AI-Based Fraud Detection and Prevention at the Radio Access Network: Architectures and Mechanisms for Financial Wireless Service
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P115Keywords:
AI-Based Fraud Detection, Financial Wireless Services, Network-Level Fraud Prevention, Secure Wireless Communication Systems, Deep Learning–Based Fraud Analytics, Context-Aware Fraud Detection, Adaptive Fraud Mitigation Strategies, Policy-Driven Access Control, Closed-Loop Detection and Response, Edge Data Computing, Intelligent Threat DetectionAbstract
The recent explosive growth in wireless financial services, mobile banking applications, electronic payment systems, and edge-computerized financial applications have greatly expanded the fraud and cyber-financial crime attack space. The classic fraud detection systems are mostly performed at application or cloud layers and this may lead to very slow detection, financial losses and a poor situational awareness of the network level behaviors. Conversely, the next generation Radio Access Network (RAN) designs can offer a prospect to integrate Artificial Intelligence (AI) into wireless infrastructure in order to detect and mitigate any fraudulent activity on the network edge early. The paper will provide an elaborate framework of AI-fraud detecting and prevention at the Radio Access Network, with architectures, mechanisms and adaptive security measures concerning financial wireless services. To enable RAN intelligence the proposed framework will combine a deep learning-based fraud analytics, context-based anomaly detection, policy-based access control, and closed-loop mitigation mechanisms in a single layer. Nodes computing on the edge that are installed in base stations analyze patterns of user behavior, network traffic, and metadata of financial transactions in near real time to identify threats in a low-latency manner. The architecture exploits multi-modal data fusion which involves the integration of data concerning wireless channel, pattern of user mobility, device fingerprints and transaction history to detect fraudulent activities which were not detected by traditional security systems. Moreover, adaptive mitigation methods dynamically change network policies, authentication and access privileges with reference to risk scores produced by AI models. This study also provides mathematical modeling of the estimation of the fraud risk, scoring of anomalies and the optimization of adaptive decision-making. Experimental analysis shows that there is an increase in detection accuracy, response latency and financial loss prevention other than the conventional centralized fraud monitoring systems. Its findings show that AI intelligence can be incorporated into the RAN and bring latency in fraud detection to a minimum of 45 seconds, as well as increase the rates of anomaly detection by more than 30 and the resilience to changes in the tactics of fraud. Moreover, control mechanisms based on the policies allow automated responses and reduce the number of people and operation expenses. The research results point out the significance of smart wireless infrastructures to reliable financial ecosystems and the significance of edge AI in future communication systems like 5G and beyond. The suggested structure is supportive of the development of secure wireless communication systems as it supports proactive prevention of fraud, smart detection of threats, and adaptive mitigation plans. The valuable information in this work is to telecommunications providers, financial institutions and cybersecurity researchers to create integrated network-level solutions of fraud prevention in future digital economies.
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