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Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

M. Valavan, S. Rita*
Department of Statistics, Periyar University, Salem, Tamilnadu, India
* Corresponding Author: S. Rita. Email:

Computer Systems Science and Engineering 2023, 45(1), 231-245. https://doi.org/10.32604/csse.2023.026508

Received 28 December 2021; Accepted 06 April 2022; Issue published 16 August 2022

Abstract

Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence. In this paper, the rendering of different machine learning algorithms such as Decision Tree, Random Forest, linear regression, and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations. Further model accuracy metric have been performed with confusion matrix and calculation of accuracy, precision, recall and F-1 score along with Receiver Operating Characteristic (ROC )curves.

Keywords

Random forest; decision tree; logistic regression; machine Learning; gradient boosting method; confusion matrix

Cite This Article

M. Valavan and S. Rita, "Predictive-analysis-based machine learning model for fraud detection with boosting classifiers," Computer Systems Science and Engineering, vol. 45, no.1, pp. 231–245, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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