Controllers deal with numbers all day long. They have to check a lot of data from different sources. Often the reports contain erroneous or missing data. Identifying outliers and suspicious data is time-consuming.

This presentation will introduce a Small Data Problem-End2End workflow using statistical tools and machine learning to make controllers' jobs easier and help them be more productive.

We will demonstrate how we used amongst others,
- scipy
- pandera
- dirty cat
- nltk
- fastnumbers
to create a self-improving system to automate the screening of reports and report outliers in advance so that they can be eliminated more quickly.