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Why the Financial Sector Should Adopt AIOps

Sean McDermott
Windward Consulting Group

Physical bank branches in the US could be extinct by 2034, according to a study funded by Self Financial Inc. While the permanence of physical banking is still up for debate, it's clear that digital banking is growing exponentially. In fact, 75.4% of Americans will use digital banking services by the end of 2021, and by 2025, this percentage will climb to an estimated 80.4%. Increasing digital transformation is even prompting some traditional financial institutions to proclaim themselves tech companies.

Many financial institutions are expanding online services to bolster their digital presence and keep pace with consumers' growing reliance on virtual banking. But the financial industry is still slow to deploy automation that will elevate their competitive advantage. According to Cornerstone Advisors, only 57% of banks and credit unions began digital transformations before 2021. And, of those at least halfway through their digital transformations, only 14% were using machine learning (ML).

Let's dig into why advanced automation — and AIOps in particular — is vital to staying competitive in today's financial sector.

AIOps as an Asset

Technology is now foundational to financial companies' operations with many institutions relying on tech to deliver critical services. As a result, uptime is essential to customer satisfaction and company success, and systems must be subject to continuous monitoring. But modern IT architectures are disparate, complex and interconnected, and the data is too voluminous for the human mind to handle.

Enter Artificial Intelligence for IT Operations (AIOps). AIOps tools leverage artificial intelligence to help SRE teams and DevOps practitioners monitor complex IT stacks, identify — and even predict — incidents and provide actionable insights into fixes.

Here are specific ways the power of AIOps can benefit the financial sector:

Enhance the customer experience. Today's consumers have no tolerance for downtime, and the stakes are exceptionally high when personal finances are involved. AIOps tools help prevent customer-impacting downtime by quickly finding problems within a system and determining their root cause. The resulting outcome is better service assurance and less mean time to remediation (MTTR), which leads to happier customers.

Defend against cybercrime. Roughly three-fourths (74%) of financial institutions in the US and U.K. experienced a rise in cybercrime from March 2020 to March 2021, according to BAE Systems Applied Intelligence. AIOps can help defend against cybercrime and their potentially devastating financial fallout for companies — falling stock prices, reputational damage and legal action on top of the known monetary losses. AIOps tools provide around-the-clock monitoring, rooting out suspicious behavior and instigating defense tactics to secure systems under attack.

Unlock time to innovate. While SREs and DevOps teams should develop innovative technology that delights customers and drives profitability, many operate in a constant reactive mode. Without the proper tools, teams can spend entire days drowning in incidents, putting out fires and falling short of their strategic goals. AIOps tools unlock time by automating mundane tasks, reducing noise, correlating events and raising system visibility to enable collaboration across teams.

One of my companies recently implemented an AIOps tool for a $100 billion global financial institution that found itself inundated with alerts. Prior to its AIOps adoption, the company relied on legacy monitoring tools to detect incidents. When an incident surfaced, upwards of 100 employees would bring their siloed data from their own disparate monitoring tools to cumbersome triage calls. In the meantime, time was ticking with the downtime causing mounting revenue losses and frustrated customers. But once the AIOps tool rolled out, MTTR fell by 40% in just six months, increasing valuable uptime and unlocking time to develop new customer-facing technologies.

As consumers embrace virtual experiences and the convenience of always-on digital services, digital banking will become more ubiquitous. But financial companies need to brace for this shift while addressing rising consumer expectations — for uptime and leading-edge technology — and broadening cybersecurity risks. AIOps is the key to addressing these challenges and gaining a competitive advantage in today's fiercely competitive financial sector.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

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Why the Financial Sector Should Adopt AIOps

Sean McDermott
Windward Consulting Group

Physical bank branches in the US could be extinct by 2034, according to a study funded by Self Financial Inc. While the permanence of physical banking is still up for debate, it's clear that digital banking is growing exponentially. In fact, 75.4% of Americans will use digital banking services by the end of 2021, and by 2025, this percentage will climb to an estimated 80.4%. Increasing digital transformation is even prompting some traditional financial institutions to proclaim themselves tech companies.

Many financial institutions are expanding online services to bolster their digital presence and keep pace with consumers' growing reliance on virtual banking. But the financial industry is still slow to deploy automation that will elevate their competitive advantage. According to Cornerstone Advisors, only 57% of banks and credit unions began digital transformations before 2021. And, of those at least halfway through their digital transformations, only 14% were using machine learning (ML).

Let's dig into why advanced automation — and AIOps in particular — is vital to staying competitive in today's financial sector.

AIOps as an Asset

Technology is now foundational to financial companies' operations with many institutions relying on tech to deliver critical services. As a result, uptime is essential to customer satisfaction and company success, and systems must be subject to continuous monitoring. But modern IT architectures are disparate, complex and interconnected, and the data is too voluminous for the human mind to handle.

Enter Artificial Intelligence for IT Operations (AIOps). AIOps tools leverage artificial intelligence to help SRE teams and DevOps practitioners monitor complex IT stacks, identify — and even predict — incidents and provide actionable insights into fixes.

Here are specific ways the power of AIOps can benefit the financial sector:

Enhance the customer experience. Today's consumers have no tolerance for downtime, and the stakes are exceptionally high when personal finances are involved. AIOps tools help prevent customer-impacting downtime by quickly finding problems within a system and determining their root cause. The resulting outcome is better service assurance and less mean time to remediation (MTTR), which leads to happier customers.

Defend against cybercrime. Roughly three-fourths (74%) of financial institutions in the US and U.K. experienced a rise in cybercrime from March 2020 to March 2021, according to BAE Systems Applied Intelligence. AIOps can help defend against cybercrime and their potentially devastating financial fallout for companies — falling stock prices, reputational damage and legal action on top of the known monetary losses. AIOps tools provide around-the-clock monitoring, rooting out suspicious behavior and instigating defense tactics to secure systems under attack.

Unlock time to innovate. While SREs and DevOps teams should develop innovative technology that delights customers and drives profitability, many operate in a constant reactive mode. Without the proper tools, teams can spend entire days drowning in incidents, putting out fires and falling short of their strategic goals. AIOps tools unlock time by automating mundane tasks, reducing noise, correlating events and raising system visibility to enable collaboration across teams.

One of my companies recently implemented an AIOps tool for a $100 billion global financial institution that found itself inundated with alerts. Prior to its AIOps adoption, the company relied on legacy monitoring tools to detect incidents. When an incident surfaced, upwards of 100 employees would bring their siloed data from their own disparate monitoring tools to cumbersome triage calls. In the meantime, time was ticking with the downtime causing mounting revenue losses and frustrated customers. But once the AIOps tool rolled out, MTTR fell by 40% in just six months, increasing valuable uptime and unlocking time to develop new customer-facing technologies.

As consumers embrace virtual experiences and the convenience of always-on digital services, digital banking will become more ubiquitous. But financial companies need to brace for this shift while addressing rising consumer expectations — for uptime and leading-edge technology — and broadening cybersecurity risks. AIOps is the key to addressing these challenges and gaining a competitive advantage in today's fiercely competitive financial sector.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...