The banking, financial services and insurance (BFSI) industry has seen more transformation in the last decade than it had over the last several centuries of its existence – and the pandemic has only accelerated it further. The fundamental difference today is that digital technologies (IT) are no longer a “support function” but have become the foundation for how the service is delivered to the customer.
With fierce competition from digital native players, growing cybersecurity concerns, tightening compliance protocols and rising customer expectations, BFSI companies need to seek new and innovative ways to compete. As a result, increased attention has been put on improving operational efficiency, eliminating errors and downtime and delivering a stellar customer experience.
Here are five ways in which leveraging artificial intelligence for IT operations (AIOps) can help companies strengthen their competitive advantage.
1. Getting transactions first-time-right
One of the key reasons for transaction failures is downtime and delays in page load. Customers are bound to abandon a transaction if it fails or takes too long to complete. Unless the purchase is essential, they are unlikely to return later to complete the transaction. So, poor transaction response time is a major impediment to a bank or financial institution’s performance.
AIOps maximizes transaction success by identifying system weaknesses before a problem occurs. For instance, a good AIOps engine can anticipate mass failures that may arise from unexpected volume surges and prevent them, so that there is no disruption of service. AIOps can also help identify patterns in the performance of tools outside your own landscape such as downtimes/delays in partner systems. This way, you can choose the right partners or even help existing partners upgrade their systems.
2. Solving problems autonomously
Monitoring is not an end goal, as it was once believed to be. Even the best monitoring tools today simply send a storm of alerts for IT teams to perform root-cause analysis (RCA) and repair manually. This causes downtime of the machine and alert fatigue in team members. In the BFSI industry, where it is mandated by law to be watchful of concerns, alert fatigue can result in critical incidents falling through the cracks.
AIOps eliminates much of the manual intervention by analyzing data contextually, performing RCA and autonomously remediating problems. This reduces mean time to identify (MTTI) problems and resolve them. In fact, a good AIOps tool can predict concerns and address them even before they occur.
3. Breaking down information siloes
Every large business has a sprawling toolkit today. While these tools help solve the problem at hand, they end up in information siloes obstructing organizational efficiency in the long run. Even within interconnected applications, it becomes difficult to locate points of failure as they reside in heterogenous environments.
Acting as an intelligent monitoring center, AIOps can help break the siloes by interpreting complex data from different sources to give a bird’s eye view of operations. It can also handle data in various formats from multi- or hybrid-cloud environments to effortlessly make sense of enterprise chaos.
4. Enabling scale
Until the last decade, worldwide scale was a strength for banks. Today, however, in the world of tech-powered banking, scale has become a burden on the agility and responsiveness of the institution. Common hurdles include:
- Infrastructure and applications struggling to dynamically scale to meet customer needs
- Current data systems unable to deliver personalization at scale
- Large cloud workloads and hundreds of third-party integrations creating susceptibility to malware and giving rise to new security threats
AIOps can ease the overwhelm that comes from scale through real-time visibility into points of congestion, whatever the workload size. It can identify and isolate security concerns, perform root-cause analysis and enable autonomous remediation. In fact, artificial intelligence and machine learning (AI/ML) models become better-trained and more accurate with every subsequent dataset they process, dynamically preparing to handle more and more scale.
5. Automating regulatory compliance
The financial services industry is among the most regulated in the world. Even the simplest compliance failure can invite huge fines, penalties and may even be cause for the revoking of licenses. However, at the scale of operations today, it is almost impossible to manually ensure compliance.
AIOps can help process large tracts of data for compliance reporting. It can compare such data against enterprise/regulatory standards to identify anomalies and take remedial action. It can also be trained to identify compliance gaps in real-time and flag them for action.
For the BFSI industry, adoption of digital technologies is critical to growth, even survival. But adoption is just the first step. To grow, BFSI players need to monitor, manage and leverage their digital tools. You need to turn your digital investments into your competitive advantage. AIOps can help with that.