Predictive Analytics for Claims Lifecycle Optimization in Cloud-Native Platforms
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P110Keywords:
Predictive Analytics, Claims Lifecycle, Cloud-Native Platforms, Machine Learning, Fraud Detection, Data Pipeline, KubernetesAbstract
Insurance industry is under immense pressure to facilitate the claims process, minimize fraud and customer satisfaction. Combined with cloud-native systems, predictive analytics (PA) presents an innovative way to address the claims lifecycle, including initial receipt of notification to resolution. Cloud-native application designs are scalable, resilient and flexible in deploying AI/ML-based analytics in real time. This article throws light on how predictive models enhance efficiency, fasten the decision-making process, and minimize consumption costs along the claims processing pipeline. The primary attention is paid to the activities prior to the year 2022, where the containerized microservices, data pipes, and big data platforms (e.g., Apache Kafka, Spark, Hadoop) have been adopted early to enable predictive analysis. The paper describes machine learning techniques, both supervised and non-supervised learning, time-series forecasting, and anomaly detection, which might be used to optimize fraud detection, the estimate of losses, and triage of claims. Experimental findings using historical datasets (pre-2022) are shown to yield a 35 percent reduction in the number of days that claims take to settle, a 20 percent increase in the accuracy of the fraud detection system, and a 25 percent decrease in the cost of operations when compared to the systems of the past. The paper also includes a comparative study of predictive algorithms (Random Forest, Gradient Boosting, Deep Neural Networks) run in Kubernetes clusters as a scalable way of deploying predicitve models. Issues such as data privacy, data latency and regulatory compliance are explained. 1- In the efforts to come, 2- It is intended to add real-time information with the IoT, blockchain-based transparency, and federated learning to enhance predictive analytics in the claims management
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