Język Polski
English
Profile of the fraudulent customer
Anna Matuszyk
Aneta Ptak-Chmielewska
Abstrakt
When there is an economic downturn, financial crime proliferates and people are more likely to commit fraud. One of the most common frauds is when a loan is secured without any intention of repaying it. Credit crime is a significant risk to financial institutions and has recently led to increased interest in fraud prevention systems. The most important features of such systems are the determinants (warning signals) that allow you to identify potentially fraudulent transactions. The purpose of this paper is to identify warning signals using the following data mining techniques - logistic regression, decision trees and neural networks.
Proper identification of the determinants of a fraudulent transaction can be useful in further analysis, i.e. in the segmentation process or assignment of fraud likelihood. Data obtained in this way allows profiles to be defined for fraudulent and non-fraudulent applicants. Various fraud-scoring models have been created and presented.
Inne teksty tego samego autora
- Agnieszka K. Nowak, Anna Matuszyk, Wykorzystanie metody DEA do oceny efektywności banków komercyjnych , Bezpieczny Bank: Tom 46 Nr 1 (2012): Bezpieczny Bank
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Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Na tych samych warunkach 4.0 Międzynarodowe.