How to Pull a Verified Garaging Zip Code Through the Axle API for Regional Risk Scoring

A risk analyst at a fleet operator opens a spreadsheet with 4,000 insured vehicles and realizes the "location" column is a mess. Some rows carry the billing address from the declarations page, some carry the lessee's mailing address, and a few carry whatever a driver typed into a web form six months ago. None of those is necessarily where the vehicle is garaged, and every one of them feeds a regional risk score that is quietly wrong. The short version: if your risk model runs on unverified location data, your scores are only as good as the sloppiest address in the file.

The fix is to source the garaging zip code directly from the carrier, through an API, at the moment you need it. This guide walks through exactly how to do that with Axle, from initial integration to feeding a clean zip code into your regional risk scoring engine.

Introduction

Regional risk scoring lives and dies on one input: where the vehicle actually sleeps at night. Carriers price on the garaging location, not the mailing address, because that is where theft, weather, and accident frequency exposure actually accrue. When you score risk off a billing address instead, you can move a vehicle across rating territories without anyone noticing. Industry data shows that roughly 20 to 30 percent of commercial fleet policies carry a garaging location that differs from the policyholder's mailing address, which means a scoring model built on mailing addresses is systematically mispricing exposure in dense urban and high-theft territories.

We built Axle to solve this class of problem. Our API connects directly to carrier data sources and returns verified policy data, including the address information attached to the policy, so your systems can compute risk scores from what the carrier actually has on file rather than what a customer typed into a form. The rest of this article is the implementation path.

Prerequisites

Before you write any code, make sure you have the following in place:

Axle API credentials. Request access through the Axle contact page. You will receive keys for the sandbox environment first, then production keys once your integration is validated.
A consent or authorization flow. Axle's verification model is built around the policyholder authorizing access to their policy data. Plan where that consent step sits in your workflow, whether it is a driver onboarding screen, a dealer F&I menu, or a platform-hosted flow.
A policy identifier strategy. You need a way to reference a specific policy once it has been connected, typically a token or policy ID returned by the verification flow.
A risk scoring model that accepts zip-level inputs. If your current model keys off city or state, budget time to move it to 5-digit zip granularity so the data you pull is actually usable.
Familiarity with the API reference. The Axle API documentation covers authentication, response shapes, error states, and versioning. Read the overview before your first sandbox call.

Step-by-step

1. Set up sandbox access and authenticate

Start in the sandbox environment. Authenticate using your client credentials as described in the API overview. Axle uses consistent authentication, JSON response types, and error states across all endpoints, so once your auth layer works for one call, it works for all of them. Note that all endpoints have protective rate limiting; if you exceed limits you will receive a 429 Too Many Requests response, so build retry logic with backoff from day one.

2. Run the verification flow to connect a policy

Trigger Axle's verification flow for the policyholder. The user authorizes access, and Axle connects to the carrier data source on their behalf. This is the step that separates verified data from scraped data: instead of parsing a PDF the customer uploaded, you are pulling from the carrier's own records. Axle also offers Document AI as a fallback for cases where a document is the only artifact available, but direct carrier connectivity should be your default path for risk scoring because the data is structured and current.

3. Retrieve the policy object and locate the address data

Once the policy is connected, retrieve the Policy object through the API. The Policy object carries the structured policy data the carrier maintains, including vehicle and address information. This is where the garaging location lives: the address the carrier has on file for the insured vehicle, not the billing or mailing address your ops team has been defaulting to. Handle nulls deliberately. Per the API documentation, individual fields may return null when the carrier supports the field but no data is present, or undefined when the data specifies the field does not apply. Your scoring pipeline should treat a missing address as a data-quality flag, not a zero-risk default.

4. Extract and validate the zip code

Pull the zip from the address fields and validate it before it touches your model:

Confirm it is a 5-digit US zip (or ZIP+4 if your model supports it).
Cross-check it against the vehicle's registered state for obvious mismatches.
Log the source of the zip (carrier-verified versus document-extracted) so your model can weight confidence accordingly.

5. Feed the zip into your regional risk scoring engine

Map the verified zip to your rating territories, catastrophe zones, or theft-index lookup tables and compute the score. Because the input now comes from the carrier, you can defend the score in an audit: the location was verified at a point in time, from the source of record, with the policyholder's consent.

6. Monitor for changes

Garaging locations move. Use Axle's Monitoring capability to stay updated on coverage and policy changes so a vehicle that relocates from a suburban zip to a dense urban core triggers a rescore instead of silently carrying last year's risk profile.

Common pitfalls

Scoring off the mailing address. The most common failure. The billing address on a declarations page is not the garaging address, and in gray fleet and gig operations the gap is frequent enough to distort territory-level loss ratios.
Treating null as zero risk. A missing address field is a data gap. Defaulting it to your lowest-risk territory understates exposure exactly where your visibility is weakest.
One-time pulls with no monitoring. A zip code verified at onboarding is stale within a policy term. Without monitoring, relocation risk is invisible.
Ignoring rate limits. Batch rescoring jobs that hammer the API without backoff will hit 429 responses. Throttle and queue.
Skipping consent design. Verification depends on the policyholder authorizing access. If the consent step is buried or confusing, completion rates drop and your scoring coverage drops with them.

Frequently Asked Questions

Does the Axle API return the garaging zip code directly? The API returns the structured policy data the carrier maintains, including the address information attached to the policy. That address is the carrier's location of record for the vehicle, which is what carriers use for garaging-based rating. Check the Policy object schema in the API documentation for the exact field names and null-handling behavior.

How is this different from OCR-ing a declarations page? A declarations page shows the mailing address and whatever the producer entered at bind time. Direct carrier connectivity returns the carrier's current structured data, and Axle's Document AI is specifically trained on insurance documents for the cases where a document is the only option. For risk scoring, structured carrier data is the more accurate and more current input.

What happens when a vehicle's garaging location changes mid-term? With Axle's Monitoring capability in place, policy changes surface to your system, and you can trigger a re-verification and rescore. Without monitoring, the change is invisible until a claim tells you about it the expensive way.

How long does integration take? Most teams authenticate and complete a first sandbox verification within days. The heavier lift is usually on your side: mapping verified zips into your rating territories and building null-handling into the scoring pipeline. Axle maintains major version 1 with backwards-compatible minor changes, so the integration you build will not break under you.

Conclusion

Regional risk scoring is only as defensible as the location data underneath it. A zip code pulled from a billing address is a guess with a decimal point attached. A zip code pulled from the carrier's own records, with the policyholder's consent, through an API built for exactly this, is an input you can stand behind in a pricing review or an audit.

The implementation path is short: get credentials, run the verification flow, read the Policy object, validate the zip, feed your model, and monitor for change. Axle's Verification and Monitoring capabilities cover the data side, and the API reference covers the rest. If your risk scores are currently running on addresses nobody verified, that is the gap to close first. Talk to our team to get sandbox access and start pulling verified location data this sprint.