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AI-powered visibility key to mitigating IT systems risk in financial services

AI-powered visibility key to mitigating IT systems risk in financial services 1

By Penelope Feros, Vice President, APAC, Cherwell Software

The world of finance changes by the minute. Financial businesses — from banks to insurance agencies and brokerages — are facing a myriad of macro challenges that come along with modernisation.

The rise of fintech and challenger banks make the landscape more competitive than ever, putting pressure on the ingenuity of offers, fees, and net interest margins (NIMs). Today’s mostly online banking means an increased need for cybersecurity and protection against data breaches and costly outages.

Automation is no longer a futuristic option for the workplace, but now, a reality that also impacts the customer experience. Disruptive technologies like AI to address the exponential rise in data and complexity in finserv organisations is primed to grow in significance across IT teams in the region.

According to IDC, financial services spending on AI in Asia Pacific will reach US$4.29 billion in 2024, with Australia making significant traction and advancements in AI spending, alongside key financial hub markets Singapore and Hong Kong.

Rising system vulnerabilities within complex IT environments

Digital transformation has resulted in IT infrastructure complexity growing at an astronomical rate. The resultant increases in infrastructure data and alarms at the service desk far exceed the capacity of any human to meaningfully read, analyse and respond to them. The infrastructure itself is also constantly morphing and changing, yet finance service desks are still expected to resolve requests, incidents, and performance issues in seconds – an impossible task, given the volume of data.

Penelope Feros

Penelope Feros

Financial brands are expected to maintain a pristine reputation while making sure all business-critical applications perform optimally, in order to stay competitive and deliver a better service experience.

Every application is supported by a complex fabric of servers, network devices and services – both physical and virtual – local and in the cloud. The impact of an outage or vulnerability for any one of those components can be extreme. IT teams need to monitor not just every device, but also the dependencies between them. Understanding all of the pieces that make an application available is about more than knowing the up or down status.

With security vulnerabilities putting customer data at risk, having awareness of the priority systems that need to be secured is also vital. As the architectures behind applications become even more complicated with the cloud, virtualisation and shared services, manual documentation of dependencies is no longer a feasible option.

AIOps implementation begins with discovery and analysis

The good news is that the solutions available to automatically discover the organisation’s systems and map dependencies are much more sophisticated today.

Discovery and dependency mapping (DDM) tools enable organisations to see how physical, virtual, and logical compute, network, and storage entities are connected. They can handle the complexity of distributed hybrid environments, giving finserv IT teams the opportunity to visualise and manage the components of their online retail, supply chain, ERP and other critical applications.

The discovery phase involves uncovering all of the compute, network and storage entities across the IT environment and ensuring the organisation’s configuration management database (CMDB) is always kept up to date.

DDM tools depict the interdependencies in a graphical format, so IT teams can readily see the connections between assets, and which services they support – critical to troubleshooting incidents or preventing impact of change.

Once the CMDB is kept up to date with automated discovery and dependency mapping, finserv organisations need to make sense of the data in order to make informed decisions. By applying machine learning algorithms to analyse this data, AIOps can identify patterns, spot anomalies and predict (and prevent) future outages. The resulting insights can trigger intelligent automations to effectively prevent outages, improve performance, and ease the burden of managing increasingly complex infrastructure. By automatically triggering actions based on insights, AIOps can quickly fix issues or prevent them from happening, whether it’s a network link that’s gone down, an over-utilised disk or a service that simply requires a restart.

With automatic discovery and dependency mapping, AIOps allow financial services organisations to, gain visibility over IT environments, rapidly evaluate changing needs, and the means to quickly meet them. Adoption of the right solutions leveraging AI can help IT operations in financial services organisations make sense of the growing volumes of data and get on top of increasing risks of outages.

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