CASE STUDY

NLP Based Error Categorization

When a quote fails at a carrier, the error comes back in that carrier's own format and codes. Sorting and routing those messages by hand consumed hours every week. We built an NLP model that categorizes and assigns them automatically.

99%

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Client

A modern insurance platform focused on home coverage

Lines

Homeowners

What We Solved

Error triage and routing across carrier integrations

Engagement

Custom build

The Challenge

Quoting a risk across multiple carriers means handling a great many reasons a quote does not come back. Carriers return error messages when they decline to quote, and although the underlying reasons are often similar, each carrier communicates them in its own formats and codes.

That inconsistency made categorization a manual job. Considerable hours went into reading errors, classifying them, and assigning them to the right team member for resolution, which delayed resolution and raised operational cost.

The Solution

A Natural Language Processing solution trained on historical data using a logistic regression algorithm, integrated into the workflow through an API so error categorization and assignment happen automatically.

Technologies

Python Natural Language Processing Logistic Regression

Results

99%

decrease in assignment time, from 24 to 48 hours down to minutes

$80K

operational savings annually

Where This Applies

Integration work generates its own operational overhead, and error handling is where it accumulates. Any organization quoting across many carriers eventually finds that the exceptions cost more to manage than the integrations did to build.

Explore: Paktolus for Carriers → AI + Intelligent Automation → Systems Integration + Carrier Connectivity →

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How would results like these impact your business?

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