When was the last time you walked into a bank to withdraw cash? And how often do you balance your checkbook? These once routine manual processes are now primarily digital, even leading some financial giants to proclaim themselves tech companies.
While many are keeping pace with consumers’ demands for digital services, few organizations are implementing the advanced automated technologies that will help them stay competitive in today’s digital era. Just over half (57%) of banks and credit unions started their digital transformations before this year, according to Cornerstone Advisors’s “What’s Going on in Banking 2021.” And the survey indicates that a mere 14% of financial institutions (that are at least halfway through their digital transformations) have implemented machine learning tools.
But what does machine learning have to do with the financial sector?
Benefits of AIOps in the Financial Sector
AIOps, or artificial intelligence for IT operations, uses big data analytics, machine learning and automation to simplify how IT operations teams support and manage modern, decentralized IT environments. By automating mundane tasks, providing actionable insights, and predicting outages, AIOps tools help increase system performance and uptime.
In an industry where IT is no longer a support function but the foundation behind how services are delivered, service assurance is core to a company’s success. And, in today’s complex IT architectures, AIOps tools are the only path toward continuous service assurance.
Let’s dive into some of the innovative ways AIOps can help financial institutions compete in today’s digital economy:
1. Deliver a superior customer experience. “Customer experience” used to be synonymous with “customer service,” but this definition has changed with the shift toward digitized financial services. Today, technology is the backbone of the customer journey, and the number of system errors and amount of downtime shape the entire customer experience. AIOps tools help IT teams mitigate service-impacting issues by identifying incidents and providing actionable insights for quick fixes. This reduced downtime is critical in the financial sector as there could be severe repercussions for customers that can’t access their online bank accounts.
2. Optimize operational efficiency. Streamlining internal operations is critical considering the world’s largest companies like Amazon, Google and Facebook are creeping their way into the financial services game. AIOps can help traditional players remain competitive by tightening their belts. With a properly orchestrated system, AIOps can detect anomalies that catch money laundering and other fraudulent activities. And these tools can automate IT teams’ low-level tasks, unlocking time to focus on high-value tasks like innovating new technologies that provide real business value.
3. Mitigate increasing cyberattacks. Because financial enterprises manage sensitive customer information, malicious actors will continue to target these companies with increasing and increasingly sophisticated cyber-attacks. And the stakes are high -- companies experiencing breaches face sinking stock prices, fleeing customers, significant monetary losses and even legal action. AIOps is moving into the cybersecurity space as these tools can help provide 24/7 monitoring of ever-complex financial systems, detect signs of a cyberattack (rather than a run-of-the-mill IT issue), and trigger a process to defend the system against bad actors.
Use Case: Financial Company Embraces AIOps
My company helped a $100 billion global financial institution flooded with alerts decommission its legacy platform and harness an advanced AIOps event management tool. Before the company implemented AIOps and the old monitoring platform detected an incident, operations support would host unwieldy triage calls that could include up to 100 employees. The teams on these calls lacked a single source of truth or machine learning capabilities, so they would look at their own disparate monitoring tools and siloed data. These disjointed tools, manual processes and data silos caused a slow mean time to resolution (MTTR), and the business lost significant revenue.
When our team implemented AIOps capabilities, the financial institution reduced its MTTR by 40% in the first six months, meaning greater availability of customer-facing services and more revenue for the business. AIOps is just scratching the surface of optimizing operational efficiency but has already reduced the company’s tool footprint by more than 50%, saving millions of dollars in licensing fees and lowering the cost of maintenance and operations of these tools.
With rising customer expectations, fierce competition, and growing cybersecurity concerns, companies in the financial sector need to increase attention on advancing their digital transformations and making investments in automated technologies like AIOps. These tools will provide a competitive advantage in delighting customers, streamlining internal processes, and fighting cyberattacks.