Cloud-Native Deep Learning Pipelines for Intelligent Insurance Fraud Analytics
DOI:
https://doi.org/10.64137/31079911/IJMST-V2I3P102Keywords:
Fraud Prevention Systems, Intelligent Data Pipelines, Cloud-Native Data Engineering, Deep Learning Fraud Models, Real-Time Fraud Detection, Data Acquisition Strategies, Privacy-Aware Labeling, Fraud Analytics Platforms, Scalable Detection Pipelines, AI-Driven Fraud IntelligenceAbstract
Fraud prevention is critical across industries due to its financial burden and regulatory requirements. Traditional techniques, which often rely on rule-based methods, are limited by their inability to generalize and require periodic retraining. The growing amount of data available allows fraud detection to be integrated into intelligent pipelines that support data acquisition, processing, model training, and predictions. Cloud-native data engineering principles enable the design of scalable data pipelines, while advanced deep learning architectures improve detection performance. The intelligent pipelines gain from both disciplines and support successful adoption in fraud-related tasks. Specific challenges of the underlying data acquisition task have been addressed using real-world data from the insurance sector. Auxiliary sources of information such as access logs and distance measurements from external APIs have proven to be critical to model training, while special labeling strategies guarantee adherence to privacy regulations.
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