Systems | Development | Analytics | API | Testing

Building a digital banking platform from scratch in 2026 is becoming less about launching a banking app and more about designing the right architecture from day one. The industry is moving through a major infrastructure shift. According to McKinsey Financial Services Insights, global fintech revenues crossed nearly 650 billion dollars in 2025, growing at roughly 21 percent year over year.

Version v0.2.0 of @ably/ai-transport reorganises the SDK to better support a wide range of interaction patterns. Everything in an AI session – input, output, agent lifecycle, control signals – is captured durably, allowing you to easily build the sophisticated interaction patterns that support modern AI user experiences. When we first built @ably/ai-transport, we modelled an AI conversation the way most people first picture it: as a request and a response.

If your team runs automated tests in Katalon Studio and relies on Jira test management to track defects, you already know the friction: a test fails, a tester screenshots the error, opens Jira, creates a ticket manually, pastes in the log, and attaches the file. Multiply that by dozens of failures per sprint, and you have a process that eats hours and invites human error. Katalon Jira integration eliminates that bottleneck entirely.

The gap between how fast organizations can generate software and how confidently they can validate it is becoming one of the defining operational risks of this era.

Here’s the uncomfortable truth. You don’t just choose a neobank technology stack, you commit to it. And that commitment compounds over time. In 2026, most fintech teams are no longer debating cloud native or API first, that part is settled. The real question is alignment. Does your architecture actually match your business model, your licensing path, and your scale ambitions? Because once you grow, changing your stack is not a simple rewrite.

Moving a retrieval-augmented generation (RAG) prototype from a Python notebook into production isn't an API orchestration challenge. It's a distributed systems problem. For engineering managers and data platform leads, the build-versus-buy decision on streaming infrastructure will dictate your artificial intelligence (AI) feature velocity for the next three to five years. This guide assumes you've already prototyped a RAG pipeline.

The EU AI Act's general provisions are already in force, and high-risk AI system obligations apply from August 2026. The National Institute of Standards and Technology (NIST) AI Risk Management Framework and its Generative AI Profile set the baseline for what auditors expect, framing governance around four functions: identify, measure, manage, and monitor. Deploying artificial intelligence (AI) agents in regulated environments isn't a sandbox experiment anymore. It's a strict governance challenge.

AI runs on data, and global enterprises are awash with petabytes of data. That might suggest that it’s easy for companies to advance their businesses through the power of AI. Yet enterprise data is often fragmented across departmental and technological silos, and that data is often inconsistent, ungoverned and disconnected from mission-critical systems. As a result, many AI initiatives stall before they can deliver operational value, and the root cause is rarely the model.

One line from Snowflake Summit 2026 stood out above everything else. Christian Kleinerman, EVP of Product at Snowflake: "We do not want any of you spending money with Snowflake, in any use case, if you are not getting more value in return." It's a refreshing commitment, and it points directly at the cost efficiency conversation we've been having with customers around open lakehouse architectures. Here's the core argument: data movement doesn't directly generate value.

This is the third post in our "Features Sitting Idle" series, where we shine a light on OctoPerf features that are already in your account but rarely used to their full potential. This is a blind spot many teams discover too late. Tests pass, metrics look fine, yet real users report slowness or errors after a release. The root cause is almost always the same: the load test was built against the HTTP protocol layer, but the user pain happens in the browser, above that layer.