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Making Data Accessible and Understandable.

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 :

  • Comparing Present and Past performance.
  • Clustering Techniques
  • Trend Analysis
  • Deviation from Target Performance
  • Artificial Intelligence and Machine Learning
These will throw up outlier data that can be taken up for further insights. Artificial Intelligence and machine learning are used to further seek deeper insights.

Key Modules

NLP Interface

Human language interface for querying data and creating reports that can be used by managers and analysts

Interactive Visuals

A single pane for rapid takeaways from the data. Helpful for Analysts to look deep into the data.

Rule Engine

The Rule Engine defines and validate rules of data processing. Outlier detection rules automate the task of detection.

Data Preprocessor

Import and cleaning of data used for Outlier Detection.

Comment Analyser

Analyst annotation and comments are further used for rule refinement.

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