AUTHOR=Cerchiello Paola , Scaramozzino Roberta 

TITLE=On the Improvement of Default Forecast Through Textual Analysis

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=3

YEAR=2020

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2020.00016

DOI=10.3389/frai.2020.00016

ISSN=2624-8212

ABSTRACT=<p>Textual analysis is a widely used methodology in several research areas. In this paper we apply textual analysis to augment the conventional set of account defaults drivers with new text based variables. Through the employment of <italic>ad hoc</italic> dictionaries and distance measures we are able to classify each account transaction into qualitative macro-categories. The aim is to classify bank account users into different client profiles and verify whether they can act as effective predictors of default through supervised classification models.</p>