Enhancing Financial Fraud Detection with Hybrid Deep Learning and Random Forest Algorithms

Abstract

This research paper explores the integration of hybrid deep learning models and the Random Forest algorithm to enhance the detection of financial fraud. As financial systems become increasingly complex, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent schemes. The proposed approach leverages the strengths of deep learning models in handling high-dimensional data and the interpretability and decision-making capabilities of the Random Forest algorithm. We construct a hybrid model that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM) to capture both spatial and temporal patterns in transaction data. The output from the hybrid deep learning model is then fed into a Random Forest classifier to improve overall prediction accuracy and reduce false positives. The model is evaluated using real-world financial transaction datasets, demonstrating a significant increase in fraud detection accuracy compared to existing methods. Our experimental results indicate a reduction in false positive rates by 15% and an increase in overall detection accuracy by 20%. The findings suggest that this hybrid approach offers a robust framework for financial institutions seeking to enhance their fraud detection systems and protect against evolving threats. Additionally, the paper discusses the model’s scalability and adaptability to various types of financial data, emphasizing its potential for widespread application in the financial industry.