Near-real-time operational visibility for a digital bank
Up Bank · Operations · digital banking
Shipping product at high velocity strained operational tooling: answering straightforward customer questions meant jumping across systems and chat threads.
We wired operational data into a single near-real-time layer and dashboards tailored for support and product, with guardrails so teams trust what they see.
Fewer handoffs, clearer ownership, and faster paths from signal to resolution.
32% fewer repeat contacts for the same issue category.
Context
Digital-native banks win on product iteration—but every release adds edge cases for support: new error states, partner API behaviours, and card or payment flows that do not surface cleanly in a single console.
When frontline staff and engineers each look at different partial views, the same customer issue gets re-diagnosed multiple times. Repeat contacts rise, CSAT suffers, and product teams lack a trustworthy operational picture tied to version and cohort.
Problem framing
The organisation did not need “more dashboards” in the abstract; it needed a consistent operational record that joined customer touchpoints, back-end events, and release context so first-line teams could answer “what happened” without a multi-team chase.
We prioritised categories with the highest repeat-contact rates and the longest mean time to understand—usually payment and partner-integration paths—so early wins would be visible to customers and measurable in ops metrics.
What we built
We landed near-real-time event and case data into a curated layer with clear retention and access rules. On top of that we built role-specific views: support leaders see queues, hotspots, and error families; product and engineering see cohorts tied to app versions and feature flags where available.
Guardrails included anomaly alerts when error rates stepped outside baselines, and explicit labelling when data was delayed or sampled—so teams did not over-trust a stale tile during an incident.
Outcomes
Repeat contacts for targeted issue categories fell as first-contact resolution improved—our reference metric is 32% fewer repeats in the same category bucket quarter-on-quarter after rollout, alongside qualitative feedback that escalations carried complete context.
Product and ops began referencing the same operational snapshot in incident reviews, which shortened the loop from customer signal to engineering prioritisation.
Lessons for fast-shipping teams
Invest in a single operational spine before layering AI on top of chat; tie views to release and cohort metadata wherever possible; and measure repeat contact and handle time—not just average response time—when judging success.
“Our frontline finally has the same picture as the people building the product—and we resolve issues before they echo.”
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