How AI-driven endpoint monitoring mitigates the risk of IT outages in financial firms

Nine major banks and building societies operating in the UK accumulated at least 803 hours - the equivalent of 33 days - of tech outages in the past two years, figures published by a group of MPs show. This recent spate of outages in banking apps and services has resulted in a push from  UK MPs, who are now demanding that banks and building societies disclose details on the scale and impact of IT failures that have affected their businesses and customers over the last two years. This demand highlights the importance of maintaining comprehensive data records, which are of sufficient breadth, depth, history, and quality, to accurately meet disclosure requirements.

As someone who tracks these disruptions with a mix of professional curiosity and concern, I see a clear pattern: despite technological leaps, these incidents aren’t going away. They expose a fragile underbelly in modern banking - and the stakes couldn’t be higher.

Modern financial institutions operate on incredibly complex technology stacks that manage millions of transactions daily. When any of those systems fail, the impact is immediately felt by consumers. Every customer interaction – whether it’s online or via an ATM – is important. If accounts freeze or payments stall, it's often because a critical endpoint has failed without the error being detected early enough. These incidents don’t just harm reputations; they signal a global problem and demand smarter tech to catch issues early.

Outages can stem from a variety of causes. Software glitches or hardware failures in core banking systems can lead to crashes. Scheduled maintenance, updates, or patches may not always go as planned, sometimes introducing new issues. Human error or a corrupted database can also disrupt operations. Network problems, often tied to internet service providers, can sever connectivity, while an influx of simultaneous users—each vying to access apps or technology—can overload the system, bringing it to a halt. The quality and historical depth of data collected by banks play a pivotal role in diagnosing these diverse causes, enabling quicker and more accurate root cause analysis.

Banks have robust disaster recovery plans, shaped by strict global regulations. Yet outages persist, sometimes breaching compliance rules. For instance, a glitch exposing customer data can violate privacy laws, amplifying the fallout. Minimising downtime and preventing knock-on effects are non-negotiable, but old-school monitoring - focused on, for example, server uptime - falls short. What’s needed is real-time insight into the customer’s experience across every digital touchpoint.

The AI fix: predict, don’t just react

Enter advanced AI monitoring. With comprehensive data analysis spanning a multitude of devices, regions and times, banks can pinpoint subtle warning signs, including sluggish APIs or fraying micro-services, before they escalate into major outages. Automated baselines flag odd patterns in transaction systems, while cross-channel analysis ties together seemingly unrelated hiccups. This isn’t just about detection; it’s about aiding prevention. The effectiveness of these AI simulations and pattern recognitions is directly dependent on the breadth and quality of the data that is being fed into the system.

When trouble hits, speed is everything. Endpoint monitoring tools deliver instant diagnostics—think real-time session replays, network traces, or code-level insights—pinpointing whether the issue is a rogue API, a third-party outage, or a hardware snag. Smart alerts, powered by machine learning, cut through the noise, escalating only what matters and syncing with incident response systems. Meanwhile, proactive testing ensures failover systems and backups aren’t just for audit reasons - they’re ready to be switched on quickly. Real-time diagnostics and smart alerts are ultimately driven by the depth of data available for analysis, which again highlights the importance of comprehensive data collection.

Monitoring third-party dependencies and supply chain integrations are all too often the blind spots in traditional monitoring approaches so financial organisations should ensure their end-user monitoring platform also monitors third-party dependencies and supply chain integrations.

In addressing the recent spate of outages within the banking sector, it is instructive to look at how some institutions are already ahead of the curve, utilising advanced technologies to pre-empt and resolve these disruptions. For instance, a large Midwest US bank successfully integrated our digital experience monitoring solution, which enabled proactive support and predictive insights into potential IT failures. This adoption allowed the bank to dramatically reduce help desk tickets and pre-emptively resolve issues that would otherwise have impacted end-user productivity​

Similarly, a major global financial institution experienced unplanned downtime and poor performance during a virtual desktop infrastructure (VDI) migration. By leveraging comprehensive data analytics and real-time monitoring, the bank was able to revise its approach, stabilise its systems, and successfully implement the VDI with improved performance and reduced total ownership costs. This proactive strategy not only optimised operations but also minimised the risk of future outages during similar migrations and updates. The successful implementation of these solutions relies heavily on the historical data available to train AI models and provide accurate predictive analytics.

The UK’s parliamentary demand for transparency is a positive step toward ensuring accountability. However, beyond explanations, banks need strategic partners who can help them transition to more resilient, future-proof data systems. This isn't just about fixing problems when they arise. It is about an architectural transformation and, as part of this, predictive monitoring assists banks in proactively identifying potential outages from occurring in the first place.

AI works closely alongside predictive monitoring to anticipate failures. It does this by pinpointing underlying issues and catching even faint signals of trouble and extrapolating that data to recommend fixes. AI systems can provide evidence that a bank took early action on emerging problems, which helps demonstrate responsible management to regulators. This proactive approach is only effective with high-quality data, allowing AI to accurately identify and predict potential issues. As transaction volumes soar, predictive monitoring using AI has scalable oversight capabilities, so it can maintain monitoring standards across expanding operations and ensure uniform control regardless of how much information there is.

The numbers back this up. The AI-driven predictive maintenance market is booming—from £0.7 billion in 2024 to £0.81 billion in 2025 (a 15.7% jump), and projected to hit £1.43 billion by 2029. Why? It tackles aging infrastructure, rising complexity, and customer-first priorities head-on. Regulators, too, are pushing this shift, expecting banks to wield AI-driven tools for stability and security. The growth of this market reflects the increasing reliance on data-driven AI solutions to maintain operational stability and meet evolving regulatory demands.

The financial sector stands at a crossroads. As regulators tighten their grip, demanding proactive stability and security, the push for smarter tech isn’t just inevitable - it’s urgent. AI-driven predictive maintenance and endpoint monitoring aren’t mere add-ons; they’re the backbone of a new era. Together, they dissect performance patterns and track networks in real-time, turning chaos into control. By embracing these resilient, future-proof systems, banks don’t just reduce the likelihood of outages - they prove to regulators and the public alike that responsibility isn’t a buzzword, but a battle won through foresight and innovation. The tools are here. It’s time to wield them.