Two Ways to Improve Banking Application Performance

Keith Bromley
Learn more about Keysight Technologies

The financial industry is experiencing a massive wave of change over the last several years. Digital disruption has been truly disruptive to this industry. Traditional banks face stiff competition from fintechs because these new competitors are more nimble, faster, and often have a different viewpoint that allows them to understand customer needs (especially from a user experience) better.

This includes not only the technology involved with conducting business, but also how to interact and service customers in this day and age. For instance, a mobile-centric world demands optimization of mobile applications and content delivery to provide the best possible customer experience. To this end, there are several ways to go about monitoring the network and its applications to collect the necessary performance data and deliver the requisite customer quality of experience.

One way is to use packet data. A copy of the data can be made and forwarded on to purpose-built tools (like network performance monitoring (NPM) and application performance monitoring (APM) appliances) for packet analysis. The flow of this type of monitoring data to these tools should be optimized using a network packet broker (NPB) which can filter, deduplicate, strip extraneous header information, and perform other useful tasks. This reduces the amount of non-relevant data being sent to the performance tools.

A second way to monitor the network is to look at flow data. In this scenario, application intelligence within a packet broker can be used to deliver key NetFlow-based data about the network to external performance monitoring tools. Some packet brokers can also deliver additional value-add features like geolocation, user device type, user browser type, etc. to aid with better application management and troubleshooting across the network.

By combining geolocation, user device type, and browser type metadata, it is easy to understand if issues exist on the network and where. This saves an exorbitant amount of troubleshooting time. Instead of trying to figure out if there is a problem, where it is located, and who is affected, application-level metadata can answer most, if not all, of those questions. Specifically, you can visually see that there is (or is not) an application problem, which application(s) are having issues, where (i.e. between which network segments) the issue(s) is occurring, and the affected user types.

In the end, better monitoring data allows you to enhance your customer experience. Here are some examples:

■ Better monitoring data improves the measurement of key performance indicators (KPIs) for mobile application success

■ The collection of monitoring data allows you to isolate application design problems and issues to improve user experience

■ Complete network traffic visibility can be accomplished to speed up application performance analysis

■ You now have easy access to data to perform application performance trending

■ The capture and documentation of user data helps improve the collaboration between IT and the lines of business responsible for specified mobile banking applications

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Two Ways to Improve Banking Application Performance

Keith Bromley
Learn more about Keysight Technologies

The financial industry is experiencing a massive wave of change over the last several years. Digital disruption has been truly disruptive to this industry. Traditional banks face stiff competition from fintechs because these new competitors are more nimble, faster, and often have a different viewpoint that allows them to understand customer needs (especially from a user experience) better.

This includes not only the technology involved with conducting business, but also how to interact and service customers in this day and age. For instance, a mobile-centric world demands optimization of mobile applications and content delivery to provide the best possible customer experience. To this end, there are several ways to go about monitoring the network and its applications to collect the necessary performance data and deliver the requisite customer quality of experience.

One way is to use packet data. A copy of the data can be made and forwarded on to purpose-built tools (like network performance monitoring (NPM) and application performance monitoring (APM) appliances) for packet analysis. The flow of this type of monitoring data to these tools should be optimized using a network packet broker (NPB) which can filter, deduplicate, strip extraneous header information, and perform other useful tasks. This reduces the amount of non-relevant data being sent to the performance tools.

A second way to monitor the network is to look at flow data. In this scenario, application intelligence within a packet broker can be used to deliver key NetFlow-based data about the network to external performance monitoring tools. Some packet brokers can also deliver additional value-add features like geolocation, user device type, user browser type, etc. to aid with better application management and troubleshooting across the network.

By combining geolocation, user device type, and browser type metadata, it is easy to understand if issues exist on the network and where. This saves an exorbitant amount of troubleshooting time. Instead of trying to figure out if there is a problem, where it is located, and who is affected, application-level metadata can answer most, if not all, of those questions. Specifically, you can visually see that there is (or is not) an application problem, which application(s) are having issues, where (i.e. between which network segments) the issue(s) is occurring, and the affected user types.

In the end, better monitoring data allows you to enhance your customer experience. Here are some examples:

■ Better monitoring data improves the measurement of key performance indicators (KPIs) for mobile application success

■ The collection of monitoring data allows you to isolate application design problems and issues to improve user experience

■ Complete network traffic visibility can be accomplished to speed up application performance analysis

■ You now have easy access to data to perform application performance trending

■ The capture and documentation of user data helps improve the collaboration between IT and the lines of business responsible for specified mobile banking applications

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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 ...

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Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

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