Enhancing Financial Fraud Detection in Banking Systems: Integrating IoT, Deep Learning, and Big Data Analytics for Real-time Security
Keywords:
Financial Fraud Detection, Banking system, IoT, Deep learning, Big Data Analytics, Real time securityAbstract
Financial fraud poses a significant threat to banking systems, with increasingly sophisticated attacks targeting sensitive customer data and financial transactions. This paper proposes an innovative approach to enhance financial fraud detection in banking systems by integrating Internet of Things (IoT), deep learning, and big data analytics for real-time security. By leveraging IoT devices to gather real-time transaction data and user behavior patterns, coupled with advanced deep learning algorithms and big data analytics techniques, banks can detect and prevent fraudulent activities more effectively. This paper outlines the key principles and mechanisms underlying the integration of IoT, deep learning, and big data analytics in financial fraud detection. Furthermore, it discusses the potential benefits and challenges of adopting this approach, as well as future research directions to improve real-time security in banking systems.
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