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Affirm CEO James Reinhardt: AI to Revolutionize Shopping and Payments

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  Published in Business and Finance on Wednesday, November 19th 2025 at 15:09 GMT by Channel NewsAsia Singapore

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AI set to redefine shopping and payments, says Affi​rm CEO

In an interview published on Channel News Asia, James Reinhardt, the chief executive of U.S.‑based buy‑now, pay‑later (BNPL) company Affirm, outlined how artificial intelligence (AI) will overhaul the way consumers shop and pay across the globe. The conversation, which ran under the headline “AI set to redefine shopping and payments, affirm CEO says,” highlights both the strategic imperatives and the broader industry trends that are pushing retailers, fintechs, and merchants to rethink their technology stacks.

The AI‑driven future of checkout

Reinhardt began by framing AI as a catalyst for “frictionless commerce.” He noted that traditional checkout funnels—spending time on payment pages, entering shipping details, dealing with CAPTCHA—have long been stumbling blocks that cost brands revenue. AI, he argued, can streamline each of these steps.

1. One‑click payment and authentication – By leveraging machine‑learning models that learn a customer’s transaction habits, the checkout can be reduced to a single tap. Reinforced by token‑based authentication, this approach also addresses data‑privacy concerns that have been a growing pain point for merchants.

2. Dynamic risk assessment – AI can continuously evaluate risk in real‑time, using behavioural patterns to differentiate between genuine fraud attempts and legitimate transactions. This is especially crucial for BNPL products, where a misjudged risk can lead to bad debt. Reinhardt explained that his team is working on a “neural‑network‑driven credit decision engine” that will be rolled out in phases across all markets, starting with the U.S. and the U.K.

3. Personalised offers – Reinforced by the same behavioural data, AI can suggest the most appropriate payment plan, discount, or complementary product. The goal is to boost average order value (AOV) while keeping customer satisfaction high.

Affi​rm’s own AI roadmap

Affirm has traditionally relied on “scorecard‑based” underwriting, using credit bureau data and a handful of behavioural variables. Reinhardt said the next stage involves “deep learning” models that can parse unstructured data such as purchase history, online browsing patterns, and even sentiment analysis from social media. According to him, this will:

• Increase approval rates for consumers with limited credit history while keeping delinquency rates under control.
• Improve pricing precision, ensuring that consumers receive fairer rates that reflect their real risk.
• Reduce operating costs by automating underwriting decisions that currently require human oversight.

Reinhardt also hinted at the upcoming integration of a “AI‑powered fraud‑detection layer” across all merchant partners. This layer will feed directly into the merchant’s own fraud systems, creating a “shared‑risk” environment that is more resilient against sophisticated attackers.

Partnering with merchants and ecosystems

Affirm’s expansion strategy is increasingly AI‑centric. The CEO discussed a new partnership with Shopee, the Southeast Asian e‑commerce giant, in which AI will be used to personalize the BNPL experience for Singaporean consumers. The goal is to “create a single, AI‑driven recommendation engine” that can predict the likelihood of payment default as well as the optimal payment plan structure.

Reinhardt also cited a collaboration with Coupang in South Korea, where a joint AI model will be used to cross‑sell complementary goods and upsell premium payment options. In both cases, AI is central to achieving “mutually beneficial” outcomes: merchants receive higher conversion rates, and consumers get a tailored payment experience.

Beyond e‑commerce, the CEO highlighted potential use cases in brick‑and‑mortar retail. In 2025, he envisions “AI‑enabled smart checkouts” that use computer vision to automatically scan items, compute a digital receipt, and apply relevant BNPL offers—all in a matter of seconds.

Regulatory and ethical considerations

As with any technology that influences credit decisions, AI brings regulatory scrutiny. Reinhardt underscored that “Affirm has been proactive in engaging with regulators,” citing his team’s efforts to align with the U.S. Equal Credit Opportunity Act (ECOA) and the European General Data Protection Regulation (GDPR). The company is working to embed fairness constraints into its AI models, ensuring that decisions are explainable and free from bias.

He also touched on the importance of transparency for consumers, suggesting that an “AI‑driven dashboard” will be introduced, allowing users to see exactly how their data is being used to shape payment terms. This is part of a broader trend where fintechs are being asked to provide “explainable AI” to meet both regulatory requirements and consumer expectations.

Market context and competitive landscape

The AI‑enabled payments space is highly competitive. Companies like PayPal and Square have already begun to deploy machine‑learning‑based credit underwriting for their BNPL offerings. Meanwhile, traditional credit card issuers are exploring AI to refine risk scoring for “Buy‑Now, Pay‑Later” segments. Reinhardt noted that this competition is driving rapid innovation, with firms scrambling to develop “real‑time, on‑device” AI solutions that can be deployed across billions of devices worldwide.

Reinhardt also acknowledged the role of large data brokers and how their APIs can feed into AI models. He suggested that, rather than relying solely on third‑party data, companies should build proprietary data pipelines to create truly differentiated insights.

Looking ahead

Reinhardt concluded that the next few years will be pivotal for AI in payments. He predicted that:

• Personalisation will become a default – Consumers will expect AI‑generated offers to be a standard part of the shopping experience.
• Real‑time risk assessment will be the norm – Fraud teams will rely on AI that learns continuously, reducing false positives and negatives.
• AI will blur the line between commerce and credit – Payment solutions will become seamlessly embedded into the product experience, with less emphasis on separate “checkout” screens.

As the industry continues to evolve, companies like Affirm are positioning themselves not merely as fintechs but as “AI‑first” commerce platforms. The integration of advanced machine learning into every stage of the consumer journey—from discovery and recommendation to payment and post‑purchase—promises to deliver a more personalized, efficient, and ultimately trustworthy shopping experience.

Key Takeaways

In sum, James Reinhardt’s vision paints a future where AI is no longer an optional add‑on but a core pillar that reshapes shopping and payments, making the process more intuitive, data‑driven, and consumer‑centric. As more merchants adopt AI‑powered solutions, the industry will likely see a shift toward a more integrated, seamless commerce ecosystem—one that places AI at the heart of every interaction.