Drive incremental revenue and customer loyalty by recognizing and stopping eGift card fraud with next-generation fraud detection. Kount’s AI-driven fraud prevention detects anomalies, performs link analysis, and automatically evaluates and weighs features to quickly identify and mitigate potential fraud.
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through stolen credit cards or account takeovers
to an unsuspecting customer
with the bad eGift cards and is shipped to the customer’s address
for the original eGift card purchase and the item that was sold. Merchant suffers associated penalties, loss of merchandise, brand reputation damage
eGift cards have the highest fraud attempt rates because purchases made with eGift cards are difficult to track. Most organizations lack systems to prevent fraud as fast as the cards are stolen. eGift card fraud can impact your business and result in chargebacks, operational costs, and dollar losses from stolen goods and services, as well as impact to brand reputation.
Learn more about eGift card fraud and how Kount can prevent it.
eGift cards can be fraudulently obtained in many ways. Credit cards can be stolen, accounts can be compromised, and legitimate customers can get pulled into unsuspecting fraud schemes.
Stolen payment data is used to purchase a gift card and email it to a legitimate email address.
A discounted, high-demand item is advertised in a marketplace. A consumer buys the third-party item and it is fulfilled using stolen credit cards.
Stolen account credentials are used to take over a customer account. Stored payment methods are used to purchase eGift cards.
Stolen eGift cards are sold on marketplaces at a discount. When the buyer tries to redeem the gift card, it may not work.
Kount plays an important role in supporting our multi-layered approach to fraud prevention.
Tyler Roye, CEO, eGifter
A multi-faceted solution that evaluates multiple points of potential fraud must go hand-in-hand with an eGift card initiative. Kount focuses on identifying good customers so that you can easily approve and process purchases in real time.
Identifies high risk devices, or high-volume activity from a small number of devices
Indicate the relationship between email reputation, age, and history, as well as physical identity attributes
Combines supervised and unsupervised machine learning to generate a score that indicates risk or unusual activity
Can be built around desired business outcomes, such as special event promotions and tracking shipping details
Learn more about eGift card fraud and how Kount can prevent it.