Intelligent Fraud Detection in Financial Transactions using Machine Learning Models

Abstract

The financial industry has a lot going on online, which brings up several problems. Fraudulent financial transactions are among the biggest concerns for individuals and financial institutions. Detecting fraudulent activity is crucial for banks and individuals. Traditional fraud detection systems do not effectively work with this large amount of transactional data, necessitating efficient fraud detection mechanisms for individuals and financial institutions worldwide. The financial industry has significantly reduced fraud due to the rise of technology and economic growth. This study shows that machine learning techniques have promising results in detecting fraudulent transactions. This paper proposes Parameterized Random Forest and other classifiers for fraud detection in financial transactions. We developed the Synthetic Minority Oversampling Technique (SMOTE)-Oversampling based machine learning model for monitoring financial transactions and detecting fraudulent activity. The proposed model uses Principal Component Analysis(PCA) to extract features from the transaction data and make predictions about the probability of fraud. We compare the suggested model's performance with other cutting-edge machine learning methods and assess its effectiveness on a dataset that is accessible to the general public. Our results show that the proposed model outperforms existing methods in accuracy and F1-score.