Owl Automatically Alerts to Incorrect FX Rate Data without a Single Rule. There are roughly 28,000 currency pairs and the exchange rates change throughout the day but at a minimum most banks are concerned with the daily close of the FX Rate. Now imagine trying to write a rule for each currency pair.
One of our favorite feautres was the ability to create thousands of granular ML models with just a few clicks.
It is common for financial organizations to receive a steady stream of files that have hourly or minutely data. The files might trail the market in a near real-time fashion. Pre-built analytics and OwlDQ Coverage for Position data can be applied out of the box. (Schema Evolution, Profiling, Correlation Analysis, Segmentation, Outlier Detection, Duplicate Detection, Pattern Mining)
Before OwlDQ we were relying on just row counts and column checks. OwlDQ enables us to do more with less. Pre-built rules, dashboards, and analytics replace custom work in Excel, Tableau, and Oracle.
In no uncertain terms, critical business decisions rely on the accuracy of this data.
Given the interconnected, automated nature of the data generated by reporting, exchanges, and source systems - hidden patterns go unnoticed.
Financial firms of all shapes and sizes ingest reference data for a variety of reasons. Bloomberg, Thomson Reuters, ICE Data Services or SIX Financial Information are just a few.
OwlDQ allowed us to reduce false positives, scale coverage, and quickly model a more complex series of checks than domain experts would want to develop.