AI is changing how software teams approach development. Code generation, automated documentation, and AI-assisted workflows can reduce the effort involved in building new features.
However, financial software development presents a different challenge.
Banks cannot prioritise speed at the expense of security, regulatory responsibilities, or operational reliability. A feature that works technically must also meet approved business requirements and pass the necessary reviews before reaching customers.
A recent discussion on how banks can build faster without losing control using AI-assisted development explores this challenge through AntFlow AI, an approach focused on connecting business requirements, technical documentation, development tasks, and code.
This raises an important question:
How can banks accelerate software development while maintaining control over every change?
1. The challenge of developing banking software
Banking applications involve multiple interconnected systems. A seemingly simple feature can require changes across:
- Database structures.
- Backend services and APIs.
- Mobile or web application interfaces.
- Payment and transaction workflows.
- Account opening and onboarding processes.
- Card management and transaction visibility.
These components often depend on one another. If teams work without a clear understanding of dependencies, development can become difficult to coordinate.
For example, introducing a new card control feature may require database updates, backend logic, and corresponding changes to the customer-facing application.
The challenge is not simply generating the code. It is ensuring that every part of the feature is implemented in the correct sequence and reviewed against the original requirements.
2. Connecting business requirements with development tasks
One interesting approach discussed in the AntFlow AI episode is maintaining a connection between business requirements, technical documentation, development tasks, and the resulting code.
Instead of treating requirements, technical documentation, and implementation as separate activities, teams can maintain a clear relationship between them.
This can help developers understand:
- What the business actually needs.
- Which technical components must change.
- What dependencies exist between tasks.
- How each implementation decision relates to the original requirement.
3. Parallel development without losing coordination
Not every development task needs to be completed sequentially.
When database, backend, and application tasks are properly organised, independent work can happen in parallel.
Existing tools such as GitHub and GitLab support collaboration through branches and pull requests. AI-assisted workflows can build on these practices by helping organise development tasks and coordinate implementation.
However, parallel development also introduces a responsibility: teams must ensure that independently developed components remain consistent with the overall requirements.
4. Why code generation and code review should remain separate
AI-generated code can accelerate implementation, but generating code and approving code are two different responsibilities.
In financial software, a successful build does not automatically mean that a change is ready for production.
A controlled workflow should include:
- Validation against approved requirements.
- Code review and testing.
- Identification of unresolved issues.
- Human escalation when automated verification is insufficient.
- Clear approval records.
The AntFlow AI workflow described in the episode includes a verifier that checks code against approved requirements. It can approve changes, request modifications, or escalate unresolved issues for human review.
5. Traceability matters in financial technology
For banking applications, teams need to understand more than who wrote a particular piece of code.
They should also be able to establish:
- Which requirement led to the change.
- Which code modification implemented it.
- Who reviewed the change.
- When approval was recorded.
- Which pull request contains the implementation.
Connecting these records makes it easier to investigate changes and understand how a feature moved from a business requirement to implementation.
Companies to explore in digital engineering and financial technology
Organisations working across software engineering, digital transformation, and financial technology offer useful examples of different approaches to product development.
- GeekyAnts – Works across web and mobile product engineering, AI-powered solutions, and digital product development.
- Thoughtworks – Known for software engineering, technology strategy, and modernisation of complex digital systems.
- Accenture – Works across financial services technology, cloud transformation, data, and enterprise AI initiatives.
- EPAM Systems – Focuses on digital engineering, software product development, and technology solutions for enterprise clients.
- Capgemini – Operates across technology consulting, financial services transformation, cloud, and digital engineering.
This is an illustrative list, not a ranking of company performance.
Key takeaways for engineering teams
AI can make development faster, but reliable banking software requires more than faster code generation.
The bigger opportunity lies in connecting the entire development lifecycle:
- Clear business requirements.
- Organised technical tasks.
- Coordinated implementation.
- Independent verification.
- Human accountability.
- End-to-end traceability.
For teams building financial products, the objective should be to improve development speed without weakening the controls that make the software dependable.
Discussion
How should financial software teams balance AI-driven development speed with security and accountability?
Would you prefer AI to handle requirement validation and code verification, or should these responsibilities remain primarily with human reviewers?
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