Case Study: nyctechlabs achieves ~45% underwriting evidence cost savings and 20% improved risk portfolio with Ezappsolution

Insurance Company

nyctechlabs, a life insurance company, needed a Real‑Time Policy Engine to price policies dynamically against customer data and shifting market risk (exposed by events like COVID). Their existing BI and predictive-modeling framework lacked AI capabilities and could not handle volatile scenarios, so they engaged Ezappsolution to modernize pricing, risk classification, and underwriting.

Ezappsolution implemented a full solution—scrubbing and consolidating data into a single customer view, integrating ERP, booking, CRM and analytics engines, and building neural-network and machine‑learning models (risk-class models, Bayesian customer scoring, XGBoost policy risk, clustering, pricing and reinforcement‑learning ROI models) to power real‑time policy pricing. The engagement delivered measurable impact: an AI-driven underwriting workflow that cut evidence costs by ~45% with ~80% STP, a 20% improvement in risk portfolio classification, and a 12% improvement in optimized monthly returns.