Emerging Approach for Detection of Financial Frauds Using Machine Learning

Abstract

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

A systematic review of literature on credit card cyber fraud detection using machine and deep learning

“…The DT algorithm appeared in 49 articles. In Bandyopadhyay et al (2021) , the DT classifier applied for detection of financial frauds. DT algorithm performs the best with an accuracy of (0.99) comparing with another classifier.…”

“…In ( Arun & Venkatachalapathy, 2020 ) Kappa assesse the predication performance of the classifier model. Few article ( Arya & Sastry, 2020 ; Bandyopadhyay et al, 2021 ; Bandyopadhyay & Dutta, 2020 ; Benchaji, Douzi & El Ouahidi, 2021 ; Rezapour, 2019 ) introduced mean square error (MSE) assessment metrics, mean absolute error (MAE), and root mean square error (RMSE). Table C1 shows the proposed ML/DL model along with performance and datasets.…”