Streamlined Claims Adjudication: Observability-Driven Dashboard Intelligence

Significant delays in claims processing for insurance companies are primarily due to human review, data silos and compliance-related issues, which lead to very high levels of fraud and a large number of mistakes made by regional carriers. To remedy this situation, an observability-based architecture is proposed that will integrate technologies such as Apache Spark streaming, MLflow, Snowflake and Grafana to accomplish real-time anomaly detection, as well as KPI visualization. As validated with simulated claims processing, this system would result in a high level of successful straight-through processing for low-complexity claims, accurate detection of fraud relative to the number of claims processed with low levels of bias (the fraud detection rate approaching 100%); and a significant reduction in total time required for claims processing. The key advanced capabilities of the architecture include improved logging, ethical a safeguards against bias and comprehensive auditability. From a business perspective, by providing compliance with audit requirements; faster payments to customers; and lower claim costs, the enterprise can benefit from improved compliance with regulatory and other requirements, improved customer service (due to faster payment), and lower claim costs. Additionally, this system will provide real-time alerts for claims adjusters at peak times of activity and a governance framework for the ethical deployment of AI that can be used to enhance recommendation systems for customer management. Keywords: Apache Spark streaming, MLflow, Anomaly Detection, Kpi Visualization, Governance Framework