Bank Credit Risk Management based on Data Mining Techniques

被引：1

作者：

Martinelli, Fabio ^{[1
]}

Mercaldo, Francesco ^{[1
,3
]}

Raucci, Domenico ^{[2
]}

Santone, Antonella ^{[3
]}

机构：

[1] Natl Res Council Italy, Inst Informat & Telemat, Pisa, Italy

[2] Univ G dAnnunzio, Dept Econ Studies, Chieti, Italy

[3] Univ Molise, Dept Biosci & Terr, Pesche, IS, Italy

来源：

ICISSP: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY | 2020年

关键词：

Bank Credit Risk Management; Credit Risk Assessment; Probability of Default; Loan Repayment Prediction; Machine Learning; Classification; Association Rules; Data Mining; KNOWLEDGE DISCOVERY; NEURAL-NETWORK; CLASSIFICATION; DATABASES;

D O I：

10.5220/0009371808370843

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：837 / 843

页数：7

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