Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

Abstract

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

“…The findings revealed that ANN performed well, with an F1 score of 0.91. Regarding banking transaction fraud detection context, Mytnyk et al [14] have compared seven machine learning models: RF, k-nearest neighbors (KNN), LR, stochastic gradient descent (SGD), decision tree (DT), naive Bayes (NB), and SVM on a transactional dataset. According to the findings of the various methods, the LR works better, yielding a final AUC value of around 94.6%.…”

Quality and Security of Critical Infrastructure Systems

“…The authors in [2] underscore the significance of leveraging artificial intelligence for the detection of fraudulent banking transactions. They introduce several classification algorithms applied to discern transaction types based on specific features.…”

Presenting a Model to Detect the Fraud in Banking using Smart Enabling Tools

“…In addition to detecting fraud and fraud in organizations, fraud detection techniques and intelligence tools aim to predict future behavior and reduce the risk of fraud by understanding user and customer behavior. Banks and financial institutions are striving to speed up the process of identifying fraudsters due to the high costs of fraud (1)(2)(3). It is against the law to commit fraud in electronic banking and electronic money transfer.…”