CASE STUDY

Modernizing Personal Lines Underwriting with AI and External Data Sources

Homeowners underwriting relied on self-reported property data and manual validation, producing inaccurate risk assessments and frequent post-bind premium adjustments. We built an AI-powered property intelligence solution that validates characteristics automatically during quoting.

18%

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CLIENT

A publicly traded homeowners insurance company known for issuing quotes in under 60 seconds

LINES

Homeowners

WHAT WE SOLVED

Property risk assessment and quote accuracy

ENGAGEMENT

Custom build

The Challenge

The homeowners underwriting process depended heavily on self-reported data and manual validation of property characteristics.

The consequences showed up throughout the book: inaccurate risk assessments, inflated replacement costs, and frequent post-bind premium adjustments that eroded margin and customer confidence at the same time. Meanwhile, high-volume submissions from digital channels were straining underwriters, causing quote delays and inconsistent outcomes across similar risks.

The Solution

An AI-powered property intelligence solution using computer vision, geospatial analytics, and third-party data to automate risk assessment during quoting.

The system pulls real-time data from aerial imagery, public records, and proprietary data vendors to validate key property attributes including roof type, square footage, pool presence, and proximity to hazards. AI models flag inconsistencies between what was self-reported and what is actually observable, automatically adjust replacement cost estimates, and recommend risk-tiering decisions based on wildfire, flood, and crime exposure scores.

Technologies

Python OpenAI Node.js ReactJS PostgreSQL Computer Vision

Results

95%

of quotes now include independently validated property characteristics

35%

faster quote issuance across direct and agency channels

23%

improvement in underwriting accuracy for replacement cost estimates

18%

reduction in premium leakage from post-bind adjustments

2x

underwriter capacity increased by 2x through automation of routine reviews

Where This Applies

Underwriting accuracy and quote speed usually trade against each other, which is why most carriers accept one and live with the other. External data breaks that trade by removing the step where a person confirms what a machine could have confirmed already.

Explore: Paktolus for Carriers → Underwriting + Policy Operations → AI + Intelligent Automation →

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How would results like these impact your business?

Twenty minutes, a candid conversation. If we’re not the right fit, we’ll say so.

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