On the Improvement of Default Forecast Through Textual Analysis

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:
text analysis
credit scoring
default
classification models
finance
Online Access:https://www.frontiersin.org/article/10.3389/frai.2020.00016/full
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spelling doaj-2555df738a684442933e93f7e70243d92020-11-25T02:59:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122020-04-01310.3389/frai.2020.00016523672On the Improvement of Default Forecast Through Textual AnalysisPaola CerchielloRoberta ScaramozzinoTextual 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.https://www.frontiersin.org/article/10.3389/frai.2020.00016/fulltext analysiscredit scoringdefaultclassification modelsfinance
collection DOAJ
language English
format Article
sources DOAJ
author Paola Cerchiello
Roberta Scaramozzino
spellingShingle Paola Cerchiello
Roberta Scaramozzino
On the Improvement of Default Forecast Through Textual Analysis
Frontiers in Artificial Intelligence
text analysis
credit scoring
default
classification models
finance
author_facet Paola Cerchiello
Roberta Scaramozzino
author_sort Paola Cerchiello
title On the Improvement of Default Forecast Through Textual Analysis
title_short 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_sort on the improvement of default forecast through textual analysis
publisher Frontiers Media S.A.
series Frontiers in Artificial Intelligence
issn 2624-8212
publishDate 2020-04-01
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.
topic text analysis
credit scoring
default
classification models
finance
url https://www.frontiersin.org/article/10.3389/frai.2020.00016/full
work_keys_str_mv AT paolacerchiello ontheimprovementofdefaultforecastthroughtextualanalysis
AT robertascaramozzino ontheimprovementofdefaultforecastthroughtextualanalysis
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