Improving IT Reliability Reporting for a Financial Institution

Overview

A mid-to-large financial institution (obscured name) ran a hybrid set-up with on-prem core services and cloud-native digital banking apps as well as a wide area network that covered branches, call centres and home office staff - but its reliability reporting system was the weak link : teams had plenty of monitoring tools and a whole load of data, yet they couldn't agree on what was meant by 'reliable' because their monthly service reliability reports were manually cobbled together from a load of different providers and teams. Here's what had been going on - each team (app, network, infrastructure) was producing their own reliability KPIs that painted slightly different pictures, and by the time executives got their regular updates they were often late, a bit too techie for them and frequently up for discussion rather than action. Leadership were crying out for a standardised, auditable reliability score that worked across the lot - apps, network, cloud and on-prem - and gave them clear drivers, trends and accountability, without any of the tech speak.

Solution Overview

This financial institution implemented Scout-itAI - a cloud-based Event Intelligence Service that translates complex telemetry into plain English and tells the business what to do.

The centrepiece of the rollout was the RPI-Index which provides a unified Reliability Path Index score for each critical service. This enables teams to measure reliability consistently, rather than debating definitions.

What changed in practice

Scout-itAI gave the business a reliability operating model that they could really work with:

One single reliability KPI per service: that is, the RPI score - designed to tell you how reliable a service actually is.
Explainable drivers: the RPI score breaks down which bits have had an effect on overall performance (this is shown as a percentage of total impact).
Predictive reliability analysis: the team used forecasting to predict how much of an impact specific fixes would have on future reliability - so they could have a sensible conversation about whether the investment was worth it.
Reliable reporting: weekly and monthly reports were written up for IT leaders and executives that were all about business service reliability - they didn't need to spend time poring over raw numbers.

Architecture

Scout-itAI was layered on top of the institution's existing monitoring set-up - no wholesale replacement. It uncovered all the hidden signals, filtered out the noise and produced a single reliability dashboard that worked for both day-to-day troubleshooting and continuous improvement work.

Existing telemetry sources: gets data from APM (performance tracing), network monitoring (latency/jitter/packet loss), infrastructure and cloud metrics, logs/events/alerts, and on-prem and multi-cloud set-ups.
Scout-itAI Event Intelligence Service (EIS): normalises all the signals from the end-to-end service path, then calculates a single Reliability Path Index (RPI score) using the RPI Index / RPI-Index 13 bucket model. Adds trend tracking over a 100 days, predictive reliability scoring (predicts RPI after you've fixed whatever was broken) and gives you clear, plain language insights from GenAI along with some guided root cause analysis.
Reliability outputs: gives the business a reliability dashboard, weekly/monthly reporting, top drivers analysis and reliability impact analysis and integrates with their existing alerts and dashboards.

Results

Within their initial rollout window, the financial institution moved from debating reliability to actually acting on it. Before Scout-itAI, reporting was a manual exercise that was late and confusing and often argued over because different teams (apps, network and infrastructure) were relying on different KPIs and different tools. After implementing the Scout-itAI RPI Index, reporting became standardised and reliable (ha!) with one clear RPI score (Reliability Path Index) per service - and one that both IT and business stakeholders could understand.

Operationally, root cause analysis improved because teams stopped jumping between different tools and started using what really mattered - the RPI scores and the 'top drivers' that were impacted to pinpoint what was really impacting service reliability. Executive updates also became clearer and more consistent as they were based on plain English reliability and performance insights. And finally, when it came to planning improvements, teams could justify their priorities because they could forecast exactly how much of an impact specific fixes would have on future reliability scores - so they could track continuous reliability improvements across application, network, cloud and on-prem environments with much more confidence.

Lessons Learned

A shared reliability language beats more dashboards. If your reliability reporting still depends on manual rollups, competing dashboards, or “SLA theater,” you’re not missing data, you're missing a reliability language everyone can share.
One score, clear drivers, faster action. Scout-itAI delivers that language through the Reliability Path Index (RPI Index / RPI-Index) and plain-language reliability & performance insights so you can measure business service reliability in real time, explain the “why,” and forecast the impact of fixes before shipping.
Looking to see what Scout-itAI can do for you? Book a demo and take a closer look at the Scout-itAI RPI Index.