Still waiting days to identify a total loss? That delay is costing you—big time. In auto insurance, every extra day spent identifying a total loss racks up rental fees, storage costs, and adjuster hours and it erodes customer trust. In our latest blog by Jason Ling he shows how insurers can move from reactive to predictive by combining machine learning and Snowflake to spot total losses at FNOL. The results? ☑️ 40% faster cycle times ☑️ Lower loss adjustment expenses ☑️ 15–20 point NPS improvements If you're still relying on rules-based systems or manual processes, it's time to rethink the claims journey. Read the blog to see how it works in action: https://lnkd.in/demzbyrY #WNSKipiAI #Insurance #ClaimsTransformation #MachineLearning #Snowflake #AutoInsurance #Insurtech #TotalLoss #DataScience #CustomerExperience #DigitalClaims
