In fact, more than eight in 10 corporate decision makers expect generative AI to unlock new opportunities for their business, particularly in commercial payments, according to a HSBC report titled ‘Navigating the AI Wave: Innovations in Commercial Payments’.
At its core, generative AI in payments uses large language models and related techniques to understand natural language, generate content and spot patterns across huge volumes of structured and unstructured data. In banking, these systems are already being used to automate routine work, enhance customer service, detect fraud and offer tailored financial guidance.
Research from Accenture found that leading financial institutions have already automated 40% of manual tasks in their payments operations through AI implementation.
Ireland’s advantage: Payments footprint + applied AI ecosystem
Ireland is well placed to benefit from this shift, with major financial services and payments operations anchored here. The country hosts tech giants like IBM, BNY Mellon, Stripe, Mastercard, and Citi, and remains a hub for cybersecurity and artificial intelligence with banks and payment platforms increasingly exploring AI solutions to thwart the efforts of fraudsters around the world and protect their customers.On the talent and R&D side, Ireland’s applied AI ecosystem includes CeADAR, the national centre for AI, and major research centres such as ADAPT, INSIGHT, Lero, alongside programmes like EMPOWER focused on data governance.
Why generative AI is moving centre stage in payments
The commercial payments industry is facing pressure on several fronts. Traditional transaction services are being squeezed by digital first FinTechs while clients increasingly judge providers on service quality rather than price alone. Research by HSBC shows that many corporate clients would consider switching provider for better digital experiences, especially around service responsiveness.At the same time, payments data is exploding. According to the report, the majority of banking information is unstructured, locked in emails, contracts and narrative fields that are difficult to analyse with older tools. Generative models excel at reading this kind of messy text, summarising key points and surfacing anomalies, which makes them attractive for functions such as onboarding, investigations and reconciliation.
Another catalyst is speed. Real time schemes and instant payment rails are becoming the norm in many markets, leaving very little room for manual checks.
Finally, there is a clear shift in leadership attitudes. IBM’s global survey of banking executives suggest that most major institutions now have at least pilot projects under way, with many focusing on a series of targeted generative AI use cases in payments rather than a single large bet.
Generative AI use cases in payments
The most generative AI use cases in payments cluster around a few themes that span both retail and commercial banking.Smarter client servicing and virtual assistance
Banks and payment processors are deploying AI-powered virtual assistants that can read policy documents, product catalogues and transaction histories, then answer staff or customer questions in plain language. These tools can summarise correspondence, guide staff through client queries and recommend next best actions, freeing human support teams to focus on more strategic tasks.
Fraud detection and financial crime
Fraudsters already use sophisticated technology, so static rules alone are no longer enough. Generative AI can generate synthetic fraud scenarios for testing, helping financial institutions bolster their defences against the evolving cybersecurity threats. Since generative AI can analyse troves of transaction data quickly, it can spot unusual payment patterns and help banks and payment providers detect fraudulent activities such as account takeover and money laundering.US tech giant IBM's Safer Payments platform uses machine learning and generative AI techniques to help banks build their own decision models, allowing them to respond quickly to new fraud patterns while maintaining very low false positive rates. At BNY Mellon, the US banking giant uses its enterprise AI platform, Eliza, to help identify and manage risk signals as part of a broader range of AI applications across the company.
Irish-founded payments giant Stripe uses its Radar platform primarily for AI-powered fraud detection and prevention across its entire network. It leverages machine learning models trained on data from over a trillion dollars in annual payment volume to identify and block fraudulent transactions in real time.
Cash flow forecasting and liquidity management
Financial institutions can tap into generative AI to improve an array of processes such as accounts receivable (AR) and accounts payable (AP), cashflow management, and financial forecasting. AI-powered predictive analysis can provide more accurate forecasts, aiding institutions manage liquidity more effectively and make prudent decisions.Debt collection and credit approval
Generative AI can support credit approval by analysing thousands of data points at speed, from income and spending patterns to industry trends, helping lenders distinguish between genuinely risky customers and those who simply do not fit traditional credit scoring models.The same capability can be applied to debt collection, with AI models spotting patterns of delinquency, proposing tailored repayment options and guiding teams towards strategies that improve recovery rates while preserving customer relationships.
Compliance and regulatory reporting
New rules such as ISO 20022 message standards and evolving AI regulation in Europe require institutions to structure data more carefully and explain decisions. Generative AI can help parse scheme rules, prepare regulatory reports, validate addresses and support legal teams in reviewing contracts, provided that humans remain in the loop to check outputs.Risks, regulation and the road ahead
As generative AI in payments scales, conversations about risk and AI ethics are moving from specialist forums to board agendas. According to the HSBC report, many institutions are struggling with readiness due to legacy tech and data architecture, despite the promise of generative AI.Payment providers must manage model bias, data privacy, explainability and operational resilience, especially as supervisors in Europe and elsewhere treat payments as critical infrastructure.
Bias and data privacy are particular concerns. If training data reflects historical discrimination in areas such as credit approval or fraud flagging, generative models may reproduce those patterns. Organisations also need to ensure that AI systems are tightly secured so that confidential and personal information is protected.
There is also the problem of hallucinations, where a model produces confident but incorrect answers. In a high-stakes environment like payments, that is unacceptable and reinforces the need for transparent and explainable AI within a tightly regulated industry.
Regulators are moving in parallel. The European Union's Artificial Intelligence Act introduces risk-based categories and tougher requirements for AI systems used in areas such as credit scoring, lending, and transaction monitoring.
FAQs: Generative AI in payments
How is AI used in the payment industry?AI is used to detect fraud, automate back office tasks, analyse payments data in real time and deliver more personalised experiences to both consumers and corporate clients.
How can generative AI be used in finance?
Generative AI can summarise complex documents, simulate scenarios, generate tailored advice, support compliance teams and power conversational assistants that sit on top of core banking and payment systems.
Are banks using generative AI?
Yes, banks across major markets are rolling out generative AI for fraud detection, customer service, payments operations and analytics, with surveys suggesting that a large majority now use it in at least some areas.