CU Underwriting Technology and Data Analytics Flashcards
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Read the first 6 CU Underwriting Technology and Data Analytics flashcards as text
In modern underwriting, predictive analytics models are primarily used to:
Answer: Improve risk segmentation and pricing accuracy by identifying patterns in historical data
Predictive models analyze large datasets to segment risks more precisely, enabling underwriters to price policies more accurately and identify adverse risks.
What is 'telematics' as applied to personal auto underwriting?
Answer: Technology that collects real-time driving behavior data to inform risk assessment and pricing
Telematics devices or apps capture driving data such as speed, braking, and mileage, allowing insurers to price auto policies based on actual behavior.
Which type of data source provides underwriters with real-time property condition information without requiring a physical inspection?
Answer: Aerial imagery and geospatial data services
Aerial imagery and geospatial platforms allow underwriters to assess roof condition, proximity to hazards, and property features remotely.
A 'straight-through processing' (STP) workflow in underwriting means:
Answer: Automated rules-based evaluation that binds low-risk policies without human intervention
STP uses automated decision engines to process and bind straightforward risks instantly, reserving human review for complex or high-hazard submissions.
What is the main underwriting concern with using third-party data aggregators for risk scoring?
Answer: Data accuracy, completeness, and potential regulatory compliance issues with data use
Third-party data may be outdated, incomplete, or subject to fair lending and privacy regulations, requiring underwriters to validate quality and compliance before relying on it.
In underwriting, 'machine learning' models differ from traditional actuarial tables primarily because they:
Answer: Automatically identify complex, non-linear relationships in large datasets without being explicitly programmed
Machine learning algorithms detect patterns and interactions in data that traditional linear models may miss, enabling more granular risk segmentation.