Cloud-Based Deep Learning for Real-Time Insurance Fraud Detection
Keywords:
Cloud-Native Fraud Detection, Real-Time Insurance Analytics, Deep Learning Fraud Models, Multi-Cloud AI Architecture, Streaming Data Processing, Insurance Risk Prediction, Bidirectional LSTM Models, Scalable Fraud Analytics, Autonomous Risk Detection, Low-Latency AI Systems.Abstract
Insurance fraud detection has received significant attention in recent years, yet early and real-time detection remains a major challenge. This work presents a cloud-native deep learning architecture, deployed across a multi-cloud environment, for real-time fraud detection on insurance company data. The architecture supports both vertical and horizontal scaling and hosts deep learning models optimized for real-time inference. It is designed to integrate with a Streaming Processing System capable of handling a set of multi-modal, loosely predefined features. In addition, the architecture incorporates a second model — based on bidirectional long short-term memory (Bi-LSTM) — for autonomous insurance risk prediction across different customer categories. The overall goal is to help insurance companies detect fraud in real time and improve business processes and administrative decision-making through integrated risk-detection services. Cloud-native products are leveraged throughout to ensure a scalable, cost-effective ecosystem.
While the external surface of this cloud-native approach is secured, internal processing pipelines are not the focus of this security scope; only real-time fraud detection is addressed here. Real-time detection remains inherently complex, requiring very low latency and the ability to process multiple concurrent data streams. In practice, fraud in insurance companies is often verified only after the fact rather than at the point of occurrence, increasing long-term liability and risk exposure for the company. To address this, real-time fraud detection is implemented on a cloud-native framework (Architecture v2) that supports the incorporation of external features. Two distinct sets of models, built using cloud ML products, are used together — one for detection and one for prediction — to strengthen the overall business model.
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