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RISK ASSESSMENT FOR UNDERWRITING HEALTH AUTO LIFE PROPERTY - Prompt

Auto Insurance Risk Evaluation: Data-Driven Underwriting for Compliance and Transparency

Dataset 1: Applicant Profile

This table (visualized as a bar chart comparing key metrics) provides basic applicant information.

Applicant Name Age License Issue Date Vehicle Model Vehicle Year Annual Mileage (miles) Resident Area
John Miller 28 2015-06-15 Toyota Camry 2018 12,000 Urban

Dataset 2: Driving Record History

This dataset (shown as a timeline chart) shows recorded traffic infractions and violations.

Date Infraction Type Points Fine Amount ($)
2017-09-10 Speeding (minor) 2 100
2019-03-22 Red Light Violation 3 200
2021-11-05 Speeding (minor) 2 120

Dataset 3: Accident & Claim History

This table (illustrated with a line graph showing claim frequency over time) details past accident events and claims.

Date Accident Type Claim Amount ($) Severity
2018-12-10 Rear-end collision 1,200 Minor
2020-05-15 Side-impact collision 5,000 Moderate
2022-03-30 Animal collision 800 Minor

Dataset 4: Telematics Driving Behavior

This dataset (displayed as a multi-line graph) captures monthly driving metrics from on-board telematics.

Month Average Speed (mph) Harsh Braking Events Rapid Acceleration Events
January 45 3 2
February 47 4 3
March 48 2 3
April 46 5 4
May 47 3 3
June 45 6 2

Dataset 5: Vehicle Maintenance History

This unstructured log (displayed in a document format) contains mechanic notes regarding vehicle upkeep.

"2021-11: Routine service performed; brake pads replaced. 2022-08: Minor engine check completed; no issues found. 2023-03: Oil change and tire rotation performed; vehicle in good condition."

Dataset 6: Credit History Overview

This table (visualized as a simple metric dashboard) provides key credit scoring parameters relevant for underwriting.

Credit Score Report Date Debt-to-Income Ratio (%) Payment History
720 2023-08-01 25 Excellent

Dataset 7: Customer Call Transcript

This unstructured transcript records a recent call between John Miller and an underwriter, providing qualitative insights.

"Agent: Good afternoon, Mr. Miller. I see you had a couple of driving infractions a few years ago and a moderate accident in 2020. Can you tell me about the steps you've taken since?

John: Yes, I completed a defensive driving course after my 2019 violation and have been more cautious ever since. My telematics data also reflects smoother driving, especially over the past year."

Dataset 8: Local Sports Team Performance DS

This dataset (presented as a pie chart and line graph) details the win/loss records and scoring statistics of the local football team.

Season Wins Losses Draws
2022 8 4 2
2023 10 2 3

Dataset 9: Restaurant Customer Satisfaction Survey DS

This table (visualized as a bar chart) shows recent survey results from a popular local restaurant chain.

Restaurant Average Rating (out of 5)
Gourmet Express 4.2
Urban Bites 3.8
Corner Cafe 4.5

Dataset 10: Regional Weather Patterns DS

This dataset (shown as a line graph) tracks average daily temperatures and rainfall in the applicant's region over the past month.

Date Avg Temp (°C) Rainfall (mm)
2023-09-25 22 0
2023-09-26 21 2
2023-09-27 23 0
2023-09-28 22 1
2023-09-29 24 0

As a compliance-focused insurance underwriter, you are tasked with evaluating the auto insurance application. Using the provided datasets, determine whether John Miller meets the underwriting standards for comprehensive auto insurance coverage. Include in your analysis the applicant's driving history, accident and claim records, telematics data, credit history, and vehicle maintenance history. Based on your evaluation, decide if the applicant should receive standard premium rates or if additional surcharges or conditions (e.g., a premium increase or usage-based monitoring) should be applied. Provide a detailed, transparent rationale backed by data from the datasets.

Please include all intermediate calculations and working steps in your analysis. Reference specific data points from the provided datasets to support your conclusions. Your final submission should begin with a brief executive summary outlining the key findings, risk metrics calculated (such as accident frequency or harsh braking rate), and your underwriting decision. Clearly delineate how each dataset influenced your overall risk assessment.