Enhancing Financial Fraud Detection Using Machine Learning Algorithms: A Comparative Analysis of Random Forest, Gradient Boosting, and Deep Neural Networks

Abstract

Background: Financial fraud has become increasingly sophisticated with the growth of digital banking and online transactions. Traditional rule-based fraud detection systems struggle to identify complex and evolving fraud patterns, highlighting the need for advanced machine learning techniques.

Objective: This study aims to evaluate the effectiveness of different machine learning algorithms in detecting fraudulent financial transactions and to identify the most accurate and efficient model for fraud detection.

Methods: A comparative experimental study will be conducted using publicly available financial transaction datasets. Machine learning models including Random Forest, Gradient Boosting, and Deep Neural Networks will be trained and evaluated. Model performance will be assessed using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC).

Results: The findings are expected to show that ensemble learning methods and deep learning models outperform traditional machine learning techniques in detecting complex fraud patterns. Gradient Boosting and Deep Neural Networks are anticipated to demonstrate superior performance in terms of predictive accuracy and fraud detection rates.

Conclusion: Machine learning-based fraud detection systems can significantly improve the identification of fraudulent transactions in financial systems. Implementing these models can enhance financial security and reduce economic losses caused by fraud.

Keywords: Artificial Intelligence, Fraud Detection, Machine Learning, Financial Technology, Random Forest, Deep Learning