customer service, as well as payment capture.
“AI is now scripting FAQs and helping us put together documentation for our APIs,” he said, so that when someone asks a question, it comes up with relevant answers.
Employing modularity and flexibility
As the concept of modularity takes hold in the payments space and more and more mature merchants embrace the concept, Mangopay has also embraced this trend and adopted a similar modular approach within its own operations. Mr Donohoe said.
Donohoe said the strategy emphasizes flexibility at both the payments and AI level, allowing merchants to customize their stacks and securely enhance their products.
“take [for example] It is a payment product in the Mangopay ecosystem,” he said. “If you need additional enhancements on the AI side, we can facilitate that in a restrained way, protect your data, and help you make your products more intelligent.”
He added that the goal is to enable strong interoperability between multiple pieces of unstructured data, and as products become “more intelligent,” AI engines also become “smarter.”
Regulatory learning curve
Asked about market readiness and education on the benefits of AI-driven payments solutions, Mr Donohoe acknowledged existing concerns about the unknown aspects of the technology, particularly its potential impact on business and data privacy.
Concerns include questions about where data is stored, who can access it to train models, and the need for clear regulation, which the European Union is pushing through its landmark AI bill. Masu.
While he also acknowledged legitimate concerns that regulatory frameworks can outpace innovation, he emphasized the importance of industry stakeholders and governing bodies to foster a regulatory environment that fosters innovation and ethical practices. did.
Mr Donohoe said it was “the right thing to do” to take part in these discussions, given his experience with fintech company Mangopay, which is regulated by Europe’s Luxembourg Financial Sector Authority (CSSF) and the UK’s Financial Conduct Authority (FCA). He emphasized. Regulators will go through a learning curve and find their footing in the evolving AI landscape.
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