An article in partnership with CIO
Financial services engineering teams face a compounded challenge: modernising systems, meeting regulatory deadlines, and competing with challenger banks simultaneously. While AI tools like code suggestions have become standard, they plateau in value because they handle syntax rather than proprietary domain knowledge like rounding rules, and other financial logic that requires developer expertise. The real gap is the absence of feedback loops to catch subtle errors. Closing this gap requires treating AI as an active participant in the engineering ecosystem, reasoning about correctness within governed cycles of observation, revision, and validation.
The key to scaling this approach is structural discipline. OBSS’s “three-lane” model matches delivery strategy to project type: greenfield builds like open banking use AI-native scaffolding and spec-driven testing from day one; brownfield modernisation projects like payment systems rely on codebase comprehension and characterisation tests before changes; bluefield re-platforming efforts like core banking use parsing agents with output parity testing and incremental cutover strategies. This differentiation ensures consistent results yielding documented cycle time reductions and meaningful productivity gains across regulated environments.
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