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...

Descripción completa

Detalles Bibliográficos
Autores principales: Cerchiello, Paola, Scaramozzino, Roberta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861220/
https://www.ncbi.nlm.nih.gov/pubmed/33733135
http://dx.doi.org/10.3389/frai.2020.00016
_version_ 1783647038061150208
author Cerchiello, Paola
Scaramozzino, Roberta
author_facet Cerchiello, Paola
Scaramozzino, Roberta
author_sort Cerchiello, Paola
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7861220
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-78612202021-03-16 On the Improvement of Default Forecast Through Textual Analysis Cerchiello, Paola Scaramozzino, Roberta Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2020-04-07 /pmc/articles/PMC7861220/ /pubmed/33733135 http://dx.doi.org/10.3389/frai.2020.00016 Text en Copyright © 2020 Cerchiello and Scaramozzino. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Cerchiello, Paola
Scaramozzino, Roberta
On the Improvement of Default Forecast Through Textual Analysis
title On the Improvement of Default Forecast Through Textual Analysis
title_full On the Improvement of Default Forecast Through Textual Analysis
title_fullStr On the Improvement of Default Forecast Through Textual Analysis
title_full_unstemmed On the Improvement of Default Forecast Through Textual Analysis
title_short On the Improvement of Default Forecast Through Textual Analysis
title_sort on the improvement of default forecast through textual analysis
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861220/
https://www.ncbi.nlm.nih.gov/pubmed/33733135
http://dx.doi.org/10.3389/frai.2020.00016
work_keys_str_mv AT cerchiellopaola ontheimprovementofdefaultforecastthroughtextualanalysis
AT scaramozzinoroberta ontheimprovementofdefaultforecastthroughtextualanalysis

Ejemplares similares