An Intelligent AI-Driven Framework for Real-Time ATM Transaction Validation, Fraud Detection and Financial Switching Integrity
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I4P119Keywords:
Artificial Intelligence, Fraud Detection, Financial Switching, ATM Networks, Graph Neural Networks, Lifecycle Governance, Cybersecurity, Decision Intelligence, Predictive Quality AssuranceAbstract
The exponential growth of digital financial transactions has placed unprecedented stress on automated teller machine (ATM) networks and the underlying financial switching architectures. Traditional rule-based transaction validation systems are increasingly inadequate for detecting sophisticated, high-velocity fraud vectors while maintaining the strict latency requirements of real-time processing. This paper proposes a comprehensive, intelligent framework that converges Graph Neural Networks (GNNs) with Extreme Gradient Boosting (XGBoost) ensembles to capture both spatiotemporal transaction anomalies and complex relational dependencies across banking nodes. By moving away from standalone models, the proposed architecture dynamically analyzes the topology of transaction requests, significantly enhancing the precision of fraud detection algorithms. Furthermore, the model embeds an architecture-centered lifecycle governance mechanism to ensure seamless deployment, continuous predictive quality assurance and automated performance degradation monitoring. Empirical evaluation using simulated high-frequency financial switch data demonstrates that the proposed framework achieves an F1-score of 0.962 in fraud detection while maintaining a sub-40 millisecond inference latency, ensuring zero disruption to switching integrity. This study contributes a novel, robust methodology for mitigating financial risk, optimizing software lifecycle integration and reinforcing cybersecurity intelligence in distributed financial systems. The framework proves that low-latency operational constraints do not necessitate a compromise in algorithmic complexity, provided that rigorous software engineering paradigms are enforced during deployment.
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