Institutional Sector Cassifier, a Machine Learning Approach

Abstract

We implement machine learning techniques to obtain an automatic classification by sector of economic activity of the Italian companies recorded in the Bank of Italy Entities Register. To this end, first we extract a sample of correctly classified corporations from the universe of Italian companies. Second, we select a set of features that are related to the sector of economic activity code and use these to implement supervised approaches to infer output predictions. We choose a multi-step approach based on the hierarchical structure of the sector classification. Because of the imbalance in the target classes, at each step, we first apply two resampling procedures – random oversampling and the Synthetic Minority Over-sampling Technique – to get a more balanced training set. Then, we fit Gradient Boosting and Support Vector Machine models. Overall, the performance of our multi-step classifier yields very reliable predictions of the sector code. This approach can be employed to make the whole classification process more efficient by reducing the area of manual intervention.