Deep Learning Architectures for Fraudulent Insurance Claim Detection in Health-Finance Ecosystems
Keywords:
deep learning, fraud detection, insurance claims, health-finance, anomaly detectionAbstract
Complex deep learning architectures can identify fraudulent insurance claims in integrated health-finance ecosystems. Convolutional and recurrent deep neural networks and graph-based anomaly detection identify medical reimbursement issues. Real-time scoring pipelines verify claims without slowing system performance or decision-making. Exploring OCR and NLP to extract structured data from unstructured medical articles enhances predictive model feature representation. Security, compliance, and claims may be automated with digital payment gateway integration. We also address data heterogeneity, model interpretability, and adversarial behavior mitigation in complicated healthcare finance networks. Insurance infrastructures get scalable, resilient, and intelligent fraud detection from us.
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References
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