Fraud & Abuse Insights

Identify behavioral patterns and apply consistent policies automatically with our Fraud & Abuse Insights assessment tool.

Overview

The Fraud & Abuse Insights assessment tool is embedded directly within the parcelLab App and enables you to detect patterns in customer activity across your post-purchase experience, including returns, claims, and refunds.

The assessment tool enriches parcelLab events, such as order history and return behavior, with behavioral risk signals provided through integrated risk assessment services. These signals are surfaced in the parcelLab App and can be used to:

• Monitor customer activity

• Filter and review high-risk cases

• Create rules to automate workflows and policies

This allows you to protect revenue and reduce refund abuse, while also maintaining a frictionless experience for trusted customers.

Take the Tour

Explore Fraud & Abuse Insights with our interactive demo below.

Configuring Fraud & Abuse Insights

The following sections describe the configuration required for Fraud & Abuse Insights.

Customer Requirements

For the feature to work, the following requirements need to be met:

• Define the thresholds that will be used to classify risk levels

• Align on how risk signals should be used in your setup

Fraud risk signals are generated via parcelLab’s integrated fraud assessment models and automatically enriched into your data once enabled.

Implementation Process

To activate this feature, please contact your parcelLab representative or sign up here to request a trial.

Once requested, parcelLab will enable the feature for your account and validate that fraud signals are available in your workflows.

Using Fraud & Abuse Insights

This section describes the process of using Fraud & Abuse Insights in the parcelLab App.

Filter Trackings by Fraud Risk Level

You can view fraud risk levels for customers in associated tracking details in the Trackings module.

View Trackings by Fraud Risk Level

To view trackings by fraud risk level:

1. Navigate to the Trackings page.

2. Click the Add filter button.

   

   A drop-down menu will display a list of available filters.

3. Select the Tags filter.

   
4. Select the required fraud risk level.

   You can select more than one risk level.

   

   After enabling the filter, the Trackings page automatically refreshes to display the relevant tracking records.

5. Select the required tracking to view. The tracking details page will display.

6. Click the fraud risk level indicator (that is: low, neutral, medium or high fraud risk).

   

   For high fraud risk, a red banner will display at the top of the tracking details page.

   

   A sidebar will open for the fraud risk level to allow you to review and monitor risk signals of the customer.

   You can expand the sections to view further details about the reasoning for the fraud risk level applied to the customer.

   

Create Rules by Fraud Risk Fields

When Fraud & Abuse Insights is enabled for your account, you can use the fraud risk levels and categories to create rules across different workflows to trigger actions based on the assessed risk. The Fraud Risk Level and Fraud Category fields can be used like any other filter field in the parcelLab App to target specific customer groups, including in journeys and returns portal configurations to adjust customer experiences and automate operational policies in the post-purchase-journey.

Fraud Signals Use Cases

Check out the following use cases that you can apply for fraud risk levels and fraud categories used in the Fraud & Abuse Insights tool.

Use fraud risk levels to apply stricter controls where needed, while fraud categories allow you to apply targeted polices based on the type of behavior detected. You can also combine fraud signals with other customer or order attributes to ensure you create comprehensive policies.

Refund Method Control

Goal: Protect cash flow and prevent refund abuse by restricting instant refunds for higher risk customers while maintaining a frictionless experience for trusted customers.

Example settings:

• If Fraud Risk Level = "low" or "neutral" → allow instant refund

• If Fraud Risk Level = "medium" → allow standard refund

• If Fraud Risk Level = "high" → store credit only

Refund Window Adjustment

Goal: Discourage abusive return behavior by shortening return windows for higher risk customers while keeping flexible policies for trusted customers.

Example settings:

• If Fraud Risk Level = "low" or "neutral" → 60 day return window

• If Fraud Risk Level = "medium" → 30 day return window

• If Fraud Risk Level = "high" → 14 day return window

Manual Review Routing

Goal: Ensure suspicious returns or claims receive manual review while allowing low-risk cases to be processed automatically.

Example settings:

• If Fraud Risk Level = "low" or "neutral" → auto-approve return

• If Fraud Risk Level = "medium" → show warning and require reason

• If Fraud Risk Level = "high" → route to manual review

Wardrobing Prevention

Goal: Reduce temporary product use followed by immediate returns by shortening the return window to discourage abuse while still allowing trusted customers to return items within a reasonable timeframe.

Example settings:

• If Fraud Risk Level = "medium" and Fraud Category = "Fast return" → shorten return window

High-Value Refund Protection

Goal: Prevent high-value orders from being automatically refunded when fraud risk signals indicate suspicious behavior.

Example settings:

• If Fraud Risk Level = "high" and Order Value = >$300 → disable instant refund → route to manual review

Loyalty Reward for Trusted Customers

Goal: Provide premium experiences for trusted customers by prioritizing low-risk customers in campaigns for loyalty offers.

Example settings:

• If Fraud Risk Level = "low" or "neutral" → include in loyalty campaign

• If Fraud Risk Level = "medium" or "high" → exclude from loyalty campaign