Abuse Post-purchase

Abuse Prevention Post-Purchase

The post-purchase integration helps to prevent bad actors from exploiting customer-friendly returns and refund policies by providing a decision or a recommendation at the time the consumer initiates a request, post-purchase. This integration also enables you to issue faster refunds to trustworthy customers with a high degree of confidence by weeding out the bad actors.

Forter supports the following policy types at post-purchase (ie. initiate a return, report an issue, etc.):

• Item Not Received
• Returns

Integration Steps

1. Complete Abuse Prevention at Checkout Integration

   In order to integrate Abuse Prevention at Post-purchase, complete the integration for Abuse at Checkout and provide Forter with the relevant data as outlined.

2. Send Compensation Requests When Received

   Using the Compensation request API endpoint, send Forter all information regarding customers’ requests for compensation or appeasements (e.g. reship, refund, return, store credit) due to various service issues (e.g. item not received, items are missing or damaged, etc) in real time at the time the request is received. The information will be used for this decision as well as informing the decision the next time that this customer places an order during checkout.

You can send the compensation requests at either:

• Item Level - When compensation requests can be made on an item by item basis, and an order may receive multiple compensation requests each with different items. Granularity of relevant information such as the type of compensation requested is detailed at the item level and may vary between items.
  • Order Level - When compensation requests are aggregated at the order level. A simpler approach where no item level data is shared with Forter.

It is particularly important to ensure that all INR and returns requests are sent to Forter in order to be able to order to define targeted Item Not Received and Returns policies based on specific conditions, whether they are applied at checkout or post-purchase. Important data for this purpose includes:

• orderId - of the full reservation details sent via Order API.
• correlationId - to the corresponding compensation request (when applicable).
• compensationRequest - the main object that contains all relevant data about the compensation including:
  • Type of compensation that was requested by the consumer (e.g. Replacement, Refund, Return etc..) the channel through which the request was made,
  • Reason provided for the request (e.g. changed my mind)
  • Items being returned
  • Return method requested

It is critical to map your internal returns data to Forter’s fields and ENUMS in order to define targeted refund/returns policies based on specific conditions.

3. Handle Automated and Policy Decisions from the API response

   The Compensation request API response will contain Forter's decision (approve or decline) regarding the compensation request and, if applicable, a recommendation based on the abuse prevention policies you have set. The recommendation responses will vary based on the policy type.

Recommendation Responses per policy type

• Item Not Received: MONITOR_POTENTIAL_INR_ABUSE
• Returns: MONITOR_POTENTIAL_RETURN_ABUSE

The merchantPolicyId field, is the unique ID that Forter assigns to a policy when a user creates a policy in Policy Builder. The ID can be used in downstream systems to have custom flows. If the decision was not influenced by a user-defined policy, then the field will return a blank string.

Compensation Request API response example

{
  "forterDecision": "DECLINE",
  "recommendations": [],
  "decisionReason": "MerchantPolicyPostPurchaseINR",
  "merchantPolicyId": "d8b0fe7062eb4ad1a92bfbcc71b074d3",
  "status": "success"
}

4. Dispute Notifications

   Confirm that you are sending all disputes to Forter through webhooks from your PSP or the Dispute API. Forter uses prior chargeback data to evaluate future refund requests as well as enforce Item Not Received and returns policies at checkout and post purchase.

5. Prepare and Upload Historical Data

   To ensure the highest level of accuracy for our decisioning model, Forter customizes our model to fit the specific risk profile of each of our customers. We achieve this by training the model with your past compensation data in order to provide you with better accuracy from Day 1. Provide 1 year of historical data for returns and INR, where possible, so that INR & returns policies can take effect immediately, and velocity counters take into consideration prior returns/INR refunds.