Bank Credit Risk Management based on Data Mining Techniques

Fabio Martinelli, Francesco Mercaldo, Domenico Raucci, Antonella Santone

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.

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Paper Citation


in Harvard Style

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, pages 837-843. DOI: 10.5220/0009371808370843


in Bibtex Style

@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},
}


in EndNote Style

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
AU - Martinelli F.
AU - Mercaldo F.
AU - Raucci D.
AU - Santone A.
PY - 2020
SP - 837
EP - 843
DO - 10.5220/0009371808370843