For years, payment personalization has been heralded as a revolution for the customer experience. Targeted offers, intelligent recommendations, personalized journeys... Yet, in-store experiences remain largely standardized. Whether you're a loyal customer or an occasional buyer, the checkout process is identical: the same payment methods, the same promotions visible to everyone, with no adaptations based on preferences or purchase history.
But these scenarios are still in their infancy. While some retailers and payment providers are beginning to experiment with generative AI, its integration into physical payment journeys remains limited. Real-time recommendations, dynamic adjustment of offers, and automatic optimization of payment options are promising advances, but they are still far from becoming a widespread reality in stores. Between technological challenges, regulatory constraints, and gradual adoption by merchants, it remains to be seen whether, this time, the promise will truly be kept.
Generative AI: an emerging personalization engine with promising prospects
Ultra-targeted, finally relevant merchant offers
No more impersonal promotions that inundate all customers indiscriminately. By cross-referencing transactional data and consumer preferences, AI allows brands to adjust their offers in real time to provide truly tailored discounts. For example, Mastercard uses this technology in Smart Subscriptions, a service designed to optimize the management of digital subscriptions. Thanks to AI, users receive specific offers on their most used subscriptions—streaming platforms, cloud services, or paid apps—increasing customer engagement while generating additional revenue for merchants and issuers.
Intelligent shopping assistants that go beyond the simple chatbot
Far from simply answering customer questions, AI-powered conversational assistants are becoming true sales advisors. Shopify has integrated AI into Selli, its ChatGPT-based assistant, capable of understanding users' purchasing intentions and recommending the most relevant products based on their history. This type of intelligent assistance not only streamlines the purchasing journey: it also boosts conversions. Mastercard's Shopping Muse solution has seen a 15 to 20% increase in sales by making recommendations more intuitive and engaging.
Relying on AI assistants to facilitate onboarding and merchant support
Generative AI is no longer limited to traditional chatbots. Thanks to these intelligent assistants, merchants can access essential information more quickly, benefit from proactive support, and gain autonomy, thus reducing their dependence on traditional call centers. And tomorrow? Voice AI could take the experience to the next level by making it even more fluid and intuitive.
More transparent and effective financial recommendations
AI no longer simply suggests banking offers; it makes them clearer and more engaging. By explaining why a credit card or loan is suitable for a profile, as Credit Karma (Intuit) does, it reassures customers and reduces hesitation. Result: fewer lost opportunities and higher acceptance rates.
A promising technology, but still under scrutiny
While generative AI paves the way for increased payment personalization, its integration still raises many challenges. The explainability of algorithms, the transparency of decisions, and data protection remain major issues. Without a solid governance framework, these advances could quickly become a factor of distrust rather than a lever for engagement.
Personalization without transparency is risky. When a generative AI recommends a payment option, a discount, or a credit, how can we ensure that these suggestions are fair and relevant? Without solid safeguards, it can produce biased recommendations, generate errors, or create dangerous approximations for the consumer.
This is why payment players must guarantee explainability and control. Recommendations must not be black boxes: both merchants and end users must understand why an offer is being made to them. To this end, frameworks like KPMG's Trusted AI are emerging to oversee these technologies. They establish transparency rules, document algorithm decision criteria, and enable regular audits. Mastercard, for example, relies on this type of approach to ensure that its AI systems clearly explain the reasons for a discount, a split payment proposal, or a dynamic exchange rate.
Data protection is another major issue. Ultra-personalization must not become intrusion. Complying with regulations like the GDPR or the DSA and ensuring complete transparency on data use is essential to maintain consumer trust.
Finally, continuous monitoring is essential to avoid any abuse. “AI control towers” allow for continuous auditing of model performance and correction of biases. Yet, by the end of 2023, only 13% of financial institutions worldwide had a policy governing generative AI, according to BCG. This delay raises questions, even as adoption of this technology is accelerating.
Generative AI is no longer a fantasy, but its adoption remains uneven. Deploying AI capable of personalizing payments in real time requires massive investments, access to colossal volumes of proprietary data, and advanced technical expertise. Few players truly have the means to do so, and even those that are working on it, like Intuit with Intuit Assist, are still proceeding cautiously. The solution, which is supposed to warn its users of the risk of overdrafting and offer financial adjustments, is still in the testing phase and displays a warning indicating that it is "still learning" and may make mistakes.
Players who can combine relevance, transparency, and efficiency will come out on top, provided they allocate budgets and structure solid strategies. For others, generative AI risks remaining nothing more than a marketing argument without any real impact. The question is therefore no longer whether it will transform payments, but how it will be adopted intelligently, without excess or loss of user confidence.
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