loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Fabio Martinelli 1 ; Francesco Mercaldo 2 ; Domenico Raucci 3 and Antonella Santone 4

Affiliations: 1 Institute for Informatics and Telematics, National Research Council of Italy, Pisa, Italy ; 2 Institute for Informatics and Telematics, National Research Council of Italy, Pisa, Italy, Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy ; 3 Department of Economic Studies, G. d’Annunzio University, Chieti-Pescara, Italy ; 4 Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy

ISBN: 978-989-758-399-5

ISSN: 2184-4356

Keyword(s): Bank Credit Risk Management, Credit Risk Assessment, Probability of Default, Loan Repayment Prediction, Machine Learning, Classification, Association Rules, Data Mining.

Abstract: In last years, data mining techniques were adopted with the aim to improve and to automatise decision-making processes in a plethora of domains. The banking context, and especially the credit risk management area, can benefit by extracting knowledge from data, for instance by supporting more advanced credit risk assessment approaches. In this study we exploit data mining techniques to estimate the probability of default with regard to loan repayments. We consider supervised machine learning to build predictive models and association rules to infer a set of rules by a real-world data-set, reaching interesting results in terms of accuracy.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.232.55.103

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Martinelli, F.; Mercaldo, F.; Raucci, D. and Santone, A. (2020). Bank Credit Risk Management based on Data Mining Techniques. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-399-5 ISSN 2184-4356, pages 837-843. DOI: 10.5220/0009371808370843

@conference{forse20,
author={Fabio Martinelli. and Francesco Mercaldo. and Domenico Raucci. and Antonella Santone.},
title={Bank Credit Risk Management based on Data Mining Techniques},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,},
year={2020},
pages={837-843},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009371808370843},
isbn={978-989-758-399-5},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,
TI - Bank Credit Risk Management based on Data Mining Techniques
SN - 978-989-758-399-5
IS - 2184-4356
AU - Martinelli, F.
AU - Mercaldo, F.
AU - Raucci, D.
AU - Santone, A.
PY - 2020
SP - 837
EP - 843
DO - 10.5220/0009371808370843

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.