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Is Better Release Management the Solution to the Persistent Banking App Downtime?

Joe Byrne
LaunchDarkly

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration.

This wasn't an isolated incident. In the first four months of 2025 alone, major high-street banks including Lloyds, TSB, Bank of Scotland, Nationwide and First Direct all experienced outages. When only 23% of Brits trust financial apps, the number is unlikely to improve if banking services remain vulnerable during critical moments.

The reality is mobile banking has become a core part of daily life. For many people, it's their primary way of managing money. Data shows that 37% of UK residents check their current account balance daily so repeated failures only weaken trust in apps.

So why are these failures still happening?

The short answer is that many banks are still relying on legacy systems that weren't built for the complexity of today's digital world and they're being pushed to their limits. These platforms must now support diverse devices, operating systems, third-party integrations, and cloud services. But without modern delivery practices, even routine updates can become high-risk deployments.

To prevent future outages and build more dependable digital services, banks need to rethink how they deliver and manage change. DevOps offers a practical framework for doing just that. There are four strategies that can help banks modernize their delivery approach and minimize disruption:

1. Start small, then scale

Rather than deploying a new feature or update to all users at once, changes are rolled out in controlled stages, starting with a small percentage and expanding only when stability is confirmed, and no further issues are detected. This is especially important for banks as with a staged approach they can check potential impacts before it hits the entire customer base.

2. Keep watch in real time

Teams need a clear view of the issue before they can respond and effectively address. Continuous monitoring and observability allow DevOps teams to detect abnormal system behavior immediately. When something does go wrong, automated rollback allows a fast return to the last known good state, minimizing user impact and preserving trust.

3. Stay agile under pressure

Not every problem needs a new build. Feature flags and runtime controls empower teams to make live adjustments without a full redeployment. That means if something breaks, it can be toggled off instantly, without bringing the app down for everyone.

4. Tailor updates to your audience

Customers use different devices and platforms, so why push identical updates to everyone?

Instead of pushing updates universally, banks can target specific groups to minimize disruption and gain clearer insights into performance across different environments.

Building Resilience Starts with Modern Delivery Practices

The Halifax outage may not be the last, but it should serve as a turning point for the industry. It highlights a clear urgent need for banks to rethink how they build and maintain the systems millions rely on daily. Legacy approaches to software delivery simply can't keep pace with modern demand, and they're putting customer trust at risk.

To meet the expectations of today's users, banks need the ability to move quickly, resolve issues in real time, and deploy changes safely. DevOps provides the mindset, practices and technology to make that possible, helping institutions avoid widespread disruption while continuously improving the customer experience.

Reliability is everything. Adopting DevOps isn't just about preventing the next outage. It's about building the foundations for a more agile, trustworthy, and future-ready banking sector.

Joe Byrne is Global Field CTO at LaunchDarkly

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Is Better Release Management the Solution to the Persistent Banking App Downtime?

Joe Byrne
LaunchDarkly

The development of banking apps was supposed to provide users with convenience, control and piece of mind. However, for thousands of Halifax customers recently, a major mobile outage caused the exact opposite, leaving customers unable to check balances, or pay bills, sparking widespread frustration.

This wasn't an isolated incident. In the first four months of 2025 alone, major high-street banks including Lloyds, TSB, Bank of Scotland, Nationwide and First Direct all experienced outages. When only 23% of Brits trust financial apps, the number is unlikely to improve if banking services remain vulnerable during critical moments.

The reality is mobile banking has become a core part of daily life. For many people, it's their primary way of managing money. Data shows that 37% of UK residents check their current account balance daily so repeated failures only weaken trust in apps.

So why are these failures still happening?

The short answer is that many banks are still relying on legacy systems that weren't built for the complexity of today's digital world and they're being pushed to their limits. These platforms must now support diverse devices, operating systems, third-party integrations, and cloud services. But without modern delivery practices, even routine updates can become high-risk deployments.

To prevent future outages and build more dependable digital services, banks need to rethink how they deliver and manage change. DevOps offers a practical framework for doing just that. There are four strategies that can help banks modernize their delivery approach and minimize disruption:

1. Start small, then scale

Rather than deploying a new feature or update to all users at once, changes are rolled out in controlled stages, starting with a small percentage and expanding only when stability is confirmed, and no further issues are detected. This is especially important for banks as with a staged approach they can check potential impacts before it hits the entire customer base.

2. Keep watch in real time

Teams need a clear view of the issue before they can respond and effectively address. Continuous monitoring and observability allow DevOps teams to detect abnormal system behavior immediately. When something does go wrong, automated rollback allows a fast return to the last known good state, minimizing user impact and preserving trust.

3. Stay agile under pressure

Not every problem needs a new build. Feature flags and runtime controls empower teams to make live adjustments without a full redeployment. That means if something breaks, it can be toggled off instantly, without bringing the app down for everyone.

4. Tailor updates to your audience

Customers use different devices and platforms, so why push identical updates to everyone?

Instead of pushing updates universally, banks can target specific groups to minimize disruption and gain clearer insights into performance across different environments.

Building Resilience Starts with Modern Delivery Practices

The Halifax outage may not be the last, but it should serve as a turning point for the industry. It highlights a clear urgent need for banks to rethink how they build and maintain the systems millions rely on daily. Legacy approaches to software delivery simply can't keep pace with modern demand, and they're putting customer trust at risk.

To meet the expectations of today's users, banks need the ability to move quickly, resolve issues in real time, and deploy changes safely. DevOps provides the mindset, practices and technology to make that possible, helping institutions avoid widespread disruption while continuously improving the customer experience.

Reliability is everything. Adopting DevOps isn't just about preventing the next outage. It's about building the foundations for a more agile, trustworthy, and future-ready banking sector.

Joe Byrne is Global Field CTO at LaunchDarkly

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...