Ai-Driven Fraud Detection in Financial Transactions - Using Machine Learning and Deep Learning to Detect Anomalies and Fraudulent Activities in Banking and E-Commerce Transactions

Md Mainul Islam; Ismoth Zerine; Md Arifur Rahman; Md Saiful Islam; Md Yousuf Ahmed

Author(s)

Md Mainul Islam College of graduate and professional studies, Trine university, Angola, Indiana, USA.

Ismoth Zerine College of graduate and professional studies, Trine university, Angola, Indiana, USA.

Md Arifur Rahman College of graduate and professional studies, Trine university, Angola, Indiana, USA.

Md Saiful Islam College of graduate and professional studies, Trine university, Angola, Indiana, USA.

Md Yousuf Ahmed College of graduate and professional studies, Trine university, Angola, Indiana, USA.

Abstract

Financial fraud has become a growing concern with the rapid digitalization of banking and e-commerce platforms. Detecting fraud effectively is challenging due to the high imbalance between legitimate and fraudulent transactions. This research addressed the issue by analyzing publicly available datasets using AI-based techniques to identify patterns and indicators of fraudulent activity. The significance of the study lies in its potential to support financial institutions in minimizing fraud losses and improving transaction security without developing new models. The main objective was to evaluate and compare the performance of machine learning (ML) and deep learning (DL) methods in detecting fraudulent transactions using real-world data. Three datasets were used: the European Credit Card Fraud Dataset, PaySim (a mobile money simulator), and a dataset from the UCI Machine Learning Repository. The methodology included data cleaning, balancing using SMOTE, feature engineering, and statistical analysis. Algorithms such as Logistic Regression, Random Forest, XGBoost, and Neural Networks were applied. Model performance was assessed using accuracy, precision, recall, F1-score, and AUCROC. The results showed that XGBoost achieved the highest performance with an accuracy of 99.2%, precision of 96.8%, recall of 94.5%, F1-score of 95.6%, and AUC-ROC of 0.987. Deep learning models also performed well, especially on large datasets, but required more computational power. In conclusion, ML models, particularly XGBoost, were highly effective in detecting fraudulent transactions. The main finding of this research is that AI-based techniques can significantly enhance fraud detection without complex model development. This study supports the practical implementation of existing AI methods to improve financial security and reduce fraud-related losses.

Publication Details

Page(s) 270-290

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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Keywords

machine learning, deep learning, Anomaly Detection, XGBoost., Fraud Detection

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