One of the main activities of a Data Scientist is the use of knowledge of a given domain (eg, processing credit card transactions) for the analysis of raw datasets (eg Credit), in order to identify features that increase the effectiveness and efficiency of computational learning algorithms.
Unfortunately, the essential tools to the performance of that activity are neither integrated nor optimized, being a time consuming and complex process involving a multitude of completely different tools and programming.
Complete platform for data modeling and analysis (data science), to be applied to fraud prevention activities, but without discarding other domains such as insurance or alternative payments. The goal is to drastically increase the productivity of data scientists, allowing at the same time data science's democratization by making it accessible to less specialized roles, such as business or fraud analysts.
Development of a complete and integrated solution for data modeling and analysis, using machine learning and Big Data techniques, with an emphasis on fraud prevention, but with applicability to other domains.