user2016

During the last decade, data has changed the behaviors of individuals and corporations alike. On the latter, Advanced Analytics has gained considerable momentum – not only as a source of competitive advantage in the short-term, but also the risk of becoming obsolete in the medium-term. In this context, data scientists cannot offer solutions on data-reach problems without leveraging the opportunities of statistical learning with a tool like R, which allows to rapidly transform prototypes into useful solutions, thanks to the functional nature of R. To be specific we will focus on a particular and pressing issue across industries, geographies and organizations: collections & bad debt. We will show how machine learning algorithms leveraging R helped shape better solutions on a “millennial” problem (i.e. how am I getting paid back?) During this talk, we will show how, with the help of R as our main power horse, we approach a collection problem from its inception to the actual business implementation. First, we will describe how we can preprocess the data that may be useful for the purpose of predicting which customers are going to fail to pay their bills. Then, we will explore the relationship between the past payment behavior of a customer and his ability to satisfy future obligations. Finally, we will conclude sharing briefly how the output of a prediction model can be translated into effective business strategies using a project we have been involved on recently as an example.