CU Underwriting Technology and Data Analytics Flashcards
6 cards from real CU practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 CU Underwriting Technology and Data Analytics flashcards as text
What is 'API integration' in the context of an underwriting workstation?
Answer: Connecting the underwriting platform to external data sources to pull information automatically during the quoting process
API integrations allow underwriting systems to automatically retrieve credit scores, property data, or loss history in real time, reducing manual data entry and improving accuracy.
Which data privacy regulation most directly affects how US insurers collect and use consumer data for underwriting?
Answer: Gramm-Leach-Bliley Act (GLBA)
The GLBA requires financial institutions, including insurers, to protect consumers' nonpublic personal information and provide privacy notices.
An underwriting 'digital appetite statement' serves to:
Answer: Communicate to agents and brokers the types of risks an insurer's automated systems will quote and bind
A digital appetite statement guides producers on which submissions will be handled automatically versus referred to a human underwriter, improving submission quality.
How does geographic information system (GIS) mapping benefit property underwriters?
Answer: It visualizes risk concentrations, natural hazard zones, and proximity to loss-contributing features
GIS tools allow underwriters to overlay risk data on maps, identifying flood plains, wildfire zones, or wind corridors that affect pricing and coverage decisions.
What is an underwriting 'rules engine'?
Answer: An automated system that applies predefined decision criteria to submissions to accept, decline, or refer them
A rules engine encodes underwriting guidelines into logic that is applied consistently and instantly to every submission, ensuring compliance with company standards.
In commercial lines underwriting, 'exposure data quality' is critical because:
Answer: Inaccurate exposure information leads to mispriced risks and potential adverse selection
If the data describing a risk — such as payroll, revenue, or square footage — is wrong, the premium will be calculated incorrectly, distorting profitability.