Securing National Healthcare Infrastructure: Intelligent Monitoring Of Fraudulent Claims Using AI
Keywords:
National healthcare infrastructure, fraudulent claims, AI monitoring, anomaly detection, graph neural networks.Abstract
Healthcare fraud remains a persistent and costly threat to national healthcare infrastructures, undermining both financial sustainability and patient trust. Globally, fraudulent activities consume more than 5% of total health expenditure, with annual losses estimated at approximately $300 billion in the United States alone (National Health Care Anti-Fraud Association, 2023). Despite considerable investments in detection systems, existing rule-based methodologies exhibit fundamental limitations: they are inherently reactive, incapable of identifying novel or previously unseen fraud patterns, and poorly equipped to detect collusive behaviors distributed across multiple providers, patients, and claims (Thornton et al., 2021). Consequently, sophisticated fraud rings and evolving schemes often evade detection, leading to substantial financial hemorrhaging and misallocation of critical healthcare resources.
To address these gaps, this paper proposes a hybrid artificial intelligence model that integrates three complementary paradigms: Isolation Forest for unsupervised anomaly scoring, an attention-based recurrent neural network (RNN) for sequential claim pattern recognition, and a graph neural network (GNN) for capturing relational fraud signatures across healthcare entities. The Isolation Forest component isolates anomalous claims through recursive partitioning without requiring labeled fraud data, making it robust to emerging fraud typologies (Liu, Ting, & Zhou, 2020). Simultaneously, the attention-based RNN models temporal dependencies in claimant submission behaviors, identifying subtle deviations from historical patterns that rule-based filters routinely miss (Choi et al., 2017).
The model was evaluated on a large-scale healthcare claims dataset comprising over 2.5 million claims, with ground-truth labels validated by regulatory agencies. Experimental results demonstrate that the hybrid approach achieves 97.3% accuracy and an AUC-ROC of 0.98, significantly outperforming baseline rule-based and single-classifier systems. Most critically, the model reduces false positives by 34% relative to conventional methods (p < 0.01). This reduction is operationally vital: every false positive necessitates manual review, consuming investigator time and delaying legitimate reimbursements. By lowering false alarms, the proposed system enhances investigator efficiency and reduces friction for compliant providers. In conclusion, this research contributes a scalable, real-time intelligent monitoring framework that substantially strengthens national healthcare infrastructure against evolving fraudulent schemes. The findings underscore the necessity of transitioning from static rule sets to hybrid AI architectures that integrate anomaly detection, temporal attention, and graph-based reasoning.
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