On the Improvement of Default Forecast Through Textual Analysis

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 ad hoc dictionaries and distance measures we are able to classify each...

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Bibliographic Details
Main Authors: Paola Cerchiello, Roberta Scaramozzino
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-04-01
Series:Frontiers in Artificial Intelligence
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/frai.2020.00016/full
Description
Summary: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 ad hoc 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.
ISSN:2624-8212