Financial Services solutions
Real-time Analytics for Financial Services
Beryl Analytics has spent the better part of a decade building real-time analytics systems for banks & fintech across New Zealand and Australia. We know which patterns generalise, which break, and how to ship value in weeks rather than quarters.
Why financial services teams choose Beryl Analytics for real-time analytics
- Deep-domain models. Every real-time analytics model we build is tuned to the realities of banks & fintech — not the synthetic benchmarks you see in vendor pitches.
- Production-ready, not throwaway. We ship pipelines, monitoring, alerting, and runbooks — the boring stuff that decides whether the system survives contact with reality.
- Operator-first design. Insights live inside the tools your team already uses, with thresholds and ownership matched to how decisions actually get made.
- Governance built in. Lineage, explainability, and access controls aren't an afterthought — they're scoped from day one and signed off with your security team.
- Outcomes measured in dollars. We track impact in revenue, cost avoided, or risk reduced — never in dashboard counts.
How we deliver real-time analytics engagements
- 01
Discovery sprint (week 1)
Two days on-site with your operators to map the workflow, half a day with leadership to align on the dollar metric, and an afternoon writing the scope memo we'll work to.
- 02
Spike the riskiest assumption (weeks 2-3)
Before committing to the build, we attack the assumption most likely to kill the project — usually data availability or operator adoption. A negative result here saves months.
- 03
Build, in public (weeks 4-8)
Daily commits to a shared repo your engineers can read. Weekly demo to the operator group. Nothing is built in private.
- 04
Production cutover (weeks 9-10)
A planned cutover with a rollback plan, monitoring, and a human in the loop for the first fortnight. We don't walk away from cold launches.
Frequently asked questions about Real-time Analytics for Financial Services
How long does a typical Real-time Analytics engagement take for a financial services business?
Most real-time analytics projects for banks & fintech land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger financial services programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Real-time Analytics project in financial services?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate real-time analytics with our existing banks & fintech systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of financial services-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Real-time Analytics engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Real-time Analytics engagements in financial services typically return 4-12x within the first year.
Do you work with financial services businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact real-time analytics engagements have been with regional banks & fintech.