Problem Statement: Legacy risk assessment models relied on static data and manual processes, failing to capture dynamic risk patterns and leading to suboptimal pricing and inaccurate claims prediction, costing the business significant revenue over time.
Approach: We developed and deployed machine learning models that ingested real-time and historical data to identify risk patterns and predict claims outcomes. The models were integrated into the insurer's workflow, automating risk scoring and pricing decisions.
Impact: Through a low-risk, pilot-first validation, our ML-powered risk models improved prediction accuracy by 30%, reduced claims processing time by 40%, and generated $500K in annual savings through better risk pricing, delivering tangible results rapidly and building a sustainable AI culture. The insurer now benefits from more accurate pricing, faster claims processing, and improved profitability.