TL;DR
Splitit CEO urges payments firms to focus on building their own AI technologies rather than just adopting existing solutions. This marks a strategic shift in how payment companies approach AI integration.
Splitit CEO Noga Levy-Ron has publicly emphasized that payment companies must focus on building their own AI capabilities rather than merely adopting existing solutions. Her remarks, made during a recent industry conference, highlight a strategic shift in the payments sector, underscoring the importance of in-house AI development for competitive advantage.
During the conference, Levy-Ron stated that relying solely on third-party AI tools limits the potential for innovation within payment firms. She argued that developing proprietary AI systems allows companies to tailor solutions specifically for their needs, improve security, and better serve customers. “Building your own AI is about control, customization, and long-term value,” she said.
Splitit, a leading provider of buy now, pay later (BNPL) solutions, has been exploring AI integration to enhance fraud detection, credit scoring, and customer service. However, Levy-Ron emphasized that the industry must move beyond mere adoption and invest in developing unique AI frameworks that give them a competitive edge.
Levy-Ron’s comments come amid broader industry discussions about AI’s role in financial services, with many firms currently using third-party tools from major technology providers. Her stance suggests a future where in-house AI development becomes a key differentiator for payment companies seeking to innovate and safeguard their operations.
Strategic Shift Toward Proprietary AI in Payments Sector
This statement signals a potential shift in the payments industry, where companies may prioritize developing custom AI solutions to secure competitive advantages, improve security, and better serve customers. It underscores the importance of in-house innovation amid rapid technological change and increasing regulatory scrutiny. For investors and industry watchers, this could mean a move toward more self-reliant and technologically advanced payment firms, potentially reshaping partnerships with AI technology providers.AI development tools for payment companies
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Industry Trends and Past AI Adoption in Payments
Over the past few years, many payment firms have integrated AI tools developed by large technology companies to automate fraud detection, streamline customer support, and enhance credit scoring. Major players like PayPal, Square, and Stripe have relied on third-party AI solutions, citing rapid deployment and cost efficiency.
However, concerns about over-reliance on external AI providers, including issues related to data privacy, lack of customization, and dependency, have grown. Some industry leaders have called for more in-house AI development to address these challenges and create differentiated offerings. Levy-Ron’s comments reflect this evolving mindset, emphasizing the need for strategic independence in AI capabilities.
This shift aligns with broader trends in financial technology, where firms seek to control core innovations rather than depend on external vendors, especially as AI becomes more central to competitive positioning and regulatory compliance.
“Building your own AI is about control, customization, and long-term value.”
— Noga Levy-Ron, Splitit CEO
proprietary AI solutions for finance
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Unclear How Quickly Payment Firms Will Shift Strategies
It is not yet clear how many payment companies will prioritize building their own AI systems versus continuing to rely on third-party providers. The costs, technical challenges, and timeframes involved in developing proprietary AI solutions remain uncertain, and some firms may face resource constraints or strategic hesitations.custom AI software for fraud detection
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Industry Response and Potential Adoption of Proprietary AI
Payment firms will likely evaluate their AI strategies in the coming months, balancing costs, benefits, and competitive pressures. Some may announce initiatives to develop in-house AI capabilities, while others might continue partnerships with established AI vendors. Industry conferences and investor reports over the next year will shed light on how widespread this shift becomes.
Regulatory developments related to AI in financial services could also influence how quickly firms move toward proprietary solutions, especially concerning data privacy and security standards.
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Key Questions
Why does the Splitit CEO believe payment firms should build their own AI?
Levy-Ron argues that building proprietary AI offers greater control, customization, and long-term strategic value, allowing firms to innovate and better protect themselves from dependencies on external vendors.
What are the challenges of developing in-house AI systems?
Challenges include high costs, technical complexity, the need for specialized talent, and longer development timelines. Smaller firms may find it difficult to compete with larger firms’ resources.
How might this shift impact existing AI partnerships in the payments industry?
Firms may gradually reduce reliance on external AI providers, develop their own solutions, or seek hybrid approaches. This could lead to a restructuring of vendor relationships and increased investment in internal R&D.
Could this focus on building AI lead to increased innovation in payments?
Yes, in-house AI development can enable tailored solutions that better meet specific business needs, potentially driving innovation and differentiation in the competitive payments landscape.
Source: rss
