DHL invoices: checkpoints to detect invoicing discrepancies

DHL transport invoices can combine service price, invoiced weight, geographic areas, fuel surcharge, customs fees, address corrections, delivery options and negotiated discounts. Even with a reliable carrier, discrepancies can arise due to pricing rules, incomplete data or operational parameters.

The objective is not to assume that there is an error, but to control in a structured manner possible discrepancies between what was shipped, what was stipulated in the contract and what was invoiced.

1. 01

   Check the service billed

   Compare the service used with the one actually ordered: express, standard, international, priority delivery, return or specific collection. A wrong service code can significantly modify the final amount.

2. 02

   Check the invoiced weight

   Reconcile actual weight, dimensions and volumetric weight. In express transport, the invoice may retain the higher of the two, which makes the package data decisive.

3. 03

   Check fare zones

   Make sure the country, region or area applied matches the contractual pricing schedule. Incorrect zone classification can create a price discrepancy.

4. 04

   Analyze Supplements

   Review fuel surcharge, remote area, address correction, special handling, non-mechanizable package, security, seasonal surcharge and administrative or customs fees.

5. 05

   Compare with negotiated rates

   Check that discounts, contractual conditions and negotiated prices are applied, particularly for accounts with large volumes.

6. 06

   Identify duplicates

   Look for duplicate invoices, repeated lines, or charges applied multiple times on the same shipment.

7. 07

   Prepare a documented claim

   A discrepancy must be supported by proof: invoice, tracking, contract, tariff grid, slip, declared weight, dimensions and proof of delivery.

Manually auditing DHL invoices can become very time-consuming as volumes increase. FretGuard automates these checks by comparing invoices, carrier contracts and shipping data to quickly detect discrepancies, prioritize anomalies and build an actionable complaint file.