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](pypi.org/project/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.