Only the most fundamental automation is available in the Finance Department of any given company. They’re tasked with managing massive amounts of data that are only expected to grow. The majority of an analyst’s time is spent on administrative tasks like data collection and report writing, leaving little room for actual analysis and insight development.
Outlier Detection is a problem that lends itself well to automation. We can apply a number of Techniques such as :
Human language interface for querying data and creating reports that can be used by managers and analysts
A single pane for rapid takeaways from the data. Helpful for Analysts to look deep into the data.
The Rule Engine defines and validate rules of data processing. Outlier detection rules automate the task of detection.
Import and cleaning of data used for Outlier Detection.
Analyst annotation and comments are further used for rule refinement.
