As Generative AI (GenAI) becomes increasingly embedded in financial services, banks, payment processors, fintechs, and consulting firms alike are moving beyond experimentation and exploring how it can create real business value. For simplicity, we’ll refer to GenAI as “AI” throughout the rest of this article. In this article, Paylume’s Head of Digital Transformation, Frederic Barbaix, shares how banks and payments providers can harness AI to accelerate delivery across the entire software development lifecycle.
At Paylume, this has aligned closely with our journey so far and our broader investment in both AI-driven delivery practices and our growing developer capability. This has helped us to boost our offering to clients, supporting our customers from strategy and analysis through to implementation and testing.
However, the most important lesson we’ve learned is simple: AI is a powerful accelerator, but it should never replace engineering discipline, critical thinking, quality assurance, or human accountability in teams. It needs to be embedded in a banks ways of working and its software delivery process.
AI’s real opportunity in Banking and Payments
Digital transformation can be inherently complex for banks. Modern payment ecosystems involve interconnected services. These span across integrations with a wide variety of customer channels, core banking, payment processing, fraud monitoring, compliance, authentication, reconciliation, AML reporting, and regulatory controls.
Even small changes can have significant downstream impacts, let alone the types that banks have recently had to implement, whether that be ISO 20022 changes, Instant Payment schemes, additional data requirements or new fraud controls like Verification of Payee.
This is exactly where AI provides value.
Rather than focusing solely on code generation, we see AI helping teams with:
- Refining requirements and user stories
- Identifying gaps
- Improving acceptance criteria
- Accelerating technical analysis
- Challenging assumptions and designs
- Generating test scenarios and documentation
- Supporting incident investigation and root-cause analysis
In high-stakes payments environments, better thinking often delivers more value than faster coding.
Additionally as you can embed AI through orchestration layers in your software delivery lifecycle, it allows to create full traceability of (historical) decisions, perform multiple review cycles by humans and AI agents, keep all software delivery artifacts up to date at any point in time and build up a knowledge base that allows to onboard new project members in an easy manner.
What good AI usage looks like
Teams get the best results when they approach AI with structure and intent.
The first step is providing enough context. AI only works with the information it is given. Teams need to clearly explain the business objective, constraints, architecture, standards, and expected outcome to yield better results.
It is also important to work in incremental chunks. Smaller, focused outputs are easier to review and validate, reducing the likelihood of defects, misunderstandings, or design issues slipping through.
Before generating code or tests, project members should spend enough time defining the problem properly. Agree on the expected behaviour, edge cases, validation rules, and acceptance criteria first. In many cases, this upfront thinking delivers more value than the code generation itself.
Most importantly, teams should use AI as a sparring partner. Ask it to identify risks, explore the alternatives, explain trade-offs, and challenge proposed approaches. Used this way, AI becomes less of a content or code generator and more of a useful sounding board during delivery.
Where teams go wrong
The biggest mistake is treating AI as magic. Despite the efficiency gains, AI is not a silver bullet and must be treated with scrutiny by those who use it.
There are common pitfalls worth avoiding.
One of the biggest is placing too much trust in AI-generated output. While AI can often produce impressive results, it can also generate results that are incomplete, inconsistent, or simply incorrect. The responsibility for validating the outcome still sits with the team using it.
It’s equally important not to assume that because AI produced a good result once, it will do so every time.
Unlike traditional software, AI is not deterministic. The same request can produce different outputs, which means proper quality gates are to be put in place to ensure it does not deliver the wrong outcomes.
AI should also complement existing delivery processes, not replace them. Peer reviews, automated testing, static code analysis, security assessments, and compliance controls remain essential parts of delivering high-quality software, especially in regulated industries such as banking and payments.
Data security is another area where discipline is critical. Organisations need clear guidelines around what information can and cannot be shared with AI tools. Payment data, customer information, credentials, production logs, and confidential business documents should always be handled with appropriate care and governance.
Teams should also be mindful of complexity. Just because AI can generate a sophisticated solution doesn’t mean it should. The best solutions are usually the ones that remain simple, maintainable, aligned with existing architecture, and easy for the wider team to understand and support.
Finally, organisations should avoid treating prompt-writing as a methodology in its own right. Sustainable adoption comes from establishing repeatable ways of working, shared standards, reusable templates, and clear responsibilities across teams.
Above all, AI does not remove accountability. It is great support to analysis, development, testing, and decision-making, but responsibility for the final outcome will always remain with the people delivering the work.
AI and the future of Payments delivery
As Paylume continues expanding its delivery capability, including recently significantly growing our development function, focus remains on combining deep payments expertise with modern delivery practices. This helps clients navigate increasingly complex transformation programmes.
AI plays an important role in that journey, but not as an autonomous decision-maker. Instead, it serves as a capability enhancer across analysis, design, development, testing, documentation, and operational support. That is why we did not invest only in creating our own payments AI agents but also in embedding them in an orchestration layer. This ensures that the human stays in control at any point in time.
We believe that the winning formula for banks, payment institutions, and fintechs is not “AI instead of people.” It is AI alongside experienced delivery professionals, robust engineering practices, and strong governance.
Final thoughts
AI should be used every day to accelerate thinking, analysis, software development, testing, documentation, and payments transformation.
But success comes from maintaining the fundamentals:
- Clear requirements
- Structured delivery
- Rigorous quality controls
- Human accountability.
In payments, where there is often little margin for error, the future belongs to organisations that can combine AI-powered efficiency with deep domain expertise and disciplined execution. These will not only see efficiency gains up to 80%, but also greater quality, payment applications that are easier to maintain and a large knowledge base that allows to onboard an train the next generation of payment delivery experts.
