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Introduction

eCommerce return fraud has been challenging for online businesses. It significantly threatens businesses, impacting their operations, customer trust, and billions in annual revenue losses. In this type of fraud, businesses require advanced technology, and the traditional approach has been time-consuming, often causing delays and complicating the experience for genuine customers.

Integrating Artificial intelligence can be a great option to address these challenges, as it offers targeted solutions that can significantly reduce Ecommerce return fraud. AI can easily detect such suspicious activities on time by understanding patterns and monitoring real-time data and product details. This advanced technology will help eCommerce businesses protect themselves from return fraud and maintain a smooth and efficient return experience for legitimate customers.

AI has become a cornerstone in every industry in fighting against fraud activities, where Ecommerce sectors are not exceptional. This article will help eCommerce businesses keep back the legacy system and integrate Artificial intelligence. As an AI development company, we help businesses prepare a road map with AI solutions to improve eCommerce return fraud. Therefore, let’s understand how AI will change your eCommerce business.

How AI will change eCommerce Return Fraud

1. Frequent High-Value Item Returns
Fraudsters often focus on high-value items, which they repeatedly buy in bunches and return to exploit retailers’ policies. This tactic results in revenue loss as well as losses in logistical costs and restocking.

With advanced AI and ML algorithms, it can analyze patterns based on past return data. AI will analyze accounts that frequently return high-value items or make multiple purchases without a corresponding number of final sales. Therefore, retailers can leverage AI and ML services from Bacancy to stop fraud activities and later add verification steps or limit risky account activity accordingly.

2. Switching Items During Returns (Return Item Switching)
Some fraudsters engage in item switching, where they return a similar product that is a cheaper, damaged, or counterfeit version of the original. This is a challenging issue, especially with products that require careful inspection, such as electronics, luxury goods, or branded clothing.

Using AI-powered computer vision, the model can verify the original returned items by comparing images of the item at the time of sale with pictures at the time of return. This AI model can even detect color, model details, or wore-and-tear cloths, allowing for precise product verification.

If a returned item doesn’t match the original purchase, the AI system will provide an alert option; based on this information, retailers can take action, whether to raise refunds or substitute items.

3. Account Takeover and Refund Exploits
Fraudsters often take over customer accounts and exploit their return policies, seeking refunds or replacements without returning the item. This fraud action impacts not only retailers but also the customers with the exact account.

With AI, such exploits can be monitored in real time by looking for sudden and unusual changes, such as frequent address modifications, multiple returns initiated in a short period, or a spike in high-ticket purchases. During such activities, AI will alert these anomalies and make security checks to confirm whether the accounts are genuine.

This early detection will help retailers prevent suspicious activity and alert customers to protect their accounts.

4. Policy Exploitation by Serial Returners
Some consumers purchase products to use them for a short time before returning them, taking advantage of return policies. This conduct disturbs inventory management and costs businesses a lot of money, especially on a wide scale.

Predictive analytics and machine learning can classify customers’ purchase and return behavior data. Here, AI identifies serial returners who frequently return items after short periods, creating customer profiles that help assess return risks. To stop such suspicious activities, hire AI developer from us to make your ecommerce business successful and prevent fraud activities.

5. Use of Suspicious Shipping Addresses
Fraudsters may use multiple accounts or frequently change shipping addresses to confuse tracking systems and obscure fraudulent patterns. They may also re-route returns to a third party, complicating recovery efforts and increasing the risk of loss.

With AI algorithms, customers’ addresses will be cross-verified, and shipping addresses will be monitored. AI will send an alert for multiple accounts if there is any change. AI can also track changes in location patterns over time, identifying suspicious behavior. However, if one address suddenly appears across multiple high-ticket return orders, the system can pause or escalate the transaction for additional verification.

6. Manipulation of Return Policies through False Claims
Some customers exploit return policies by claiming they received the wrong item, defective goods, or damaged products without experiencing these issues. This leads to unnecessary refunds, loss of inventory, and a compromised return policy that affects genuine customers.

Here, NLP and text analytics models are essential in reviewing and analyzing customer communication language and patterns to detect false claims associated with policy abuse. AI will identify accounts that frequently report similar issues, such as the wrong item or defective product received, and send flag messages to retailers that can apply further scrutiny or restrict certain accounts, reducing abuse and loss.

7. Refund Fraud via Payment Manipulation
Some fraudsters identify loopholes in payment processing, manipulate payments, and repeatedly ask for refunds for the same product. Sometimes, they even claim the initial refund failed and ask ecommerce businesses to issue repeated refunds.

Through AI in eCommerce, such payment activities and refund requests can be monitored in real-time. AI can link transactions to specific items and timestamps and ensure refunds are issued only once. Whenever such type of refund requests for the same purchase are detected, the AI system flags them, preventing duplicate refunds and alerting the retailer’s fraud team for further verification.

8. Addressing Package Theft Claims
In such cases, fraudsters involve customers falsely claiming that a delivered package was stolen, even if it was received. This type of fraud claim can result in high costs for both lost merchandise and compensation.
An AI-powered analytics system will track shipment and customer data to evaluate whether the thief’s claims are true or false. The AI is so clever that it will examine customer history, neighborhood theft patterns, and delivery records to identify accounts or locations with unusually high package theft claims.
Later, retailers can assess claims with additional scrutiny or require proof of non-receipt, reducing losses from false theft claims.

Takeaway