Technical realization of intelligent diagnostic system
Coverage Cat'sPolicy Analysis EngineMachine learning algorithms are used to cross-validate a user's asset portfolio against existing coverages with diagnostic dimensions including:
- Duplicate Coverage Detection: Discovery of duplicate coverage for the same risk in multiple policies
- Early warning of risk mismatches: Identify clauses where the sum insured is grossly disproportionate to the value of the asset (e.g., underestimating the replacement cost of a property)
- Geographic risk mapping: Validating the completeness of natural disaster provisions in conjunction with LBS data
Actual operational data shows that 82%'s users found coverage gaps through system testing, with an average of 2.3 important risks not covered per family. Meanwhile, 37%'s users are over-insured, mainly focusing on the small collision coverage portion of auto insurance.
This answer comes from the articleCoverage Cat: An Insurance Allocation Tool to Optimize Individual RisksThe
May not be reproduced without permission:AI productivity tools " Policy optimization feature identifies redundant insurance expenses above 30%
