Actuarial ML Platform with a Protocol-Based SRE Validation Layer

Abstract

Insurance companies increasingly rely on machine learning methods to determine payouts and premiums, forecast customer churn, process complaint narratives and support a variety of service functions. In practice, however, such models often evolve as isolated modules, which makes data quality checks, leakage control, metric registration, and service-state assessment inconsistent across the analytical pipeline. This paper proposes an actuarial ML platform with a protocol-based SRE validation layer designed to integrate these procedures into a single operational framework. The platform follows a microservice architecture and combines data collection and preparation services, model training components, an evaluation block, and a results delivery interface. Within the protocol, each stage includes structural data checks, error classification, drift monitoring, metric comparison, and confirmation that model artifacts are suitable for production use. This approach enables controlled retraining and model updates, preserves comparability with previous versions, and supports service continuity. The proposed solution is aimed at improving the reliability of risk modeling in insurance organizations and strengthening the resilience of ML systems to changes in data and operating conditions.