Using machine learning to curb fraudulent transactions

Fraud detection has become an increasingly critical challenge in sectors such as finance, e-commerce, and insurance, where the ability to process and analyze large datasets efficiently is essential. Machine learning (ML), a subset of artificial intelligence, offers a powerful approach to detecting fraudulent activities by learning from historical transaction data and identifying patterns indicative of fraud. This paper proposes a ML-based model for fraud detection, focusing on leveraging ensemble learning to combine two different models to address precision and efficiency of detection systems. The model aims to improve detection accuracy and reduce false positives in financial transactions. This study also highlights the potential benefits of leveraging singles Neural Networks and Random Forest which proven to slightly perform less than the proposed ensemble model. Through experimental validation, the proposed approach demonstrates its effectiveness in detecting fraudulent transactions and contributing to the broader goal of securing digital transactions.