Materialize for Financial Services
Eliminate tedious custom streaming solutions, remove delays, and support up-to-the second actions.
Fraud and Anomaly Detection
Detect fraud on large datasets at extremely low latencies - and easily adjust models as needed in SQL.
Work At Sub-second Latency
Support any application or use case that needs sub-second latency.
Scale To Your Largest Use Case
Use Materialize’s cloud architecture to scale your applications and analytics up or down with ease.
Operationalizing Ramp's Data with Materialize
THE CHALLENGE
Ramp's Data Platform team needed to deliver capabilities like fraud detection and account takeover prevention that typically require stream processing without forcing analysts and operations teams to deal with the complexity of stream processors.
THE RESULTS
Real-Time ATO alerts powered by Materialize have already stopped multiple waves of account take-over attempts, saving Ramp hundreds of thousands of dollars a year in loss and creating a more secure experience for Ramp customers.
- 50% Of hacked accounts flagged during a recent ATO before fraudsters could spend money.
- 60% Reduction in total monetary loss compared to previous ATO events
Why Build with Materialize
Consistent, Reliable, and Responsive
Materialize was architected from the beginning to be responsive, minimizing the time between queries and completion. It is always up to date, immediately reflecting updates without the complexity of micro-batches. Perhaps most importantly, it is consistent, always delivering the right data in the way you expect it.
Scale With Your Business
Respond to changing demand and elastic use cases by scaling up in an instant. Plus, you’ll only pay for what you use.
Deploy and Work With Ease
Materialize was built as an easily deployable cloud solution with standard-SQL at the heart. Get going quickly and put the power of real-time into the hands of anyone who understands SQL.
See Materialize in Action
See how Materialize helps financial services companies to trust and action on their operational data.