Fraud detection with machine learning: model comparison
by João Pacheco; João Chela; Guilherme Salomé
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 22, No. 4, 2023

Abstract: This work evaluates the performance of different models for predicting three types of fraudulent behaviour in a novel dataset with imbalanced data. The logistic regression model, a staple in the credit risk industry, is compared to several machine learning models. This work shows that in the binary classification case, all compared models achieved similar results to the logistic regression. The random forest model showed superior performance when classifying credit frauds ending in lawsuits. In the multi-label classification case, the logistic regression attains high levels of precision for all types of fraud, but at lower recall rates, whereas the random forest model achieves higher recall rates, but with lower precision rates.

Online publication date: Mon, 01-May-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com