Abstract:
Financial fraud is a significant concern for individuals, businesses, and financial institutions worldwide. Traditional fraud detection methods often struggle to keep pace with the ever-evolving techniques employed by fraudsters. In recent years, advancements in deep learning and big data analytics have shown promising results in improving fraud detection accuracy and efficiency. This abstract presents an adaptive predictive financial fraud detection approach that leverages deep learning methods on a big data platform.
The proposed approach utilizes a combination of deep learning algorithms, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to analyze large volumes of financial data. By extracting meaningful patterns and features from the data, these deep learning models can effectively identify fraudulent activities and distinguish them from legitimate transactions.
One of the key strengths of the proposed approach is its adaptability. The system continuously learns from new data and adapts its detection capabilities to the evolving fraud landscape. This is achieved through an iterative training process, where the deep learning models are regularly updated and refined using labeled fraud and non-fraud examples. The use of a big data platform enables efficient processing and analysis of vast amounts of financial data, ensuring scalability and real-time fraud detection.
To evaluate the effectiveness of the proposed approach, extensive experiments are conducted on a diverse and representative financial dataset. The performance metrics, such as precision, recall, and F1-score, are used to assess the accuracy and robustness of the adaptive predictive fraud detection system. Comparative analyses with existing fraud detection methods are also conducted to demonstrate the superiority of the proposed approach.
The results demonstrate that the adaptive predictive financial fraud detection approach using deep learning methods on a big data platform achieves superior accuracy and efficiency in detecting fraudulent activities. The adaptive nature of the system enables it to continuously learn and adapt to emerging fraud patterns, thereby improving its overall effectiveness over time. The proposed approach has the potential to significantly enhance fraud detection capabilities in the financial sector, leading to better protection against financial crimes and reducing potential losses for individuals and organizations alike.
Keywords: Financial fraud detection, deep learning, big data analytics, adaptive detection, artificial neural networks, convolutional neural networks, recurrent neural networks, big data platform.
AN ADAPTIVE PREDICTIVE FINANCIAL FRAUD DETECTION APPROACH USING DEEP LEARNING METHODS ON A BIG DATA PLATFORM, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
