Payrails report key takeaways – AI in payments: from hype to impact

Irina Ionescu, Senior Editor at The Paypers, highlights the most important findings from Payrails’ latest report – AI in payments: from hype to impact, focusing on how modular infrastructure can help companies leverage AI for real-world performance gains.

Artificial Intelligence (AI) is transforming the payments industry, moving beyond speculative visions to deliver measurable operational improvement. While consumer-facing innovations such as personalised shopping and voice-enabled checkouts are reshaping customer experiences, the most significant progress is happening behind the scenes in payments infrastructure. Payments generate high-volume, high-value data and the future seems to fully embrace the power of this new technology. But they can also be a major operational bottleneck for global enterprises, underscoring the importance of AI-driven efficiency.

Given the excitement around the potential of AI in payments, Payrails recently launched a new report – ‘AI in payments: From hype to impact – How modular infrastructure helps enterprises turn AI potential into real-world performance gains’. According to the report, ‘sitting at the intersection of technology, compliance, and customer experience, payments provide one of the richest testing grounds’ for AI use cases in enterprises. The report notes that 75% of organisations now use AI in at least one business function, while nearly 40% of payment professionals expect AI to heavily influence the payment processing field by the end of 2027. 

Key takeaways from the report

Key drivers of AI adoption in payments

There are several forces that accelerating the adoption of AI in payments, including consumer trends, operational pressure, regulatory change, global expansion, and AI maturity.

According to data from Salesforce and Shopify, 63% of Gen Z shoppers are interested in AI agents purchasing items on their behalf, while 84% of ecommerce businesses rank AI as their number one priority currently. 

Meanwhile, rising authorisation costs and complex reconciliation requirements are pushing payment teams to do more with fewer resources, adding extra operational pressure on companies to sustain their growth and performance amid heavy global competition. 

At the same time, the regulatory framework is consistently changing, adapting to the increasing adoption of AI. In the EU, frameworks like the EU AI Act and DORA demand explainability and audibility, enhancing the importance of modular and transparent systems. At the same time, for merchants operating across multiple markets, fragmented PSP data and inconsistent reporting are common challenges. Finally, although the AI market is still in its early days, the costs and accessibility of this technology have significantly increased, making intelligent automation viable for complex company use cases. 

Together, these factors are accelerating the shift of AI from an experimental, futuristic, and abstract tool into a necessity and driver of strategic value within the payments system. Even small improvements, such a 1% increase in authorisation rates or faster reconciliation can translate into millions in revenue or cost savings for large enterprises, which is why these entities are not only keen on adopting AI but also redirecting some of their profits for research and optimisation. 

The importance of modular infrastructure

Modular payment infrastructure is emerging as the main enabler for AI-driven optimisation. Unlike traditional monolithic systems, modular setups connect PSPs and acquirers through standard interfaces, centralise analytics for real-time insights, and allow new capabilities to be introduced without disrupting existing systems.

This flexibility enables enterprises to pilot AI-driven processes, measure results, and scale confidently. Modular systems create clean, connected, and standardised data environments, which are essential for AI to thrive. As modular infrastructure becomes more widely adopted, payments themselves are evolving from a function that moves money to a system that generates intelligence. 

At the same time, AI in payments is transitioning from mere automation to intelligent, agentic systems capable of learning and acting autonomously. Key areas of impact include:

• Router optimisation: selecting the best-performing PSP or acquirer in real time;
• Retry management: dynamically determining when and how to retry declined transactions;
• Fraud detection: using adaptive risk scoring to flag anomalies in real time;
• Reconciliation and reporting: automating transaction matching and settlement, reducing manual workloads.

As opposed to traditional systems, agentic systems respond dynamically to live performance signals, including changes in issuer behaviour or FX volatility, maintaining human-in-the-loop control but without the need for active human intervention. 

Unified analytics and automation – the foundation for AI

As highlighted by the report, unified analytics plays a crucial role in enabling AI. By consolidating PSP and acquirer data into a single, standardised view, companies can eliminate data silos, gain real-time insights into performance across markets, and make proactive, data-driven decisions. Unified analytics allow merchants to compare PSP performance accurately, optimise retry logic, and improve liquidity management all from one centralised interface. Clean, connected data also supports compliance with regulations such as the EU AI Act, ensuring transparency and auditability.

At the same time, the most prominent progress made in the payments industry with the help of AI comes from automation. Automation addresses repetitive, high-volume tasks such as reconciliation, PSP onboarding, and retries. For reconciliation, automating matching and exception handling can reduce error rates by up to 95%, whereas adopting intelligent retry systems can improve authorisation rates by up to 10%, directly impacting revenue in a positive way. 

Thus, automation not only improves efficiency but also creates structured datasets that AI systems can depend on to learn, continuously improve, and  act responsibly. 

The role of fraud and risk management

Until now, one of the payment common use cases for AI has been on how payment teams handle fraud prevention and risk management. Unfortunately, the technology still requires training as many of the fraud systems based on AI automation operate in isolation, which can lead to high false-positive rates and limited auditability. Future generations of fraud management tools will integrate risk systems within the broader payment architecture, enabling continuous learning from new data sets and real-time adjustments based on fraud signals, as well as unified views of chargebacks and dispute data.

According to the report, as fraud and payment operations converge, the boundaries between risk management and optimisation will continue to blur. What enables this convergence is modular infrastructure through its shared data layer, consistent governance, and transparency.

The future of AI in payments

Looking ahead, the next stage of payment innovation includes intelligent agents that handle operational tasks autonomously. For instance, reconciliation agents can match transactions in real time, flagging only anomalies for review, whereas routing agents can detect dips in authorisation rates and redirect traffic to higher-performing acquirers, streamlining the payment process.

It is important to note that the successful deployment of these agents depends on modular architecture, unified data, and transparent audit trails to safely and effectively operate. 

Conclusions

AI is no longer a future ambition; it is already driving measurable gains in efficiency, accuracy, and scale in payments. Modular infrastructure, unified data, and observability are the foundations that enable payments to evolve from rule-based processes into adaptive, learning systems. Companies that choose to invest in these foundations will ride the next wave of gains in performance, resilience, and insight. 

The question is no longer whether to adopt AI, but whether companies can fully support it and efficiently integrate its capabilities. By building for intelligence now, organisations can define the standard for how payments adapt to the transformations to come. 

Want more? Download the full report here!

 

About the author

 

Irina is a Senior Editor at The Paypers, primarily specialising in online payments and fraud prevention. She has a Ph.D. in Economics and a strong economic academic background, with interests in fraud prevention, chargebacks, fintech, AI, ecommerce, and online payments. Reach out to her via LinkedIn or email at irina@thepaypers.com.

About Payrails

 

Payrails is a global payment software company helping businesses maximise performance and take control of their payment operations. Our all-in-one platform spans the entire payment lifecycle — including payment orchestration, payouts, tokenization, chargeback processing, unified analytics, automated reconciliation, and in-person payments. All powered by advanced data capabilities.