Client Details
A Fortune 500 financial services company with a global footprint, providing life insurance and annuities to millions of customers. The firm is known for its data-driven approach to underwriting and customer service, managing over $300 billion in assets.
Challenge
Underwriting processes relied heavily on manual review and batch-processed risk assessments, resulting in delays and inconsistent decision-making. Legacy scoring systems lacked the ability to incorporate new risk signals in real-time, limiting the company’s agility in responding to evolving customer profiles.
Solution
A real-time AI-based risk scoring engine was developed using Azure Machine Learning and integrated into the company’s underwriting workflow. The team deployed a gradient boosting model trained on historical claims, third-party behavioral data, and socio-economic indicators.
The model was operationalized using Azure ML Pipelines and Azure Event Hubs, enabling real-time ingestion and scoring of applications within milliseconds. API integrations allowed underwriters to receive model insights embedded directly in their decision dashboard.
The Impact
✅ Real-Time Decisioning: Cut underwriting processing time by over 65% with instant risk scoring.
✅ Increased Accuracy: 18% improvement in risk classification accuracy led to better loss ratios.
✅ Operational Efficiency: Freed up underwriters’ time for complex cases, improving SLA adherence.
✅ Regulatory Compliance: Model monitoring and explainability ensured audit readiness and fairness.
